FN ISI Export Format VR 1.0 PT Journal AU Horn, W Popow, C Unterasinger, L TI Support for fast comprehension of ICU data: Visualization using metaphor graphics SO METHODS OF INFORMATION IN MEDICINE LA English DT Article NR 13 SN 0026-1270 PU F K SCHATTAUER VERLAG GMBH C1 Univ Vienna, Dept Med Cybernet & Artificial Intelligence, Freyung 6, A-1010 Vienna, Austria Univ Vienna, Dept Med Cybernet & Artificial Intelligence, A-1010 Vienna, Austria Austrian Res Inst Artificial Intelligence, Vienna, Austria Univ Vienna, Dept Pediat, Div Neonatol, NICU, Vienna, Austria DE computer graphics; data display; intensive care ID RESPIRATORY DATA; ABSTRACTION AB Objectives: The time-oriented analysis of electronic patient records on (neonatal) intensive cafe units is a tedious and time-consuming task. Graphic data visualization should make it easier for physicians to assess the overall situation of a patient and to recognize essential changes over time. Methods: Metaphor graphics are used to sketch the most relevant parameters for characterizing a patient's situation. By repetition of the graphic object in 24 frames the situation of the ICU patient is presented in one display, usually summarizing the lost Results. VIE-VISU is a data visualization system which uses multiples to present the change in the patient's status over time in graphic form, Each multiple is a highly structured metaphor graphic object. Each object visualizes important ICU parameters from circulation, ventilation, and fluid balance. Conclusion: The design using multiples promotes a focus on stability and change. A stable patient is recognizable at first sight, continuous improvement or worsening condition are easy to analyze, drastic changes in the patient's situation get the viewers attention immediately. CR COLE WG, 1993, INT J CLIN MONIT COM, V10, P91 COLE WG, 1994, METHOD INFORM MED, V33, P390 COMBI C, 1999, ARTIF INTELL, P301 COMBI C, 1999, ARTIF INTELL MED, V17, P271 GREEN CA, 1996, ERGONOMICS, V39, P412 HORN W, 1997, COMPUT BIOL MED, V27, P389 HORN W, 1998, ECAI 98 WORKSH INT D, P76 SHAHAR Y, 1998, 5 INT WORKSH TEMP RE, P11 SHAHAR Y, 1997, ARTIF INTELL, V90, P79 TUFTE ER, 1990, ENVISIONING INFORMAT TUFTE ER, 1983, VISUAL DISPLAY QUANT TUFTE ER, 1997, VISUAL EXPLANATIONS WAINER H, 1997, VISUAL REVELATIONS G TC 0 BP 421 EP 424 PG 4 JI Methods Inf. Med. PY 2001 VL 40 IS 5 GA 501EJ PI STUTTGART RP Horn W Univ Vienna, Dept Med Cybernet & Artificial Intelligence, Freyung 6, A-1010 Vienna, Austria J9 METHODS INFORM MED PA P O BOX 10 45 43, LENZHALDE 3, D-70040 STUTTGART, GERMANY UT ISI:000172654200010 ER PT Journal AU Sun, WW Wang, WC Wu, EH TI Fast combinative volume rendering by indexed data structure SO PROGRESS IN NATURAL SCIENCE LA English DT Article NR 9 SN 1002-0071 PU TAYLOR & FRANCIS LTD C1 Chinese Acad Sci, Inst Software, Comp Sci Lab, Beijing 100080, Peoples R China Chinese Acad Sci, Inst Software, Comp Sci Lab, Beijing 100080, Peoples R China Univ Macao, Fac Sci & Technol, Macao, Macao DE data structure; combinative volume rendering; data visualization AB It is beneficial to study the interesting contents in a data set by combining and rendering various contents of the data. In this regard, an indexed data structure is proposed to facilitate the reorganization of data so that the contents of the data can he combined conveniently and only the selected contents in the data are processed for rendering. Based on the structure, the cells of different contents can be queued up easily so that the volume rendering can be conducted more accurately and quickly. Experimental results chow that the indexed data structure is very efficient in improving combinative volume rendering. CR COHEN D, 1994, VISUAL COMPUT, V11, P27 IHM I, 1997, COMPUT GRAPH, V21, P497 LACROUTE P, 1994, P SIGGRAPH 94, P451 MONTANI C, 1990, COMPUT GRAPH, V24, P87 PARKER S, 1999, IEEE T VIS COMPUT GR, V5, P238 SILVER D, 1998, P IEEE VIS 98 NEW JE, P79 WALSUM TV, 1994, COMPUT GRAPH FORUM, V13, P339 WANG X, 1999, MAT SCI ENG C-BIO S, V10, P3 WILHELMS J, 1992, ACM T GRAPHIC, V11, P201 TC 0 BP 918 EP 923 PG 6 JI Prog. Nat. Sci. PY 2001 PD DEC VL 11 IS 12 GA 498EM PI LONDON RP Sun WW Chinese Acad Sci, Inst Software, Comp Sci Lab, Beijing 100080, Peoples R China J9 PROG NAT SCI PA 11 NEW FETTER LANE, LONDON EC4P 4EE, ENGLAND UT ISI:000172496900005 ER PT Journal AU Kitsiou, D De Madron, XD Arnau, PA TI Development of a data visualization and analysis tool to study the particle dynamics in the coastal zone SO MARINE POLLUTION BULLETIN LA English DT Article NR 5 SN 0025-326X PU PERGAMON-ELSEVIER SCIENCE LTD C1 Univ Aegean, Dept Marine Sci, 5 Sapfous Str, Mitilini 81100, Lesbos, Greece Univ Perpignan, CNRS, CEFREM, F-66860 Perpignan, France DE visualization; IDL (Interactive Data Language); currents; particle dynamics; data analysis; Gulf of Lions AB Developing a field experiment to study the different aspects of the marine system involves both the co-operation of research groups from distinct disciplines and the use of various meteorological and oceanographical sensors deployed simultaneously at different locations. The information obtained, stored in voluminous data sets and frequently in various formats, needs to be visualized and analysed in different ways to provide knowledge of the dynamics of the system examined. There is therefore need for tools to be able to: (i) access and retrieve data sets stored in various file formats, and (ii) to allow their visualization and analysis. In this work, a user-friendly visualization and analytical tool was developed for this purpose using the IDL (Interactive Data Language v.5.3). It allows the description, interpretation and analysis of the temporal and spatial variability of both scalar and vector variables. A description of the software is given and an application using data from the High Frequency Flux (HFF) experiment, part of the European MTP II-Mater research project, in the Gulf of Lions is presented. (C) 2001 Elsevier Science Ltd. All rights reserved. CR BROWN D, 1997, INTRO OBJECT ORIENTE EDSALL RM, 2000, COMPUT GEOSCI, V26, P109 EMERY WJ, 1998, DATA ANAL METHODS PH, P634 HEUSSNER S, 1998, MEDITERRANEAN TARGET, P61 LIU KK, 2000, EOS T AM GEOPHYS UN, V81, P641 TC 0 BP 262 EP 269 PG 8 JI Mar. Pollut. Bull. PY 2001 PD JUL-DEC VL 43 IS 7-12 GA 495UQ PI OXFORD RP Kitsiou D Univ Aegean, Dept Marine Sci, 5 Sapfous Str, Mitilini 81100, Lesbos, Greece J9 MAR POLLUT BULL PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000172359100013 ER PT Journal AU Shadewald, JK Hallmark, S Souleyrette, RR TI Visualizing system-wide economic impacts of transportation projects SO JOURNAL OF URBAN PLANNING AND DEVELOPMENT-ASCE LA English DT Article NR 15 SN 0733-9488 PU ASCE-AMER SOC CIVIL ENGINEERS C1 HNTB Corp, 715 Kirk Dr, Kansas City, MO 64106 USA HNTB Corp, Kansas City, MO 64106 USA Iowa State Univ, Ames, IA 50010 USA AB The economic evaluation of proposed transportation projects has traditionally been a technical process based on collected data and equations. Future needs must be considered to adequately meet the demands of system users. To ensure project success as political pressure forces transportation projects to be both beneficial and nonintrusive, transportation professionals must begin incorporating the public into every stage of a proposed project, including the economic analysis. This paper describes a streamlined process that brings together existing technologies to produce future needs estimates, perform the economic evaluation of the proposed solution, and display the costs and benefits. This process is performed in a geographic information system environment that enables the efficient storage and visualization of data, thereby increasing the efficiency of the economic evaluation as well as providing a venue to display results. CR 2000, BEN EFF ASS MOD PROG 2000, SMART CONV PROGR *CAL CORP, 2000, TRANSCAD OV *CTR TRANSP RES ED, 2000, ARCV TRANSPL INT DOC *FHA, 1999, SURF TRANSP EFF AN M *URB AN GROUP INC, 2000, VIP INF DECORLASOUZA P, 2000, ITE J, V70, P38 DECORLASOUZA P, 1999, TRANSPORT RES REC, V1685, P65 DECORLASOUZA P, 2000, USE STEAM EVALUATING EBERTS R, 2000, TRANSP RES BOARD C T FULTON LM, 2000, J TRANSPORTATION STA, V3, P1 MCHALE G, 2000, PUBLIC ROADS, V63, P11 POZDENA RJ, 2000, P TRANSP RES BOARD C, V21, P114 SHADEWALD JK, 2000, P MID CONT TRANSP S, P169 WEISBROD G, 1997, 477 TRANSP RES TC 0 BP 158 EP 168 PG 11 JI J. Urban Plan. Dev.-ASCE PY 2001 PD DEC VL 127 IS 4 GA 493QA PI RESTON RP Shadewald JK HNTB Corp, 715 Kirk Dr, Kansas City, MO 64106 USA J9 J URBAN PLAN DEV-ASCE PA 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA UT ISI:000172233200003 ER PT Journal AU Heskes, T TI Self-organizing maps, vector quantization, and mixture modeling SO IEEE TRANSACTIONS ON NEURAL NETWORKS LA English DT Article NR 27 SN 1045-9227 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 Univ Nijmegen, RWCP Theoret Fdn SNN, NL-6252 EZ Nijmegen, Netherlands Univ Nijmegen, RWCP Theoret Fdn SNN, NL-6252 EZ Nijmegen, Netherlands DE expectation-maximization (EM) algorithms; market basket analysis; missing values; mixture modeling; self-organizing maps; vector quantization AB Self-organizing maps are popular algorithms for unsupervised learning and data visualization. Exploiting the link between vector quantization and mixture modeling, we derive expectation-maximization (EM) algorithms for self-organizing maps with and without missing values. We compare self- organizing maps with the elastic-net approach and explain why the former is better suited for the visualization of high- dimensional data. Several extensions and improvements are discussed. As an illustration we apply a self-organizing map based on a multinomial distribution to market basket analysis. CR BERRY M, 1997, DATA MINING TECHNIQU BISHOP CM, 1997, ADV NEUR IN, V9, P354 BRIN S, 1997, P ACM SIGMOD INT C M, P265 BUHMANN J, 1993, IEEE T INFORM THEORY, V39, P1133 CHENG YZ, 1997, NEURAL COMPUT, V9, P1667 DURBIN R, 1987, NATURE, V326, P689 GHAHRAMANI Z, 1994, ADV NEURAL INFORMATI, V6, P120 GRAEPEL T, 1998, NEUROCOMPUTING, V21, P173 HANSEN J, 2000, P 15 INT C PATT REC, V2, P207 HESKES T, 1999, KOHONEN MAPS, P303 HESKES TM, 1993, P IEEE ICNN, V3, P1219 HOFMANN T, 1999, ADV NEUR IN, V11, P466 KOHONEN T, 1982, BIOL CYBERN, V43, P59 KOHONEN T, 2000, IEEE T NEURAL NETWOR, V11, P574 KOHONEN T, 1998, NEUROCOMPUTING, V21, P1 LUTTRELL SP, 1994, NEURAL COMPUT, V6, P767 LUTTRELL SP, 1989, P 3 IEEE INT JOINT C, V2, P495 NEAL RM, 1998, LEARNING GRAPHICAL M, P355 PERIERA F, 1993, P ASS COMP LING, P183 PETERS B, 1987, SIAM J APPL MATH, V35, P362 RITTER H, 1989, BIOL CYBERN, V61, P241 ROSE K, 1992, IEEE T INFORM THEORY, V38, P1249 ROSE K, 1990, PHYS REV LETT, V65, P945 ULTSCH A, 1999, KOHONEN MAPS, P33 UTSUGI A, 1997, NEURAL COMPUT, V9, P623 YAIR E, 1992, IEEE T SIGNAL PROCES, V40, P294 YUILLE AL, 1994, NEURAL COMPUT, V6, P334 TC 0 BP 1299 EP 1305 PG 7 JI IEEE Trans. Neural Netw. PY 2001 PD NOV VL 12 IS 6 GA 489WJ PI NEW YORK RP Heskes T Univ Nijmegen, RWCP Theoret Fdn SNN, NL-6252 EZ Nijmegen, Netherlands J9 IEEE TRANS NEURAL NETWORKS PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000172015200004 ER PT Journal AU Girolami, M TI The topographic organization and visualization of binary data using multivariate-bernoulli latent variable models SO IEEE TRANSACTIONS ON NEURAL NETWORKS LA English DT Article NR 23 SN 1045-9227 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 Univ Paisley, Div Comp & Informat Syst, Appl Computat Intelligence Res Unit, Paisley PA1 2BE, Renfrew, Scotland Univ Paisley, Div Comp & Informat Syst, Appl Computat Intelligence Res Unit, Paisley PA1 2BE, Renfrew, Scotland DE data clustering; data mining; data visualization; generative modeling; probabilistic modeling; self-organization; text document processing; unsupervised learning ID MAPS AB A nonlinear latent variable model for the topographic organization and subsequent visualization of multivariate binary data is presented. The generative topographic mapping (GTM) is a nonlinear factor analysis model for continuous data which assumes an isotropic Gaussian noise model and performs uniform sampling from a two-dimensional (2-D) latent space. Despite the success of the GTM when applied to continuous data the development of a similar model for discrete binary data has been hindered due, in part, to the nonlinear link function inherent in the binomial distribution which yields a log- likelihood that is nonlinear in the model parameters. This paper presents an effective method for the parameter estimation of a binary latent variable model-a binary version of the GTM- by adopting a variational approximation to the binomial likelihood. This approximation thus provides a log-likelihood which is quadratic in the model parameters and so obviates the necessity of an iterative M-step in the expectation maximization (EM) algorithm. The power of this method is demonstrated on two significant application domains, handwritten digit recognition and the topographic organization of semantically similar text-based documents. CR AGRESTI A, 1990, CATEGORICAL DATA ANA BISHOP CM, 1998, IEEE T PATTERN ANAL, V20, P281 BISHOP CM, 1998, NEURAL COMPUT, V10 BISHOP CM, 1995, NEURAL NETWORKS PATT BISHOP CM, 1998, NEUROCOMPUTING, V21, P203 CRISTIANINI N, 2000, INTRO SUPPORT VECTOR DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391 GIROLAMI M, 1998, IEEE T NEURAL NETWOR, V9, P1495 HINTON GE, 1992, ADV NEURAL INFORMATI, V4, P512 HOFMANN T, 1999, P 15 C UNC AI, P289 HOFMANN T, 1999, P 3 S INT DAT AN ISBELL CL, 1999, ADV NEUR IN, V11, P480 JAAKKOLA TS, 1997, P 1997 C ART INT STA, P283 KASKI S, 1998, NEUROCOMPUTING, V21, P101 KOHONEN T, 1995, SELF ORG MAPS MCCALLUM A, AAAI 98 WORKSH LEARN MCCULLAGH P, 1985, GENERALIZED LINEAR M MCLACHLAN G, 2000, FINITE MIXTURE MODEL OJA E, 1999, KOHONEN MAPS RITTER H, 1989, BIOL CYBERN, V61, P241 SAHAMI M, 1998, THESIS STANFORD U TIPPING ME, 1999, ADV NEURAL INFORM PR, P592 TIPPING ME, 1999, J ROY STAT SOC B MET, V46, P257 TC 0 BP 1367 EP 1374 PG 8 JI IEEE Trans. Neural Netw. PY 2001 PD NOV VL 12 IS 6 GA 489WJ PI NEW YORK RP Girolami M Univ Paisley, Div Comp & Informat Syst, Appl Computat Intelligence Res Unit, Paisley PA1 2BE, Renfrew, Scotland J9 IEEE TRANS NEURAL NETWORKS PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000172015200011 ER PT Journal AU Roederer, M TI Spectral compensation for flow cytometry: Visualization artifacts, limitations, and caveats SO CYTOMETRY LA English DT Article NR 13 SN 0196-4763 PU WILEY-LISS C1 NIH, Vaccine Res Ctr, 40 Convent Dr,Room 5509, Bethesda, MD 20892 USA NIH, Vaccine Res Ctr, Bethesda, MD 20892 USA DE flow cytometry; data analysis; compensation; multicolor immunophenotyping ID SENSITIVITY; COLOR AB Background: In multicolor flow cytometric analysis, compensation for spectral overlap is nearly always necessary. For the most part, such compensation has been relatively simple, producing the desired rectilinear distributions. However, in the realm of multicolor analysis, visualization of compensated often results in unexpected distributions, principally the appearance of a large number of events on the axis, and even more disconcerting, an inability to bring the extent of compensated data down to "autofluorescence" levels. Materials and Methods: A mathematical model of detector measurements with variable photon intensities, spillover parameters, measurement errors, and data storage characteristics was used to illustrate sources of apparent error in compensated data. Immunofluorescently stained cells were collected under conditions of limiting light collection and high spillover between detectors to confirm aspects of the model. Results: Photon-counting statistics contribute a nonlinear error to compensated parameters. Measurement errors and log-scale binning error contribute linear errors to compensated parameters. These errors are most apparent with the use of red or far-red fluorochromes (where the emitted light is at low intensity) and with large spillover between detectors. Such errors can lead to data visualization artifacts that can easily lead to incorrect conclusions about data, and account for the apparent "undercompensation" previously described for multicolor staining. Conclusions: There are inescapable errors arising from imperfect measurements, photon-counting statistics, and even data storage methods that contribute both linearly and nonlinearly to a "spreading" of a properly compensated autofluorescence distribution. This phenomenon precludes the use of "quadrant" statistics or gates to analyze affected data; it also precludes visual adjustment of compensation. Most importantly, it is impossible to properly compensate data Using standard visual graphical interfaces (histograms or dot plots). Computer-assisted compensation is required, as well as careful gating and experimental design to determine the distinction between positive and negative events. Finally, the use of special staining controls that employ all reagents except for the one of interest (termed fluorescence minus one, or "FMO" controls) becomes necessary to accurately identify expressing cells in the fully stained sample, Cytometry 45: 194 - 205, 2001. (C) 2001 Wiley-Liss, Inc. CR BAGWELL CB, 1993, ANN NY ACAD SCI, V677, P167 BAUMGARTH N, 2000, J IMMUNOL METHODS, V243, P77 BIGOS A, 1999, CYTOMETRY, V36, P36 DEROSA SC, 2000, NAT MED, V7, P245 KANTOR A, 1997, HDB EXPT IMMUNOLOGY ROEDERER M, CONJUGATION MONOCLON ROEDERER M, 1999, CURRENT PROTOCOLS CY ROEDERER M, 1997, CYTOMETRY, V29, P328 ROEDERER M, 1986, CYTOMETRY, V7, P558 SHAPIRO HM, 1998, CYTOMETRY, V33, P280 STEWART CC, 1999, CYTOMETRY, V38, P161 WOOD JCS, 1998, CYTOMETRY, V33, P256 WOOD JCS, 1998, CYTOMETRY, V33, P260 TC 3 BP 194 EP 205 PG 12 JI Cytometry PY 2001 PD NOV 1 VL 45 IS 3 GA 489ZF PI NEW YORK RP Roederer M NIH, Vaccine Res Ctr, 40 Convent Dr,Room 5509, Bethesda, MD 20892 USA J9 CYTOMETRY PA DIV JOHN WILEY & SONS INC, 605 THIRD AVE, NEW YORK, NY 10158- 0012 USA UT ISI:000172022600005 ER PT Journal AU Jones, JK TI The role of data mining technology in the identification of signals of possible adverse drug reactions: Value and limitations SO CURRENT THERAPEUTIC RESEARCH-CLINICAL AND EXPERIMENTAL LA English DT Article NR 25 SN 0011-393X PU EXCERPTA MEDICA INC C1 Degge Grp Ltd, 1616 N Ft Myer Dr,Suite 1430, Arlington, VA 22209 USA Degge Grp Ltd, Arlington, VA 22209 USA DE data mining; adverse events; pediatrics ID SPONTANEOUS REPORTING SYSTEM; LARGE FREQUENCY TABLES; KNOWLEDGE DISCOVERY; INFORMATION AB Background. The emergence of large databases of adverse event (AE) data and the need to identify signals of new, unknown adverse effects of newly marketed drugs by regulators and pharmaceutical sponsors have coincided with the development of several methods of "data mining" for identifying new associations within all types of databases. Objective: This paper provides a broad overview of the data mining methods being used in many fields to consider applications for identifying new AEs in spontaneous AE databases and other medical data sources (eg, clinical trials and claims data). Methods: Literature was obtained through a MEDLINE search of the medical literature and a broader search of the medical informatics literature on the Internet. Results: Data mining methods have emerged to define associations in many types of databases. Specific methods include artificial neural networks, Bayesian probability approaches, genetic algorithms, decision trees, nearest neighbor methods, rule induction, and new data visualization techniques. Application of selected methods is now under way at the US Food and Drug Administration and the World Health Organization Centre for Drug Monitoring, as well as in some commercial organizations. Whether such methods enhance the usual AE signal identification process remains controversial. The application of data mining to coherent population-based clinical trial and epidemiological clinical data sets will likely enhance the AE field. Conclusion: Data mining methods show promise for the identification of new, unknown signals of AEs, especially in defined populations. CR ANAND SS, 1998, P 2 PAC AS C KNOWL D BROSSETTE SE, 1998, J AM MED INFORM ASSN, V5, P373 BROSSETTE SE, 2000, METHOD INFORM MED, V39, P303 DEEKS SG, 2000, CLIN INFECT DIS S2, V30, PS177 DELGADO M, 2001, ARTIF INTELL MED, V21, P241 DOWNS SM, 2000, P AM MED INF ASS S C DUMOUCHEL W, 1999, AM STAT, V53, P201 DZEROSKI S, 2000, ST HEAL T, V77, P779 GRAHAM D, 2000, PHARMACOEPIDEM DR S, P109 HELMA C, 2000, STAT METHODS MED RES, V9, P329 HOLSHEIMER M, 1991, CSR9406 CENTR WISK I KUSIAK A, 2000, IEEE T INF TECHNOL B, V4, P274 LEE IN, 2000, MED INFORM INTERNET, V25, P81 LINDQUIST M, 2000, DRUG SAFETY, V23, P533 LOUIS TA, 1999, AM STAT, V53, P196 MADIGAN D, 1999, AM STAT, V53, P198 MCDONALD JM, 1998, ARCH PATHOL LAB MED, V122, P409 MOSER SA, 1999, EMERG INFECT DIS, V5, P454 ONEILL RT, 1999, AM STAT, V53, P190 ONEILL RT, 2001, CURR THER RES CLIN E, V62, P650 ORRE R, 2000, COMPUT STAT DATA AN, V34, P473 PATTERSON D, 1998, P AM ASS ART INT AAA PRATHER JC, 1997, P AM MED INF ASS ANN SMYTH P, 2000, STAT METHODS MED RES, V9, P309 SZARFMAN A, 1997, COMPREHENSIVE TOXICO, V4, P363 TC 0 BP 664 EP 672 PG 9 JI Curr. Ther. Res.-Clin. Exp. PY 2001 PD SEP VL 62 IS 9 GA 486DZ PI NEW YORK RP Jones JK Degge Grp Ltd, 1616 N Ft Myer Dr,Suite 1430, Arlington, VA 22209 USA J9 CURR THER RES PA 650 AVENUE OF THE AMERICAS, NEW YORK, NY 10011 USA UT ISI:000171804300006 ER PT Journal AU Chen, H Chen, YQ Finkelstein, A Funkhouser, T Li, K Liu, ZY Samanta, R Wallace, G TI Data distribution strategies for high-resolution displays SO COMPUTERS & GRAPHICS-UK LA English DT Article NR 14 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Princeton Univ, Dept Comp Sci, 35 Olden St, Princeton, NJ 08540 USA Princeton Univ, Dept Comp Sci, Princeton, NJ 08540 USA DE parallel rendering; networked graphics; large-scale displays; interactive visualization; cluster computing ID SYSTEM AB Large-scale and high-resolution displays are increasingly being used for next-generation interactive 3D graphics applications, including large-scale data visualization, immersive virtual environments, and collaborative design. These systems must include a, very high-performance and scalable 3D rendering subsystem in order to generate high-resolution images at real- time frame rates. We are investigating how to build such a system using only inexpensive commodity components in a PC cluster. The main challenge is to develop scalable algorithms to partition and distribute rendering tasks effectively under the bandwidth, processing, and storage constraints of a. distributed system. In this paper, we compare three different approaches that differ in the type of data transmitted from client to display servers: control, primitives, or pixels. For each approach, we describe our initial experiments with a working prototype system driving a multi-projector display wall with a PC cluster. We find that different approaches are suitable for different system architectures, with the best choice depending on the communication bandwidth, storage capacity, and processing power of the clients and display servers. (C) 2001 Elsevier Science Ltd. All rights reserved. CR BALA A, 1998, P 1998 ICPP WORKSH A, P29 BILAS A, 1997, P 11 INT PAR PROC S BODEN NJ, 1995, IEEE MICRO, V15, P29 BUCK I, 2000, EUROGRAPHICS SIGGRAP, P87 CHEN YQ, 2001, 1 IEEE ACM INT S CLU DEERING M, 1995, P SIGGRAPH 95, P13 ECKART S, 1995, P DIG VID COMPR ALG, P100 KWONG MK, 1995, MCSP5060395 ANL LI K, 2000, IEEE COMPUT GRAPH, V20, P29 SAMANTA R, 2000, EUROGRAPHICS SIGGRAP, P99 SAMANTA R, 1999, P SIGGR EUR WORKSH G, P107 SAMANTA R, 2000, SIGGRAPH 2000 SCHEIFLER RW, 1986, ACM T GRAPHIC, V5, P79 WALLACH DS, 1994, P SIGGRAPH 94 JUL, P193 TC 0 BP 811 EP 818 PG 8 JI Comput. Graph.-UK PY 2001 PD OCT VL 25 IS 5 GA 484AU PI OXFORD RP Funkhouser T Princeton Univ, Dept Comp Sci, 35 Olden St, Princeton, NJ 08540 USA J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000171668700009 ER PT Journal AU Sarfraz, M Butt, S Hussain, MZ TI Visualization of shaped data by a rational cubic spline interpolation SO COMPUTERS & GRAPHICS-UK LA English DT Article NR 17 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 King Fahd Univ Petr & Minerals, Dept Informat & Comp Sci, KFUPM 1510, Dhahran 31261, Saudi Arabia King Fahd Univ Petr & Minerals, Dept Informat & Comp Sci, Dhahran 31261, Saudi Arabia Univ Engn, Lahore, Pakistan Punjab Univ, Lahore, Pakistan DE data visualization; rational spline; interpolation; positive; monotone ID QUADRATIC SPLINE; MONOTONE AB A smooth curve interpolation scheme for positive and monotonic data has been developed. This scheme uses piecewise rational cubic functions. The two families of parameters, in the description of the rational interpolant, have been constrained to preserve the shape of the data. The rational spline scheme has a unique representation. In addition to preserve the shape of positive and/or monotonic data sets, it also possesses extra features to modify the shape of the design curve as and when desired. The degree of smoothness attained is C-1. (C) 2001 Elsevier Science Ltd. All rights reserved. CR BRODLIE KW, 1991, COMPUT GRAPHICS, V15, P15 BRODLIE KW, 1985, FUNDAMENTAL ALGORITH, P303 BUTT S, 1993, COMPUT GRAPHICS, V17, P55 CONSTANTINI P, 1997, ACM T MATH SOFTWARE, V23, P229 DEVORE A, 1986, COMPUT AIDED GEOM D, V3, P205 FRITSCH FN, 1980, SIAM J NUMER ANAL, V17, P238 FRITSCH FN, 1984, SIAM J SCI STAT COMP, V5, P303 GREGORY JA, 1986, COMPUT AIDED DESIGN, V18, P53 GREINER H, 1991, MATH COMPUT MODEL, V15, P97 LAHTINEN A, 1996, ANN NUMERICAL MATH, V3, P151 MCALLISTER DF, 1981, ACM T MATH SOFTWARE, V7, P331 MORETON HP, 1995, P SIAM 94 C, P123 PASSOW E, 1977, SIAM J NUMER ANAL, V14, P904 SARFRAZ M, 1992, B KOREAN MATH SOC, V29, P193 SARFRAZ M, 1997, COMPUT GRAPH, V21, P5 SARFRAZ M, 1992, COMPUT GRAPHICS, V16, P427 SCHUMAKER LL, 1983, SIAM J NUMER ANAL, V20, P854 TC 0 BP 833 EP 845 PG 13 JI Comput. Graph.-UK PY 2001 PD OCT VL 25 IS 5 GA 484AU PI OXFORD RP Sarfraz M King Fahd Univ Petr & Minerals, Dept Informat & Comp Sci, KFUPM 1510, Dhahran 31261, Saudi Arabia J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000171668700011 ER PT Journal AU Shoemaker, CA Pungliya, M Pedro, MAS Ruiz, C Alvarez, SA Ward, M Ryder, EF Krushkal, J TI Computational methods for single-point and multipoint analysis of genetic variants associated with a simulated complex disorder in a general population SO GENETIC EPIDEMIOLOGY LA English DT Article NR 6 SN 0741-0395 PU WILEY-LISS C1 Worcester Polytech Inst, Dept Biol & Biotechnol, 100 Inst Rd, Worcester, MA 01609 USA Worcester Polytech Inst, Dept Biol & Biotechnol, Worcester, MA 01609 USA Worcester Polytech Inst, Dept Comp Sci, Worcester, MA 01609 USA Boston Coll, Dept Comp Sci, Chestnut Hill, MA 02167 USA DE allelic linkage disequilibrium; artificial neural networks; association rule mining; genotypic disequilibrium; multivariate data visualization AB Several techniques for association analysis have been applied to simulated genetic data for a general population. We describe and compare the performance of three single-point methods and two multipoint approaches rooted in machine learning and data mining. (C) 2001 Wiley-Liss, Inc. CR AGRAWAL R, 1993, P ACM SIGMOD C MAN D, P207 HAJEK P, 1966, COMPUTING, V1, P293 LIN W, 2000, WEBKDD 2000 WORKSH W LIU B, 1998, P 4 INT C KNOWL DISC, P80 RUMELHART DE, 1986, PARALLEL DISTRIBUTED, V1 WARD MO, 1994, P IEEE C VIS SAN JOS, P326 TC 0 BP S738 EP S745 PG 8 JI Genet. Epidemiol. PY 2001 VL 21 SU 1 GA 480LK PI NEW YORK RP Krushkal J Worcester Polytech Inst, Dept Biol & Biotechnol, 100 Inst Rd, Worcester, MA 01609 USA J9 GENET EPIDEMIOL PA DIV JOHN WILEY & SONS INC, 605 THIRD AVE, NEW YORK, NY 10158- 0012 USA UT ISI:000171462700136 ER PT Journal AU Nottingham, QJ Cook, DF Zobel, CW TI Visualization of multivariate data with radial plots using SAS SO COMPUTERS & INDUSTRIAL ENGINEERING LA English DT Article NR 11 SN 0360-8352 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Virginia Polytech Inst & State Univ, Dept Management Sci & Informat Technol, 1007 Pamplin Hall, Blacksburg, VA 24061 USA Virginia Polytech Inst & State Univ, Dept Management Sci & Informat Technol, Blacksburg, VA 24061 USA DE radial plot; visualization; SAS AB Data visualization tools can provide very powerful information and insight when performing data analysis. In many situations, a set of data can be adequately analyzed through data visualization methods alone. In other situations, data visualization can be used for preliminary data analysis. In this paper, radial plots are developed as a SAS-based data visualization tool that can improve one's ability to monitor, analyze and control a process. Using the program developed in this research, we present two examples of data analysis using radial plots; the first example is based on data from a particle board manufacturing process and the second example is a business process for monitoring the time-varying level of stock return data. (C) 2001 Elsevier Science Ltd. All rights reserved. CR *SAS I, 1998, SAS SYST WIND VERS 7 BLAZEK LW, 1987, J QUAL TECHNOL, V19, P69 CHAMBERS JM, 1983, GRAPHICAL METHODS DA COLET E, 1997, BEHEAV RES METHODS I, V27, P257 FRIENDLY M, 1991, SAS SYSTEM STAT GRAP GLOSTEN LR, 1993, J FINANC, V48, P1779 KLEINER B, 1981, J AM STAT ASSOC, V76, P260 LATHAM R, 1995, DICT COMP GRAPH PESARAN MH, 1995, J FINANCE L, P1201 QI M, 1999, J FORECASTING, V18, P151 TUFTE ER, 1983, VISUAL DISPLAY QUANT TC 0 BP 17 EP 35 PG 19 JI Comput. Ind. Eng. PY 2001 PD OCT VL 41 IS 1 GA 478LX PI OXFORD RP Nottingham QJ Virginia Polytech Inst & State Univ, Dept Management Sci & Informat Technol, 1007 Pamplin Hall, Blacksburg, VA 24061 USA J9 COMPUT IND ENG PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000171348600002 ER PT Journal AU Michailidis, G de Leeuw, J TI Data visualization through graph drawing SO COMPUTATIONAL STATISTICS LA English DT Article NR 27 SN 0943-4062 PU PHYSICA-VERLAG GMBH & CO C1 Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA DE multivariate categorical data; data visualization; graphs; majorization algorithms ID SURROGATE OBJECTIVE FUNCTIONS; SYSTEM AB In this paper the problem of visualizing categorical multivariate data sets is considered. By representing the data as the adjacency matrix of an appropriately defined bipartite graph, the problem is transformed to one of graph drawing. A general graph drawing framework is introduced, the corresponding mathematical problem defined and an algorithmic approach for solving the necessary optimization problem discussed. The new approach is illustrated through several examples. CR BENZECRI JP, 1992, CORRESPONDENCE ANAL BORG I, 1997, MODERN MULTIDIMENSIO BRANDES U, 1997, P 5 INT S GRAPH DRAW, V1353, P236 BUJA A, 1998, J COMPUTATIONAL GRAP COLEMAN MK, 1996, SOFTWARE PRACT EXPER, V26, P1415 DELEEUW J, 2000, IMS LECT NOTES MONOG, P219 DELEEUW J, 2001, IN PRESS INT ENCY SO DELEEUW J, 1994, INFORMATION SYSTEMS DELEEUW J, 2000, J COMPUT GRAPH STAT, V9, P26 DIBATTISTA G, 1998, GRAPH DRAWING ALGORI EADES P, 1984, C NUMERANTIUM, V42, P149 GIFI A, 1990, NONLINEAR MULTIVARIA HARTIGAN J, 1975, CLUSTERING ALGORITHM HEALY P, 1999, LECT NOTES COMPUT SC, V1731, P205 HEISER WJ, 1995, RECENT ADV DESCRIPTI HERMAN I, 2000, IEEE T VIS COMPUT GR, V6, P24 LANGE K, 2000, J COMPUT GRAPH STAT, V9, P1 MICHAILIDIS G, 2000, COMPUT STAT DATA AN, V32, P411 MICHAILIDIS G, 2000, HOMOGENEITY ANAL ALT MICHAILIDIS G, 2000, MULTIVARIATE DATA AN MICHAILIDIS G, 1998, STAT SCI, V13, P307 PREDIGER S, 1997, P 2 INT S KNOWL RETR PURCHASE HC, 1995, P 3 INT S GRAPH DRAW, V1027, P435 SUGIYAMA K, 1981, IEEE T SYST MAN CYB, V11, P109 TUTTE WT, 1963, P LOND MATH SOC, V13, P743 WEGMAN EJ, 1990, J AM STAT ASSOC, V85, P664 WILLS GJ, 1999, J COMPUT GRAPH STAT, V8, P190 TC 0 BP 435 EP 450 PG 16 JI Comput. Stat. PY 2001 VL 16 IS 3 GA 477UP PI HEIDELBERG RP Michailidis G Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA J9 COMPUTATION STAT PA TIERGARTENSTRASSE 17, 69121 HEIDELBERG, GERMANY UT ISI:000171302900009 ER PT Journal AU Benoit, G Andrews, JE TI Data discretization for novel resource discovery in large medical data sets SO JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION LA English DT Article NR 13 SN 1067-5027 PU HANLEY & BELFUS INC C1 Univ Kentucky, Sch Lib & Informat Sci, Coll Commun & Informat Studies, Lexington, KY USA Univ Kentucky, Sch Lib & Informat Sci, Coll Commun & Informat Studies, Lexington, KY USA ID ASSOCIATION AB This paper is motivated by the problems of dealing with large data sets in information retrieval. The authors suggest an information retrieval framework based on mathematical principles to organize and permit end-user manipulation of a retrieval set. By adjusting through the interface the weights and types of relationships between query and set members, it is possible to expose unanticipated, novel relationships between the query/document pair. The retrieval set as a whole is parsed into discrete concept-oriented subsets (based on within-set similarity measures) and displayed on screen as interactive "graphic nodes" in an information space, distributed at first based on the vector model (similarity measure of set to query). The result is a visualized map wherein it is possible to identify main concept regions and multiple subregions as dimensions of the same data. Users may examine the membership within sub-regions. Based on this framework, a data visualization user interface was designed to encourage users to work with the data on multiple levels to find novel relationships between the query and retrieval set members. Space constraints prohibit addressing all aspects of this project. CR BAEZAYATES R, 1999, MODERN INFORMATION R BENOIT G, 2000, UNPUB INFORMATION PR HOLMES JH, 2000, ARTIF INTELL MED, V19, P53 LINDBERG D, 1993, UNIFIED MED LANGUAGE, P41 MILLER MH, 1997, P AMIA ANN FALL S, P533 MINEAU G, 2000, CONCEPTUAL MODELING, V33, P137 PEARL J, 1993, ARTIF INTELL, P327 PENDHARKAR PC, 1999, EXPERT SYST APPL, V17, P223 PURCELL GP, 1995, P ANN S COMP APPL ME, P851 SHEN L, 1999, INFORM SCIENCES, V118, P251 STAPLEY B, 2000, PAC S BIOC, V5, P526 WIESMAN F, 1997, COMPUT METH PROG BIO, V53, P135 WILDEMUTH BM, 1995, B MED LIBR ASSOC, V83, P294 TC 0 BP 61 EP 65 PG 5 JI J. Am. Med. Inf. Assoc. PY 2000 SU S GA 458RC PI PHILADELPHIA RP Benoit G Univ Kentucky, Sch Lib & Informat Sci, Coll Commun & Informat Studies, Lexington, KY USA J9 J AMER MED INFORM ASSOC PA 210 S 13TH ST, PHILADELPHIA, PA 19107 USA UT ISI:000170207500014 ER PT Journal AU Banas, A Kwiatek, WM Zajac, W TI Trace element analysis of tissue section by means of synchrotron radiation: the use of GNUPLOT for SRIXE spectra analysis SO JOURNAL OF ALLOYS AND COMPOUNDS LA English DT Article NR 6 SN 0925-8388 PU ELSEVIER SCIENCE SA C1 Jagiellonian Univ, Inst Phys, Reymonta 4, PL-30059 Krakow, Poland Jagiellonian Univ, Inst Phys, PL-30059 Krakow, Poland H Niewodniczanski Inst Nucl Phys, PL-31342 Krakow, Poland DE SRIXE; trace elements; GNUPLOT; prostate; kidney AB Synchrotron Radiation Induced X-ray Emission (SRIXE) is a powerful technique for Trace Element (TE) analysis in biomedical samples. Recently, SRIXE technique has been applied to TE determination in 20 mum thick kidney and prostate tissue sections, taken from cancerous and healthy organs. Results obtained for the latter two are compared. Matrices of biomedical samples are mostly composed of low-Z elements, and trace elements are at low levels. This is why SRIXE data analysis in such materials may be quite a challenge. In order to solve analytical problems, sophisticated fitting procedures have to be applied since a proper spectra analysis requires additional information about the sample. This is usually carried out by dedicated computer software. We are showing that satisfactory results can be obtained by employing GNUPLOT, a public domain script-driven engine for data visualization and curve fitting. The results of GNUPLOT are compared with ones obtained from QXAS. an open domain software (obtained from IAEA). (C) 2001 Elsevier Science B.V. All rights reserved. CR 1970, NUCL DATA TABLES A, V7, P688 BRANDENBURG T, 1992, THESIS HAMBURG U HALLIWELL B, 1984, BIOCHEM J, V219, P1 JONES KW, 1992, HDB XRAY SPECTROMETR, P411 JONES KW, 1987, P SOC PHOTO-OPT INS, V749, P37 KWIATEK WM, 1994, ACTA PHYS POL A, V86, P695 TC 0 BP 135 EP 138 PG 4 JI J. Alloy. Compd. PY 2001 PD OCT 4 VL 328 IS 1-2 GA 473RZ PI LAUSANNE RP Banas A Jagiellonian Univ, Inst Phys, Reymonta 4, PL-30059 Krakow, Poland J9 J ALLOYS COMPOUNDS PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND UT ISI:000171063000024 ER PT Journal AU Piette, MA Kinney, SK Haves, P TI Analysis of an information monitoring and diagnostic system to improve building operations SO ENERGY AND BUILDINGS LA English DT Article NR 12 SN 0378-7788 PU ELSEVIER SCIENCE SA C1 Univ Calif Berkeley, Lawrence Berkeley Lab, Berkeley, CA 94720 USA Univ Calif Berkeley, Lawrence Berkeley Lab, Berkeley, CA 94720 USA DE IMDS; building control system; building operation AB This paper discusses a demonstration of a technology to address the problem that buildings do not perform as well as anticipated during design. We partnered with an innovative building operator to evaluate a prototype information monitoring and diagnostic system (IMDS). The IMDS consists of a set of high-quality sensors, data acquisition software and hardware, and data visualization software including a web-based remote access system, that can be used to identify control problems and equipment faults. The information system allowed the operators to make more effective use of the building control system and freeing up time to take care of other tenant needs. They report observing significant improvements in building comfort, potentially improving tenant health and productivity. The reduction in the labor costs to operate the building is about US$ 20,000 per year, which alone could pay for the information system in about 5 years. A control system retrofit based on findings from the information system is expected to reduce energy use by 20% over the next year, worth over US$ 30,000 per year in energy cost savings. The operators are recommending that similar technology be adopted in other buildings. (C) 2001 Elsevier Science B.V. All rights reserved. CR *HAGL BAILL CONS I, 1998, BUILD COMM SURV ATT, P1 *PORTL EN CONS INC, 1998, NAT STRAT BUILD COMM BEHRENS D, 1996, COMMERCIAL CUSTOMER CLARIDGE DE, 1994, AM COUNCIL ENERGY EF, V5, P73 FISK WJ, 1997, INDOOR AIR, V7, P158 HAVES P, 2000, MODEL BASED PERFORMA, V3 HYVARINEN J, 1996, BUILDING OPTIMISATIO, V25 PIETTE MA, 1998, EARLY RESULTS FIELD PIETTE MA, 1994, P 2 NAT C BUILD COMM PIETTE MA, 1999, PERFORMANCE ASSESSME SEBALD AS, 1997, DIAGNOSTICS BUILDING SHOCKMAN C, 2000, IN PRESS AM COUNCIL, V8 TC 0 BP 783 EP 791 PG 9 JI Energy Build. PY 2001 PD OCT VL 33 IS 8 GA 471VB PI LAUSANNE RP Piette MA Univ Calif Berkeley, Lawrence Berkeley Lab, Berkeley, CA 94720 USA J9 ENERG BLDG PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND UT ISI:000170948400003 ER PT Journal AU Pilipenko, VA Watermann, J Popov, VA Papitashvili, VO TI Relationship between auroral electrojet and Pc5 ULF waves SO JOURNAL OF ATMOSPHERIC AND SOLAR-TERRESTRIAL PHYSICS LA English DT Article NR 32 SN 1364-6826 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Inst Phys Earth, B Gruzinskaya 10, Moscow 123995, Russia Inst Phys Earth, Moscow 123995, Russia Danish Meteorol Inst, DK-2100 Copenhagen, Denmark Inst Terr Magnetism & Radiowave Propagat, Troitsk 142092, Russia Univ Michigan, Ann Arbor, MI 48109 USA DE ULF waves; auroral electrojet; ionosphere-magnetosphere interaction ID POLARIZATION CHARACTERISTICS; GEOMAGNETIC-PULSATIONS; MORNING SECTOR; STARE RADAR; MICROPULSATIONS; STORM; INSTABILITY; PHASE AB Applying a new data visualization technique to magnetic field observations from the Greenland west coast array we observe that Pc5 wave power spatial/temporal variations in the morning/pre-noon sector are closely related to the location and intensity of the auroral electrojet. This effect is not taken into account by existing theories of ULF Pc5 waves, but it could be significant for the development of more adequate models. Consideration of the most evident interpretation schemes shows that there is no simple explanation of this effect. An adequate interpretation may require a substantial revision or augmentation of existing Pc5 models. (C) 2001 Published by Elsevier Science Ltd. CR ALLAN W, 1983, J GEOPHYS RES, V88, P183 ALLAN W, 1984, REV GEOPHYS, V22, P85 BARFIELD JN, 1972, J GEOPHYS RES, V77, P4720 COLQUI R, 1998, MEMOIRS FS KYASHU D, V30, P1 ENGEBRETSON MJ, 1991, J GEOPHYS RES, V86, P1527 KISABETH JL, 1971, J GEOPHYS RES, V76, P6815 KIVELSON MG, 1984, PLANET SPACE SCI, V32, P1335 KOTIKOV AL, 1987, GEOPHYSICA, V23, P143 LAM HL, 1978, PLANET SPACE SCI, V26, P473 LYATSKY VB, 1983, MAGNETOSPHERE IONOSP LYSAK RL, 1991, J GEOPHYS RES, V96, P1553 MALTSEV YP, 1974, PLANET SPACE SCI, V22, P1519 MISHIN VV, 1986, GEOMAGN AERON, V26, P952 NOPPER RW, 1982, J GEO R-S P, V87, P5911 OLSON JV, 1980, J GEOPHYS RES, V85, P1695 OLSON JV, 1978, J GEOPHYS RES, V83, P2481 PILIPENKO VA, 1990, J ATMOS TERR PHYS, V52, P1193 POPOV VA, 2001, EARTH PLANETS SPACE, V53, P129 POPOV VA, 1996, GEOMAGN AERON+, V36, P43 ROSTOKER G, 1987, PLANET SPACE SCI, V35, P429 ROSTOKER G, 1978, PLANET SPACE SCI, V26, P493 SAKA O, 1982, J GEOPHYS RES, V87, P2331 SAKA O, 1992, J GEOPHYS RES-SPACE, V97, P10693 SAMSON JC, 1972, J GEOPHYS RES, V77, P6145 SCHOTT JJ, 1998, EARTH PLANETS SPACE, V50, P101 SUTCLIFFE PR, 1979, PLANET SPACE SCI, V27, P631 TAMAO T, 1986, J GEOPHYS RES, V91, P183 TIKHONOV AN, 1977, SOLUTION ILL POSED P WALKER ADM, 1982, J GEOPHYS RES, V87, P9135 WALKER ADM, 1979, J GEOPHYS RES, V84, P3373 YUMOTO K, 1980, PLANET SPACE SCI, V28, P789 ZIESOLLECK CWS, 1994, J GEOPHYS RES, V99, P5817 TC 1 BP 1545 EP 1557 PG 13 JI J. Atmos. Sol.-Terr. Phys. PY 2001 PD SEP VL 63 IS 14 GA 469VT PI OXFORD RP Pilipenko VA Inst Phys Earth, B Gruzinskaya 10, Moscow 123995, Russia J9 J ATMOS SOL-TERR PHYS PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000170837300009 ER PT Journal AU Saracco, R TI Pooled slicing methods versus slicing methods SO COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION LA English DT Article NR 19 SN 0361-0918 PU MARCEL DEKKER INC C1 Univ Montpellier 2, Lab Probabilites & Stat, F-34095 Montpellier 5, France DE semiparametric regression; dimension reduction; sliced inverse regression ID SLICED INVERSE REGRESSION; DIMENSION REDUCTION; DATA VISUALIZATION; ASYMPTOTIC THEORY; LINK AB We consider the semiparametric regression model introduced by (1). The dependent variable y is linked to the index x ' beta through an unknown link function. (1) and (2) present Slicing methods (the Sliced Inverse Regression methods SIR-I, SIR-II and SIR.) in order to estimate the direction of the unknown slope parameter beta. These methods are computationally simple and fast but depend on the choice of an arbitrary slicing fixed by the user, When the sample size is small, the number and the position of slices have an influence on the estimated direction. In this paper. we suggest to use the corresponding Pooled Slicing methods: PSIR-I (proposed by (3)), PSIR-II and PSIRalpha. These methods combine the results from a number of slicings. We compare the sample behaviour of Slicing and Pooled Slicing methods on simulations. We also propose a practical choice of alpha in SIRalpha and PSIRalpha methods. CR ARAGON Y, 1997, COMPUTATION STAT, V12, P109 ARAGON Y, 1997, COMPUTATION STAT, V12, P355 ARAGON Y, 1994, MODELLING INCOME DIS CARROLL RJ, 1992, J AM STAT ASSOC, V87, P1040 CARROLL RJ, 1995, STAT SINICA, V5, P667 COOK RD, 1991, J AM STAT ASSOC, V86, P328 DUAN N, 1991, ANN STAT, V19, P505 FERRE L, 1998, J AM STAT ASSOC, V441, P132 HSING TL, 1992, ANN STAT, V20, P1040 KOTTER T, 1996, COMPUTATION STAT, V11, P113 LI KC, 1992, J AM STAT ASSOC, V87, P1025 LI KC, 1991, J AM STAT ASSOC, V86, P316 LI KC, 1994, SLICED INVERSE REGRE MARDIA KV, 1991, BIOMETRIKA, V58, P105 SARACCO J, 1999, COMMUN STAT-THEOR M, V28, P2367 SARACCO J, 1997, COMMUN STAT-THEOR M, V26, P2141 SCHOTT JR, 1994, J AM STAT ASSOC, V89, P141 ZHU LX, 1996, ANN STAT, V24, P1053 ZHU LX, 1995, STAT SINICA, V5, P727 TC 0 BP 489 EP 511 PG 23 JI Commun. Stat.-Simul. Comput. PY 2001 VL 30 IS 3 GA 469WA PI NEW YORK RP Univ Montpellier 2, Lab Probabilites & Stat, F-34095 Montpellier 5, France J9 COMMUN STATIST-SIMULAT COMPUT PA 270 MADISON AVE, NEW YORK, NY 10016 USA UT ISI:000170838000004 ER PT Book in series AU Han, JC Cercone, N TI AViz: A visualization system for discovering numeric association rules SO KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS LA English DT Article NR 13 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada DE KDD; data mining; data visualization; association rules AB We introduce an interactive visualization system, AViz, for discovering numerical association rules from large data sets. The process of interactive visual discovery consists of six steps: preparing the raw data, visualizing the original data, cleaning the data, discretizing numerical attributes, and discovering and visualizing association rules. The basic framework of the AViz system is presented and three approaches to discretize numerical attributes, including equal-sized, bin- packing based equal-depth, and interaction-based approaches, are proposed and implemented. The algorithm for discovering and visualizing numerical association rules is discussed and analyzed. The AViz system has been experimented on a census data set. The experimental results demonstrate that the AViz system is useful and helpful for discovering and visualizing numerical association rules. CR AGRAWAL R, 1994, P 20 INT C VER LARG, P487 CAI Y, 1991, KNOWLEDGE DISCOVERY, P213 DERTHICK M, 1997, INTERACTIVE VISUALIZ, P2 FUKUDA T, 1996, P ACM SIGMOD C MAN D, P13 GROTH R, 1998, HANDS ON APPROACH BU HAN J, 1999, P 3 PAC AS C KNOWL D, P390 KEIM D, 1997, P INT C VER LARG DAT KEIM DA, 1996, VISUALIZATION TECHNI KENNEDY JB, 1996, FRAMEWORK INFORMATIO, V25 LIU B, 1999, P 3 PAC AS KNOWL DIS, P380 MILLER RJ, 1997, P ACM SIGMOD INT C M, P452 PIATETSKYSHAPIR.G, 1991, KNOWLEDGE DISCOVERY, P229 SRIKANT R, 1996, P ACM SIGMOD INT C M, P1 TC 0 BP 269 EP 280 PG 12 SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE PY 2000 VL 1805 GA BS61A PI BERLIN RP Han JC Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada J9 LECT NOTE ARTIF INTELL PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000170556400030 ER PT Journal AU Chen, JX Wang, SB TI Data visualization: Parallel coordinates and dimension reduction SO COMPUTING IN SCIENCE & ENGINEERING LA English DT Article NR 4 SN 1521-9615 PU IEEE COMPUTER SOC C1 George Mason Univ, Fairfax, VA 22030 USA George Mason Univ, Fairfax, VA 22030 USA CR BUJA A, 1985, P 17 S INT N HOLL AM, P63 DEJONG KA, 1990, MACHINE LEARNING ART, V3, P611 POTTER MA, 2000, EVOLUTIONARY COMPUTA, V8, P1 WEGMAN EJ, 1997, COMPUTING SCI STAT, V28, P352 TC 1 BP 110 EP 113 PG 4 JI Comput. Sci. Eng. PY 2001 PD SEP-OCT VL 3 IS 5 GA 466UW PI LOS ALAMITOS RP Chen JX George Mason Univ, Fairfax, VA 22030 USA J9 COMPUT SCI ENG PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000170665400015 ER PT Journal AU Gupta, A Mumick, IS Rao, J Ross, KA TI Adapting materialized views after redefinitions: techniques and a performance study SO INFORMATION SYSTEMS LA English DT Article NR 28 SN 0306-4379 PU PERGAMON-ELSEVIER SCIENCE LTD C1 IBM Corp, Almaden Res Ctr, 650 Harry Rd, San Jose, CA 95120 USA IBM Corp, Almaden Res Ctr, San Jose, CA 95120 USA Columbia Univ, New York, NY 10027 USA AB We consider a variant of the view maintenance problem: How does one keep a materialized view up-to-date when the view definition itself changes? Can one do better than recomputing the view from the base relations? Traditional view maintenance tries to maintain the materialized view in response to modifications to the base relations; we try to "adapt" the view in response to changes in the view definition. Such techniques are needed for applications where the user can change queries dynamically and wants to see the changes in the results fast. Data archaeology, data visualization, and dynamic queries are examples of such applications. Views defined over the Internet tend to evolve and our technique can be useful for adapting such views. We consider all possible redefinitions of SQL SELECT-FROM-WHERE-GROUP-BY-HAVING, UNION, and EXCEPT views, and show how these views can be adapted using the old materialization for the cases where it is possible to do so. We identify extra information that can be kept with a materialization to facilitate redefinition. Multiple simultaneous changes to a view can be handled without necessarily materializing intermediate results. We identify guidelines for users and database administrators that can be used to facilitate efficient view adaptation. We perform a systematic experimental evaluation of our proposed techniques. Our evaluation indicates that adaptation is much more efficient than rematerialization in most cases. In-place adaptation methods are better than the non-in-place methods when the change is small. We also point out some important factors that can impact the efficiency of adaptation. (C) 2001 Elsevier Science Ltd. All rights reserved. CR *TPC D, 1995, TPC D BENCHM SPEC RE AHLBERG C, 1993, SPARKS INNOVATION HU BELLAHSENE Z, 2000, INT C CONC MOD BLAKELEY J, 1986, ACM SIGMOD BORGIDA A, 1989, ACM SIGMOD JUN, P59 BRACHMAN R, 1993, INT J INTELLIGENT CO, V2, P159 BRACHMAN RJ, 1992, 1 INT C INF KNOWL MA, P457 CERI S, 1991, VLDB CHAUDHURI S, 1995, P INT C DAT ENG CHEN CM, 1994, EDBT COLBY L, 1997, SIGMOD, P405 FRENCH CD, 1995, P ACM SIGMOD C, P449 FRENCH CD, 1997, PROC INT CONF DATA, P194 GRIFFIN T, 1995, SIGMOD, P211 GUPTA A, 1993, SIGMOD, P157 GUPTA A, 1995, SIGMOD C, P211 GUPTA A, 1995, VLDB HANSON EN, 1987, SIGMOD, P440 KAWAGUCHI A, 1997, ICDT, P306 LARSON PA, 1985, VLDB, P259 LEVY AY, 1995, PODS RAJARAMAN A, 1995, PODS SRIVASTAVA D, 1996, VLDB STONEBRAKER M, 1990, SIGMOD TSATALOS OG, 1994, VLDB, P367 WILLIAMSON C, 1993, SPARKS INNOVATION HU YAN WP, 1995, P 21 INT C VER LARG, P345 YANG HZ, 1987, VLDB, P245 TC 0 BP 323 EP 362 PG 40 JI Inf. Syst. PY 2001 PD JUL VL 26 IS 5 SI SI GA 462KB PI OXFORD RP Rao J IBM Corp, Almaden Res Ctr, 650 Harry Rd, San Jose, CA 95120 USA J9 INFORM SYST PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000170418300002 ER PT Journal AU DeLorenzo, L TI Stars aren't stupid, but our methodological training is: A commentary on Jeff Gill and Ken Meier's article "public administration research and practice: A methodological manifesto" SO JOURNAL OF PUBLIC ADMINISTRATION RESEARCH AND THEORY LA English DT Article NR 4 SN 1053-1858 PU TRANSACTION PERIOD CONSORTIUM C1 Arizona State Univ, Tempe, AZ 85287 USA Arizona State Univ, Tempe, AZ 85287 USA AB Jeff Gill and Kenneth J. Meier argue in their J-PART article "Public Administration Research and Practice: A Methodological Manifesto " that there is a crisis in public administration research. Public administration lags woefully behind political science and similar social scientific fields in its methodological sophistication. Part of the problem, they contend, is the misuse of null hypothesis significance testing (NHST); they suggest that NHST be abandoned altogether. They promote other methodological approaches that might be more useful to public administration research. This rejoinder supports their push to use alternative and more sophisticated methods in public administration research and adds that data visualization techniques, including the use of GIS, should be added to their list of valuable techniques. However, NHST testing should not be wholly abandoned because of misuse and ignorance. Instead, it is proposed here that the quality and focus of methodological training in public administration programs be improved and that those who work in the field develop a consensus on just what quantitative basics PA students should know. CR BAYBECK B, 1998, ANN M AM POL SCI ASS DELORENZO L, 1996, UNPUB DELORENZO L, 2000, URBAN PLANNING DEV A GILL J, 2000, J PUBLIC ADM RES THE, V10, P157 TC 0 BP 139 EP 145 PG 7 JI J. Publ. Adm. Res. Theory PY 2001 PD JAN VL 11 IS 1 GA 461LE PI PISCATAWAY RP DeLorenzo L Arizona State Univ, Tempe, AZ 85287 USA J9 J PUBLIC ADM RES THEORY PA RUTGERS UNIV, DEPT 8010, 35 BERRUE CIRCLE, PISCATAWAY, NJ 08854-8042 USA UT ISI:000170365100007 ER PT Journal AU Huang, B Jiang, B Li, H TI An integration of GIS, virtual reality and the Internet for visualization, analysis and exploration of spatial data SO INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE LA English DT Article NR 38 SN 1365-8816 PU TAYLOR & FRANCIS LTD C1 Chinese Univ Hong Kong, Dept Geog, Shatin, Hong Kong, Peoples R China Chinese Univ Hong Kong, Dept Geog, Shatin, Hong Kong, Peoples R China Chinese Univ Hong Kong, Joint Lab Geoinformat Sci, Shatin, Hong Kong, Peoples R China Univ Gavle, Inst Tekn, Div Geomat, SE-80176 Gavle, Sweden ID JAVA; VRML AB This paper explores the way in which GIS, Virtual Reality (VR) and the Internet are closely integrated through the link of Virtual Reality Modelling Language (VRML) for spatial data visualization, analysis and exploration. Integration takes advantage of each component, and enables the dynamic 3D content to be built, visualized, interacted with and deployed all on the Web. To accomplish this, a hybrid approach that merges the conventional client-side and server-side methods is proposed, which offers the best of both worlds in terms of flexibility and capability, as well as the rational use of computing resources. Based on this approach, a Web-based prototype toolkit is designed and implemented by using an affordable desktop GIS through its macro language together with Java, Common Gateway Interface (CGI) and HTML programming. This toolkit comprises a 3D visualization tool, a 3D analysis tool, and a Java/VRML interface, which are respectively used for the creation of VRML models from 2D maps, surface analysis (e.g. profile creation and visibility analysis), and interaction (e.g. selecting and querying) with the output VRML worlds of 3D visualization and analysis. It is demonstrated that this toolkit provides an integrated environment, facilitating users to gain insights from the interaction with virtual environments that are built from existing GIS databases. 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J. Geogr. Inf. Sci. PY 2001 PD JUL-AUG VL 15 IS 5 GA 458UN PI LONDON RP Huang B Chinese Univ Hong Kong, Dept Geog, Shatin, Hong Kong, Peoples R China J9 INT J GEOGR INF SCI PA 11 NEW FETTER LANE, LONDON EC4P 4EE, ENGLAND UT ISI:000170213100004 ER PT Journal AU Doi, S Takei, T Matsumoto, H TI Experiences in large-scale volume data visualization with RVSLIB SO COMPUTER GRAPHICS-US LA English DT Article NR 2 SN 0097-8930 PU ASSOC COMPUTING MACHINERY C1 NEC Corp Ltd, Kawasaki, Kanagawa 213, Japan NEC Corp Ltd, Kawasaki, Kanagawa 213, Japan CR DOI S, 1996, NEC RES DEV, V37, P114 DOI S, 1997, SPEEDUP J, V11, P59 TC 0 BP 10 EP 13 PG 4 JI Comput. Graph.-US PY 2001 PD MAY VL 35 IS 2 GA 459DM PI NEW YORK RP Doi S NEC Corp Ltd, Kawasaki, Kanagawa 213, Japan J9 COMPUT GRAPHICS-US PA 1515 BROADWAY, NEW YORK, NY 10036 USA UT ISI:000170235800004 ER PT Journal AU Daniel, TC TI Whither scenic beauty? Visual landscape quality assessment in the 21st century SO LANDSCAPE AND URBAN PLANNING LA English DT Article NR 113 SN 0169-2046 PU ELSEVIER SCIENCE BV C1 Univ Arizona, Sch Renewable Nat Resources, Dept Psychol, Tucson, AZ 85721 USA Univ Arizona, Sch Renewable Nat Resources, Dept Psychol, Tucson, AZ 85721 USA DE landscape quality assessment; landscape aesthetics; ecosystem management ID NATURAL-ENVIRONMENT; FOREST; PERCEPTION; PREFERENCE; MANAGEMENT; AESTHETICS; STANDS; PERSPECTIVE; RELIABILITY; PHOTOGRAPHS AB The history of landscape quality assessment has featured a contest between expert and perception-based approaches, paralleling a long-standing debate in the philosophy of aesthetics. The expert approach has dominated in environmental management practice and the perception-based approach has dominated in research. Both approaches generally accept that landscape quality derives from an interaction between biophysical features of the landscape and perceptual/judgmental processes of the human viewer. The approaches differ in the conceptualizations of and the relative importance of the landscape and human viewer components. At the close of the 20th century landscape quality assessment practice evolved toward a shaky marriage whereby both expert and perceptual approaches are applied in parallel and then, in some as yet unspecified way, merged in the final environmental management decision making process. The 21st century will feature continued momentum toward ecosystem management where the effects of changing spatial and temporal patterns of landscape features. at multiple scales and resolutions, will be more important than any given set of features at any one place at any one time. Valid representation of the visual implications of complex gee- temporal dynamics central to ecosystem management will present major challenges to landscape quality assessment. Technological developments in geographic information systems, simulation modeling and environmental data visualization will continue to help meet those challenges. At a more fundamental level traditional landscape assessment approaches will be challenged by the deep ecology and green philosophy movements which advocate a strongly bio-centric approach to landscape quality assessment where neither expert design principles nor human perceptions and preferences are deemed relevant. On the opposite side of the landscape-human interaction, social/cultural construction models that construe the landscape as the product of socially instructed human interpretation leave little or no role for biophysical landscape features and processes. A psychophysical approach is advocated to provide a more appropriate balance between biophysical and human perception/judgement components of an operationally delimited landscape quality assessment system. (C) 2001 Published by Elsevier Science B.V. 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Urban Plan. PY 2001 PD MAY 25 VL 54 IS 1-4 GA 448FQ PI AMSTERDAM RP Daniel TC Univ Arizona, Sch Renewable Nat Resources, Dept Psychol, Tucson, AZ 85721 USA J9 LANDSCAPE URBAN PLAN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000169616600021 ER PT Journal AU Ma, KL TI Large-scale data visualization SO IEEE COMPUTER GRAPHICS AND APPLICATIONS LA English DT Editorial Material NR 4 SN 0272-1716 PU IEEE COMPUTER SOC C1 Univ Calif Davis, Dept Comp Sci, 1 Shields Ave, Davis, CA 95616 USA Univ Calif Davis, Dept Comp Sci, Davis, CA 95616 USA CR FUNKHOUSER T, 2000, IEEE COMPUT GRAPH, V20, P20 MA KL, 2000, IEEE COMPUT GRAPH, V20, P16 MCCORMICK BH, 1987, P SIGG 87, V21 SMITH PH, 1998, DATA VISUALIZATION C TC 0 BP 22 EP 23 PG 2 JI IEEE Comput. Graph. Appl. PY 2001 PD JUL-AUG VL 21 IS 4 GA 447CG PI LOS ALAMITOS RP Ma KL Univ Calif Davis, Dept Comp Sci, 1 Shields Ave, Davis, CA 95616 USA J9 IEEE COMPUT GRAPH APPL PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000169550500005 ER PT Journal AU Ahrens, J Brislawn, K Martin, K Geveci, B Law, CC Papka, M TI Large-scale data visualization using parallel data streaming SO IEEE COMPUTER GRAPHICS AND APPLICATIONS LA English DT Article NR 22 SN 0272-1716 PU IEEE COMPUTER SOC C1 Kitware, 469 Dlifton Corp Pkwy, Clifton Pk, NY USA Kitware, Clifton Pk, NY USA Argonne Natl Lab, Div Math & Comp Sci, Futures Lab, Argonne, IL 60439 USA ID ENVIRONMENT AB We present an architectural approach based on parallel data streaming to enable visualizations on a parallel cluster. Our approach requires less memory than other visualizations while achieving high code reuse. CR ABRAM G, 1995, P IEEE VIS 1995 C OC, P263 CHIANG YJ, 1998, P IEEE VIS 98 NOV, P167 COX M, 1997, COURSE NOTES, V4 COX M, 1997, P IEEE VISUALIZATION, P235 FUNKHOUSER TA, 1995, COURSE NOTES, V32 GROPP W, 1994, USING MPI PROTABLE P HAIMES R, 1997, VISUALIZATION PARALL HUMPHREYS G, 2000, P SUP CD ROM ITOH T, 1995, IEEE T VIS COMPUT GR, V1, P319 JOHNSON CR, 1999, 9 SIAM C PAR PROC SC KROGH M, 1993, AVS US C, P129 LAW CC, 1999, P VIS 99, P225 MILLER M, 1998, LECT NOTES COMPUT SC, V1541, P366 MOLNAR S, 1994, IEEE COMPUT GRAPH, V4, P23 MORAN PJ, 1999, P IEEE VIS 1999, P27 PARKER SG, 1997, MODERN SOFTWARE TOOL, P1 SCHROEDER WJ, 1996, VISUALIZATION TOOLKI SONG D, 1993, P IEEE VIS 1993, P126 SUBRAMANIAN S, 1995, P ACM SIAM S DISCR A, P378 TELLER S, 1994, P SIGGRAPH 94, P443 UENG SK, 1997, IEEE T VIS COMPUT GR, V3, P370 UPSON C, 1989, IEEE COMPUT GRAPH, V9, P30 TC 1 BP 34 EP 41 PG 8 JI IEEE Comput. Graph. Appl. PY 2001 PD JUL-AUG VL 21 IS 4 GA 447CG PI LOS ALAMITOS RP Ahrens J Kitware, 469 Dlifton Corp Pkwy, Clifton Pk, NY USA J9 IEEE COMPUT GRAPH APPL PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000169550500007 ER PT Journal AU Brodlie, K Wood, J TI Recent advances in volume visualization SO COMPUTER GRAPHICS FORUM LA English DT Article NR 97 SN 0167-7055 PU BLACKWELL PUBL LTD C1 Univ Leeds, Sch Comp, Leeds, W Yorkshire, England Univ Leeds, Sch Comp, Leeds, W Yorkshire, England DE scientific visualization; data visualization; volume visualization; isosurfacing; slicing; volume rendering ID SURFACES; RECONSTRUCTION; ISOSURFACES; ALGORITHM; CONTOURS; TREES; GRIDS AB In the past few years, there have been key advances in the three main approaches to the visualization of volumetric data: isosurfacing, slicing and volume rendering, which together make up the field of volume visualization. In this survey paper we set the scene by describing the fundamental techniques for each of these approaches, using this to motivate the range of advances which have evolved over the past few years. In isosurfacing, we see how the original marching cubes algorithm has matured, with improvements in robustness. topological consistency, accuracy and performance. In the performance area, we look in detail at pre-processing steps which help identify data which contributes to the particular isosurface required In slicing too, there are performance gains from identifying active cells quickly. In volume rendering, we describe the two main approaches of ray casting and projection. Both approaches have evolved technically over the past decade, and the holy grail of real-time volume rendering has arguably been reached. The aim of this review paper is to pull these developments together in a coherent review of recent advances in volume visualization. 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Graph. Forum PY 2001 PD JUN VL 20 IS 2 GA 445AP PI OXFORD RP Brodlie K Univ Leeds, Sch Comp, Leeds, W Yorkshire, England J9 COMPUTER GRAPHICS FORUM PA 108 COWLEY RD, OXFORD OX4 1JF, OXON, ENGLAND UT ISI:000169435200006 ER PT Journal AU Thadathil, P Ghosh, AK Sarupria, JS Gopalakrishna, VV TI An interactive graphical system for XBT data quality control and visualization SO COMPUTERS & GEOSCIENCES LA English DT Article NR 11 SN 0098-3004 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Natl Inst Oceanog, Dona Paula 403004, Goa, India Natl Inst Oceanog, Dona Paula 403004, Goa, India DE quality flags; surface transient; fall rate error; temperature inversion; visual C plus AB A PC-based system has been developed for quality control and visualization of expendable bathy thermograph (XBT) data archived at the Indian Oceanographic Data Centre. The system, coded in Visual C++, is user interactive and runs on Windows-95 platform. Quality control module of the system incorporates various quality norms/checks and has two levels; inventory and data levels. Inventory level checks are applied for land-sea position, speed of the vessel, invalid date/time duplicates and station sounding. Station sounding check is performed based on the ETOPO bathymetry file having 5 ' x 5 ' spatial resolution. Although the QCS is developed for quality control and visualization of the XBT data, it could be used for inventory level quality checks of any general oceanographic data. Data quality module involves tests for XBT-specific errors such as surface transient, temperature inversions, fall rate and depth reversal. This level also involves visual inspection of the profiles for identifying and correcting/flagging of features caused by wire stretch, wire break, bowing and nub in the mixed layer. Provision is given to compare individual XBT profiles with neighboring stations and also with 1 degrees x 1 degrees monthly climatologies. Quality flags are assigned for each inventory and depth fields. Since data exchange (national and international), under the IODE system, stipulates standard quality flags, the Integrated Global Ocean Observing System recommended flags are applied in the system. Station having data with erroneous or doubtful Rags are sent to error bin, which can be accessed by privileged users for possible correction and subsequent modifications in the data base. The data visualization module has options For data queries, selection and graphical presentations, like vertical and horizontal distribution of temperature, (C) 2001 Elsevier Science Ltd. All rights reserved. CR *UNESCO, 1988, IOC MAN GUID, P3 BAILEY RA, 1993, CSIRO COOKBOOK QUALI HANAWA K, 1995, DEEP-SEA RES, V43, P1423 NAGATA Y, 1979, J OCEANOGR SOC JAPAN, V35, P141 RAO BP, 1987, P INDIAN ACAD SCI, V96, P69 RAO DP, 1981, MAHASAGAR B NATL I O, V14, P1 RAO RR, 1983, MAUSAM, V32, P92 ROEMMICH D, 1987, DEEP-SEA RES, V34, P299 THADATHIL P, 1998, DEEP-SEA RES PT I, V45, P819 THADATHIL P, 1999, J ATMOSPHERIC OCEANI, V16, P171 THADATHIL P, 1992, J OCEANOGR, V48, P293 TC 0 BP 867 EP 876 PG 10 JI Comput. Geosci. PY 2001 PD AUG VL 27 IS 7 GA 442WL PI OXFORD RP Thadathil P Natl Inst Oceanog, Dona Paula 403004, Goa, India J9 COMPUT GEOSCI PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000169307900011 ER PT Journal AU Searle, BG TI DL Visualize SO COMPUTER PHYSICS COMMUNICATIONS LA English DT Article NR 4 SN 0010-4655 PU ELSEVIER SCIENCE BV C1 Daresbury Lab, CLRC, Warrington WA4 4AD, Cheshire, England Daresbury Lab, CLRC, Warrington WA4 4AD, Cheshire, England DE visualization; surface science; CRYSTAL98 AB DL Visualize is designed to provide an integrated environment for data visualization and analysis. It supports the creation and visualization of molecules and atomic structures periodic in 2 or 3 dimensions. It can display calculated results as graphs, contours and isosurfaces depending on the data type. It currently provides a graphical user interface to CTZYSTAL98 and will be extended to support other programs from the CCPS library in the near future. (C) 2001 Elsevier Science B.V. All rights reserved. CR HARRISON N, 2001, COMPUT PHYS COMMUN, V137, P74 HARRISON NM, 2001, COMPUT PHYS COMMUN, V137, P59 SAUNDERS VR, 1998, CRYSTAL98 USERS MANU WANDER A, 2001, COMPUT PHYS COMMUN, V137, P4 TC 3 BP 25 EP 32 PG 8 JI Comput. Phys. Commun. PY 2001 PD JUN 1 VL 137 IS 1 GA 436BU PI AMSTERDAM RP Searle BG Daresbury Lab, CLRC, Warrington WA4 4AD, Cheshire, England J9 COMPUT PHYS COMMUN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000168917300005 ER PT Journal AU Sungar, N Sharpe, JP Moelter, MJ Fleishon, N Morrison, K McDill, J Schoonover, R TI A laboratory-based nonlinear dynamics course for science and engineering students SO AMERICAN JOURNAL OF PHYSICS LA English DT Article NR 19 SN 0002-9505 PU AMER INST PHYSICS C1 Calif Polytech State Univ San Luis Obispo, Dept Phys, San Luis Obispo, CA 93407 USA Calif Polytech State Univ San Luis Obispo, Dept Phys, San Luis Obispo, CA 93407 USA Calif Polytech State Univ San Luis Obispo, Dept Math, San Luis Obispo, CA 93407 USA Calif Polytech State Univ San Luis Obispo, Dept Chem, San Luis Obispo, CA 93407 USA ID PHASE-TRANSITIONS; DOUBLE PENDULUM; FIRST-ORDER; CHAOS AB We describe the implementation of a new laboratory-based interdisciplinary undergraduate course on nonlinear dynamical systems. Geometrical methods and data visualization techniques are especially emphasized. A novel feature of the course is a required laboratory where the students analyze the behavior of a number of dynamical systems. Most of the laboratory experiments can be economically implemented using equipment available in many introductory physics microcomputer-based laboratories. (C) 2001 American Association of Physics Teachers. CR BERGER JE, 1997, AM J PHYS, V65, P841 BISHOP SR, 1993, APPL FRACTALS CHAOS BRIGGS K, 1987, AM J PHYS, V55, P1083 CAMPBELL JA, 1957, J CHEM EDUC, V34, P362 CAMPBELL JA, 1957, J CHEM EDUC, V34, PA105 DREYER K, 1991, AM J PHYS, V59, P619 EARLE S, 1985, CHEM DEMONSTRATIONS, V2, PCH7 FLETCHER G, 1997, AM J PHYS, V65, P74 GOLLWITZER H, 1996, DIFFERENTIAL SYSTEMS HILBORN RC, 1997, AM J PHYS, V65, P822 HILBORN RC, 1994, CHAOS NONLINEAR DYNA KENNEDY MP, 1993, IEEE T CIRCUITS-I, V40, P657 LAUTERBORN W, 1995, COHERENT OPTICS FUND, PCH10 LEVIEN RB, 1993, AM J PHYS, V61, P1038 MANCUSO RV, 2000, AM J PHYS, V68, P271 MURRAY JD, 1993, MATH BIOL SHINBROT T, 1992, AM J PHYS, V60, P491 STROGATZ SH, 1994, NONLINEAR DYNAMICS C VANHOOK SJ, 1997, PHYS TEACH, V35, P391 TC 1 BP 591 EP 597 PG 7 JI Am. J. Phys. PY 2001 PD MAY VL 69 IS 5 GA 425JV PI MELVILLE RP Sungar N Calif Polytech State Univ San Luis Obispo, Dept Phys, San Luis Obispo, CA 93407 USA J9 AMER J PHYS PA 2 HUNTINGTON QUADRANGLE, STE 1NO1, MELVILLE, NY 11747-4501 USA UT ISI:000168288200011 ER PT Journal AU Friedman, JH TI The role of Statistics in the data revolution? SO INTERNATIONAL STATISTICAL REVIEW LA English DT Article NR 1 SN 0306-7734 PU INT STATISTICAL INST C1 Stanford Univ, Dept Stat, Stanford, CA 94305 USA Stanford Univ, Dept Stat, Stanford, CA 94305 USA DE data analysis; data mining; decision support; computational statistics AB The nature of data is rapidly changing. Data sets are becoming increasingly large and complex Modern methodology for analyzing these new types of data are emerging from the fields of Data Base Management, Artificial Intelligence, Machine Learning, Pattern Recognition, and Data Visualization, So far Statistics as a held has played a minor role. This paper explores some of the reasons for this, and why statisticians should have an interest in participating in the development of new methods for large and complex data sets. CR TUKEY JW, 1962, ANN MATH STAT, V33, P1 TC 3 BP 5 EP 10 PG 6 JI Int. Stat. Rev. PY 2001 PD APR VL 69 IS 1 GA 422NN PI VOORBURG RP Friedman JH Stanford Univ, Dept Stat, Stanford, CA 94305 USA J9 INT STATIST REV PA 428 PRINSES BEATRIXLAAN, 2270 AZ VOORBURG, NETHERLANDS UT ISI:000168125200002 ER PT Journal AU Niederjohn, S TI Data visualization: The new BAS tool SO ASHRAE JOURNAL LA English DT Article NR 4 SN 0001-2491 PU AMER SOC HEATING REFRIGERATING AIR-CONDITIONING ENG, INC, C1 Johnson Controls Inc, Milwaukee, WI 53201 USA Johnson Controls Inc, Milwaukee, WI 53201 USA CR BROOKS PL, 1997, DBMS INTERNET SYSTEM PACK T, 1998, DATABASE FEB SINGERS RR, 1994, S HUM INT COMPL SYST TERESKO J, 1996, IND WEEK AUG TC 0 BP 23 EP + PG 5 JI ASHRAE J. PY 2001 PD APR VL 43 IS 4 GA 422HB PI ATLANTA RP Niederjohn S Johnson Controls Inc, Milwaukee, WI 53201 USA J9 ASHRAE J PA 1791 TULLIE CIRCLE NE, ATLANTA, GA 30329 USA UT ISI:000168111500008 ER PT Journal AU Shoop, E Silverstein, KAT Johnson, JE Retzel, EF TI MetaFam: a unified classification of protein families. II. Schema and query capabilities SO BIOINFORMATICS LA English DT Article NR 44 SN 1367-4803 PU OXFORD UNIV PRESS C1 Univ Minnesota, Acad Hlth Ctr, Comp Biol Ctr, Mayo Mail Code 420 Delaware St SE, Minneapolis, MN 55455 USA Univ Minnesota, Acad Hlth Ctr, Comp Biol Ctr, Minneapolis, MN 55455 USA ID SEQUENCE DATABASE CLASSIFICATION; RETRIEVAL-SYSTEM; BIOLOGY; INTEGRATION; ALIGNMENTS; SEARCH; ENTREZ; FOLDS; PIR; SET AB Motivation: Protein sequence and family data is accumulating at such a rapid rate that state-of-the-art databases and interface tools are required to aid curators with their classifications. We have designed such a system, MetaFam, to facilitate the comparison and integration of public protein sequence and family data, This paper presents the global schema, integration issues, and query capabilities of MetaFam. Results: MetaFam is an integrated data warehouse of information about protein families and their sequences. This data has been collected into a consistent global schema, and stored in an Oracle relational database. The warehouse implementation allows for quick removal of outdated data sets, in addition to the relational implementation of the primary schema, we have developed several derived tables that enable efficient access from data visualization and exploration tools. Through a series of straightforward SQL queries, we demonstrate the usefulness of this data warehouse for comparing protein family classifications and for functional assignment of new sequences. Availability: Access to the MetaFam database is provided through a Java applet called MetaFamView, which can be run from the MetaFam web site at http://www.metafam. ahc.umn.edu/. Access to the relational data via named Oracle accounts can be arranged with the authors. Arrangements can also be made to obtain the data in Oracle 'export dump' format. Supplementary information: The complete relational schema, integration scripts, and analysis queries are available from the authors. CR ALTSCHUL SF, 1997, NUCLEIC ACIDS RES, V25, P3389 ATTWOOD TK, 2000, NUCLEIC ACIDS RES, V28, P225 BABBITT PC, 2000, COMMUNICATION BABBITT PC, 1997, J BIOL CHEM, V272, P30591 BAIROCH A, 2000, NUCLEIC ACIDS RES, V28, P45 BAKER PG, 1999, BIOINFORMATICS, V15, P510 BAKER PG, 1998, P 6 INT C INT SYST M, P25 BARKER WC, 2000, NUCLEIC ACIDS RES, V28, P41 BATEMAN A, 2000, NUCLEIC ACIDS RES, V28, P263 BENSON DA, 2000, NUCLEIC ACIDS RES, V28, P15 BUNEMAN P, 1999, BIOINFORMATICS DATAB CHEN I, 1995, INFORMATION SYS, V20 CHEN I, 1998, P INT SYST MOL BIOL, P43 CHUNG SY, 1999, TRENDS BIOTECHNOL, V17, P351 CORPET F, 2000, NUCLEIC ACIDS RES, V28, P267 DAVIDSON S, 1997, INT J DIGITAL LIB, V1, P36 DAVIDSON SB, 1995, J COMPUT BIOL, V2, P557 ETZOLD T, 1993, COMPUT APPL BIOSCI, V9, P59 ETZOLD T, 1996, METHOD ENZYMOL, V266, P114 ETZOLD T, 1996, P PAC S BIOC 97, P134 FAYYAD U, 1996, ADV KNOWLEDGE DISCOV FRISHMAN D, 1998, BIOINFORMATICS, V14, P551 FUJIBUCHI W, 1997, PACIFIC S BIOCOMPUTI, P683 GRACY J, 1998, BIOINFORMATICS, V14, P164 GRACY J, 1998, BIOINFORMATICS, V14, P174 GRIBSKOV M, 1999, MOL INFORMATION AGEN HENIKOFF JG, 2000, NUCLEIC ACIDS RES, V28, P228 HOFMANN K, 1999, NUCLEIC ACIDS RES, V27, P215 HOLM L, 1999, NUCLEIC ACIDS RES, V27, P244 HUBBARD TJP, 1999, NUCLEIC ACIDS RES, V27, P254 KRAUSE A, 2000, NUCLEIC ACIDS RES, V28, P270 MACAULEY J, 1998, BIOINFORMATICS, V14, P575 MARKOWITZ V, 1996, THEORETICAL COMPUTAT MCENTYRE J, 1998, TRENDS GENET, V14, P39 MURVAI J, 2000, NUCLEIC ACIDS RES, V28, P260 ORENGO CA, 1999, NUCLEIC ACIDS RES, V27, P275 OVERBEEK R, 1995, GENOBASE RITTER O, 1994, COMPUT BIOMED RES, V27, P97 ROBBINS RJ, 1993, REPORT INVITATIONAL SCHULER GD, 1996, METHOD ENZYMOL, V266, P141 SILVERSTEIN KAT, 2001, BIOINFORMATICS, V17, P249 SOWDHAMINI R, 1996, FOLD DES, V1, P209 SRINIVASARAO GY, 1999, BIOINFORMATICS, V15, P382 YONA G, 2000, NUCLEIC ACIDS RES, V28, P49 TC 1 BP 262 EP 271 PG 10 JI Bioinformatics PY 2001 PD MAR VL 17 IS 3 GA 421FJ PI OXFORD RP Shoop E Univ Minnesota, Acad Hlth Ctr, Comp Biol Ctr, Mayo Mail Code 420 Delaware St SE, Minneapolis, MN 55455 USA J9 BIOINFORMATICS PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND UT ISI:000168053800006 ER PT Journal AU Seron, FJ Badal, JI Sabadell, FJ TI Spatial prediction procedures for regionalization and 3-D imaging of Earth structures SO PHYSICS OF THE EARTH AND PLANETARY INTERIORS LA English DT Article NR 95 SN 0031-9201 PU ELSEVIER SCIENCE BV C1 Univ Zaragoza, Higher Polytech Ctr, Dept Comp Sci, Maria Luna 3, Zaragoza 50015, Spain Univ Zaragoza, Higher Polytech Ctr, Dept Comp Sci, Zaragoza 50015, Spain Univ Zaragoza, E-50009 Zaragoza, Spain DE regionalization; interpolation; volumetric visualization; surface wave tomography ID GROUP-VELOCITY TOMOGRAPHY; RAYLEIGH-WAVE DISPERSION; UPPER MANTLE; SCATTERED DATA; SURFACE; INTERPOLATION; CRUST; INVERSION; MODELS; SPAIN AB The imaging of three-dimensional (3-D) Earth structures from tomographic results is an especially delicate subject due to various problems. When the data provided by the seismic surveys are averaged values that do not describe locally the medium, surface wave tomography is a relevant example of this, the regularization constraints to be imposed in the inverse process are fairly subjective. In fact, the methods for regionalization of the seismic information (dispersion data, attenuation parameters, etc.) involve an inverse problem that usually must be solved by following a mathematical approach. Opposite to this, we propose various non-inverse procedures with a common target: to reconstruct 3-D Earth structures from irregularly sampled seismic data consisting of path-averaged values depending on the wavelength or varying with depth. For this purpose we review different imaging techniques aimed at volumetric modeling and visualization of data. We refer to special rendering methods and the key operations concerning the methodology to follow: gridding and interpolation. In order to get a faster and simpler volume visualization way, we use a regular voxel grid that we achieve by means of a selected gridding algorithm under specific controls. Exact-type methods (inverse distance weighting, kriging, splines, finite differences, gridding triangulation, wavelets and gradient- wavelets) and approximate methods (least-square fitting with splines) are very briefly described. In particular, both a modified 2-D slicing Laplacian method, based on interpolation by finite differences, and the 3-D direct wavelets and gradient-wavelets methods are original. To compare the accuracy and computational efficiency of all these methods, we have them applied to synthetic data trying the reconstruction of specific volumes whose (geometrical and physical) characteristics are known. We also have used real data and hereby show some tomographic solutions related to the research of domains at different scales and depths. (C) 2001 Elsevier Science B.V. All rights reserved. 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Earth Planet. Inter. PY 2001 PD APR VL 123 IS 2-4 SI SI GA 417BY PI AMSTERDAM RP Seron FJ Univ Zaragoza, Higher Polytech Ctr, Dept Comp Sci, Maria Luna 3, Zaragoza 50015, Spain J9 PHYS EARTH PLANET INTERIORS PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000167816600006 ER PT Journal AU Yi, P Xiao, YC Ciccolini, A Frommer, G Zhang, DN TI Rule-based model for traffic accident visualization and analysis SO JOURNAL OF COMPUTING IN CIVIL ENGINEERING LA English DT Article NR 7 SN 0887-3801 PU ASCE-AMER SOC CIVIL ENGINEERS C1 Univ Akron, Dept Civ Engn, Akron, OH 44325 USA Univ Akron, Dept Civ Engn, Akron, OH 44325 USA Univ Akron, Dept Math & Comp Sci, Akron, OH 44325 USA AB This research develops and tests a computerized model for intersection accident data visualization and analysis. The model implemented a rule-based visualization module that automatically constructs the collision diagram according to traffic flow conflicts, where accident characteristics can be analyzed either individually or collectively. A data acquisition module was developed for accessing accident databases maintained in state transportation and public safety departments. Using a local database management system as part of the model, the user can select to analyze any accident data through Structured Query Language statements. In addition, this model has implemented a data analysis and graphing module for comparing similarities and identifying differences in accident data and for presenting the results of the analysis through charts and tables. The model was tested with accident data obtained from the Ohio Department of Public Safety in a number of scenario studies, and some of them were presented in this paper as examples. Because the model is very time efficient and user friendly, it can be developed into an effective engineering tool for field applications. CR *FHWA, 1996, MAN UN TRAFF CONTR D *I TRANSP ENG, 1992, TRAFF ENG FOLEY JD, 1990, COMPUTER GRAPHICS PR GARBER NJ, 1997, TRAFFIC HIGHWAY ENG SCHROEDER W, 1997, VISUALIZATION TOOLKI SILBERSCHATZ A, 1998, DATABASE SYSTEM CONC TANEMNBAUM AS, 1995, DISTRIBUTED OPERATIN TC 0 BP 129 EP 136 PG 8 JI J. Comput. Civil. Eng. PY 2001 PD APR VL 15 IS 2 GA 415HD PI RESTON RP Xiao YC Univ Akron, Dept Civ Engn, Akron, OH 44325 USA J9 J COMPUT CIVIL ENG PA 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA UT ISI:000167714600005 ER PT Journal AU Asghar, MW Barner, KE TI Nonlinear multiresolution techniques with applications to scientific visualization in a haptic environment SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS LA English DT Article NR 43 SN 1077-2626 PU IEEE COMPUTER SOC C1 Qualicom Inc, 5775 Morehouse Dr, San Diego, CA 92121 USA Qualicom Inc, San Diego, CA 92121 USA Univ Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA DE visualization; haptics; multiresolution; wavelets; nonlinear filtering; blindness ID FILTERS AB This paper develops nonlinear multiresolution techniques for scientific visualization utilizing haptic methods. The visualization of data is critical to many areas of scientific pursuit. Scientific visualization is generally accomplished through computer graphic techniques. Recent advances in haptic technologies allow visual techniques to be augmented with haptic methods. The kinesthetic feedback provided through haptic techniques provides a second modality for visualization and allows for active exploration. Moreover, haptic methods can be utilized by individuals with visual impairments. Haptic representations of large data sets, however, can be confusing to a user, especially if a visual representation is not available or cannot be used. Additionally, most haptic devices utilize point interactions, resulting in a low information bandwidth and further complicating data exploration. Multiresolution techniques can be utilized to address the issues of low information bandwidth and data complexity. Commonly used multiresolution techniques are based on the wavelet decomposition. Such linear techniques, however, tend to smooth important data features, such as discontinuities or edges. In contrast, nonlinear techniques can be utilized that preserve edge structures while removing fine data details. This paper develops a multiresolution data decomposition method based on the affine median filter. This results in a hybrid structure that can be tuned to yield a decomposition that varies from a linear wavelet decomposition to that produced by the median filter. The performance of this hybrid structure is analyzed utilizing deterministic signals and statistically in the frequency domain. This analysis and qualitative and quantitative implementation results show that the afiine median decomposition has advantages over previously proposed methods. In addition to multiresolution decomposition development, analysis, and results, haptic implementation methods are presented. 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Vis. Comput. Graph. PY 2001 PD JAN-MAR VL 7 IS 1 GA 415UZ PI LOS ALAMITOS RP Asghar MW Qualicom Inc, 5775 Morehouse Dr, San Diego, CA 92121 USA J9 IEEE TRANS VISUAL COMPUT GR PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000167743100007 ER PT Journal AU Grandl, R TI Virtual process week in the experimental vehicle build at BMW AG SO ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING LA English DT Article NR 4 SN 0736-5845 PU PERGAMON-ELSEVIER SCIENCE LTD C1 BMW AG, Experimental Vehicle Div, Dept T1 360, D-80788 Munich, Germany BMW AG, Experimental Vehicle Div, Dept T1 360, D-80788 Munich, Germany DE digital mock-up; virtual assembly planning; large data visualization AB For many years CA technologies have been used in different areas of product development. Now visualisation systems are able to support the assembly planning process. During a so- called "virtual process week" at BMW the current state of the assembly planning is estimated by a group of people responsible for the process. Based on the product structure visualisation scenes are created. By using the group function of the visualisation system parts are shown subsequently according to the assembly plan. The participants use eight criteria to assess the operation. All results are documented online in a database. To assure a good view to all people a large screen projection and two projectors are used in parallel in a daylight environment. Due to the amount of CAD data several workstations have to be used and connected individually to the projectors. In the end, statistical evaluations of the database show where operations have to be clarified in more detail or where the geometry of parts will have to be changed because of bottlenecks during the line operation. (C) 2001 Elsevier Science Ltd. All rights reserved. CR BUXTON B, 1997, COMPUT GRAPHICS, V32, P69 JAYARAM S, 1999, IEEE COMPUT GRAPH, V19, P44 LANTZ E, 1997, COMPUT GRAPHICS, V31, P38 RASKAR R, 1998, ANN C SERIES, P179 TC 0 BP 65 EP 71 PG 7 JI Robot. Comput.-Integr. Manuf. PY 2001 PD FEB-APR VL 17 IS 1-2 GA 412UB PI OXFORD RP Grandl R BMW AG, Experimental Vehicle Div, Dept T1 360, D-80788 Munich, Germany J9 ROBOT COMPUT-INTEGR MANUF PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000167571800009 ER PT Journal AU Marion, LS McCain, KW TI Contrasting views of software engineering journals: Author cocitation choices and indexer vocabulary assignments SO JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY LA English DT Article NR 37 SN 1532-2882 PU JOHN WILEY & SONS INC C1 Drexel Univ, Coll Informat Sci & Technol, 3141 Chestnut St, Philadelphia, PA 19104 USA Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA ID RESEARCH-AND-DEVELOPMENT; SCIENCE; DISCIPLINE; BIOTECHNOLOGY; CARTOGRAPHY; TECHNOLOGY; CORE AB We explore the intellectual subject structure and research themes in software engineering through the identification and analysis of a core journal literature. We examine this literature via two expert perspectives: that of the author, who identified significant work by citing it (journal cocitation analysis), and that of the professional indexer, who tags published work with subject terms to facilitate retrieval from a bibliographic database (subject profile analysis). The data sources are SCISEARCH (the on-line version of Science Citation Index), and INSPEC (a database covering software engineering, computer science, and information systems), We use data visualization tools (cluster analysis, multidimensional scaling, and PFNets) to show the "intellectual maps" of software engineering. Cocitation and subject profile analyses demonstrate that software engineering is a distinct interdisciplinary field, valuing practical and applied aspects, and spanning a subject continuum from "programming-in-the- small" to "programming-in-the-large." This continuum mirrors the software development life cycle by taking the operating system or major application from initial programming through project management, implementation, and maintenance, Object orientation is an integral but distinct subject area in software engineering. Key differences are the importance of management and programming: (1) cocitation analysis emphasizes project management and systems development; (2) programming techniques/languages are more influential in subject profiles; (3) cocitation profiles place object-oriented journals separately and centrally while the subject profile analysis locates these journals with the programming/languages group. 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Am. Soc. Inf. Sci. Technol. PY 2001 PD FEB 15 VL 52 IS 4 GA 406HL PI NEW YORK RP Marion LS Drexel Univ, Coll Informat Sci & Technol, 3141 Chestnut St, Philadelphia, PA 19104 USA J9 J AM SOC INF SCI TECHNOL PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA UT ISI:000167208800003 ER PT Journal AU Blackwell, M Nikou, C DiGioia, AM Kanade, T TI An Image Overlay system for medical data visualization SO MEDICAL IMAGE ANALYSIS LA English DT Article NR 10 SN 1361-8415 PU ELSEVIER SCIENCE BV C1 Color Kinet Inc, 17th Floor,50 Milk St, Boston, MA 02109 USA Carnegie Mellon Univ, Ctr Med Robot & Comp Assisted Surg, Pittsburgh, PA 15213 USA Univ Pittsburgh, Med Ctr, Ctr Orthopaed Res, Shadyside Hosp, Pittsburgh, PA USA DE Image Overlay; image guided surgery; augmented reality; 3D visualization AB Image Overlay is a computer display technique which superimposes computer images over the user's direct view of the real world. The images are transformed in real-time so they appear to the user to be an integral part of the surrounding environment. By using Image Overlay with three-dimensional medical images such as CT reconstructions, a surgeon can visualize the data 'in-vivo', exactly positioned within the patient's anatomy, and potentially enhance the surgeon's ability to perform a complex procedure. This paper describes prototype Image Overlay systems and initial experimental results from those systems. (C) 2000 Elsevier Science B.V. All rights reserved. CR BLACKWELL M, 1995, P MED ROB COMP ASS S BUCHOLZ RD, 1997, CVRMED MRCAS 97, P459 DEERING M, 1992, COMPUT GRAPHICS, V26, P195 DRASCIC D, 1991, SPIE P, V1457, P302 GIMSON WEL, 1996, IFFE T MED IMAGING, V15, P129 ISEKI H, 1996, VSMM 96 SCHMANDT C, 1983, COMPUT GRAPHICS, V17, P253 SIMON D, 1997, CVRMED MRCAS 97, P583 SIMON DA, 1995, J IMAGE GUID SURG, V1, P17 TONETTI J, 1997, CAR97 TC 1 BP 67 EP 72 PG 6 JI Med. Image Anal. PY 2000 PD MAR VL 4 IS 1 GA 404LR PI AMSTERDAM RP Blackwell M Color Kinet Inc, 17th Floor,50 Milk St, Boston, MA 02109 USA J9 MED IMAGE ANAL PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000167100200007 ER PT Journal AU Edelson, DC TI Learning-for-use: A framework for the design of technology- supported inquiry activities SO JOURNAL OF RESEARCH IN SCIENCE TEACHING LA English DT Article NR 52 SN 0022-4308 PU JOHN WILEY & SONS INC C1 Northwestern Univ, Sch Educ & Social Policy, 2115 N Campus Dr, Evanston, IL 60208 USA Northwestern Univ, Sch Educ & Social Policy, Evanston, IL 60208 USA ID SCIENCE-EDUCATION; VISUALIZATION; ACHIEVEMENT AB Meeting ambitious content and process (inquiry) standards is an important challenge for science education reform particularly because educators have traditionally seen content and process as competing priorities. However, integrating content and process together in the design of learning activities offers the opportunity to increase students' experience with authentic activities while also achieving deeper content understanding. In this article, I explore technology-supported inquiry learning as an opportunity for integrating content and process learning, using a design framework called the Learning-for-Use model. The Learning-for-Use model is a description of the learning process that can be used to support the design of content-intensive, inquiry-based science learning activities. As an example of a technology-supported inquiry unit designed with the Learning-for-Use model, I describe a curriculum called the Create-a-World Project, in which students engage in open- ended Earth science investigations using WorldWatcher, a geographic visualization and data analysis environment for learners. Drawing on the Learning-for-Use model and the example, I present general guidelines for the design of inquiry activities that support content learning, highlighting opportunities to take advantage of computing technologies. (C) 2001 John Wiley & Sons, Inc. 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Res. Sci. Teach. PY 2001 PD MAR VL 38 IS 3 GA 405LA PI NEW YORK RP Edelson DC Northwestern Univ, Sch Educ & Social Policy, 2115 N Campus Dr, Evanston, IL 60208 USA J9 J RES SCI TEACH PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA UT ISI:000167159800006 ER PT Journal AU Ristau, JP Moon, WM TI Adaptive filtering of random noise in 2-D geophysical data SO GEOPHYSICS LA English DT Article NR 10 SN 0016-8033 PU SOC EXPLORATION GEOPHYSICISTS C1 Univ Manitoba, Dept Geol Sci, 240 Wallace Bldg, Winnipeg, MB R3T 2N2, Canada Univ Manitoba, Dept Geol Sci, Winnipeg, MB R3T 2N2, Canada ID RADAR IMAGES AB Random noise is often a problem in geophysical data visualization because it obscures fine details and complicates identification of image features. Adaptive filters have recently been used to suppress speckle (random) noise in synthetic aperture radar (SAR) images. SAR data are similar to seismic reflection data, both in their data acquisition approach and in their final data processed format. The nature of the random noise associated is also very similar, and adaptive Alters can be applied to reduce random noise in both types of data sets. In this paper several popular adaptive filters-the Lee filter, the Frost filter, and the Kuan filter, which have been used frequently for speckle reduction in SAR data-are tested on Lithoprobe (AGT) deep seismic reflection data and on one set of oil industry shallow seismic reflection data. In addition, a standard band-pass filter, which is common in many seismic data processing packages, is tested with the oil industry test data. Performance of the adaptive filters is also tested on Radarsat SAR data. The random (speckle) noise in both the Lithoprobe and the Radarsat (SAR) data se ts is statistically very similar, and the adaptive filters tested successfully suppressed random noise while minimizing blurring. Among the tested filters the enhanced Lee filter performed best, closely followed by the enhanced Frost filter and the Kuan filter. The background noise in the oil industry seismic data is statistically quite different from the above two data sets: the results obtained were less than satifactory, although they were still encouraging. With the oil industry data, the enhanced Frost and Kuan filters performed better than the enhanced Lee filter. The commonly used band-pass filter successfully removed the background (random) noise, but it also suppressed the reflection events making the final result less desirable. CR DOBRIN MB, 1988, INTRO GEOPHYSICAL PR FROST VS, 1982, IEEE T PATTERN ANAL, V4, P157 KUAN DT, 1985, IEEE T PATTERN ANAL, V7, P165 LEE JS, 1981, COMPUTER GRAPHICS IM, V17, P24 LEE JS, 1981, COMPUTER GRAPHICS IM, V15, P380 LOPES A, 1990, IEEE T GEOSCI REMOTE, V28, P992 RISTAU JP, 1997, 23 ANN SCI ANN M CAN RISTAU JP, 1997, NAT C CAN SOC EXPL G, P218 SHI Z, 1994, P IEEE INT GEOSC REM, P2129 YILMAZ O, 1987, SEISMIC DATA PROCESS TC 0 BP 342 EP 349 PG 8 JI Geophysics PY 2001 PD JAN-FEB VL 66 IS 1 GA 401MW PI TULSA RP Ristau JP Univ Manitoba, Dept Geol Sci, 240 Wallace Bldg, Winnipeg, MB R3T 2N2, Canada J9 GEOPHYSICS PA 8801 S YALE ST, TULSA, OK 74137 USA UT ISI:000166932200041 ER PT Journal AU Bhowmick, SS Madria, S Ng, WK Lim, EP TI Data visualization operators for WHOWEDA SO COMPUTER JOURNAL LA English DT Article NR 23 SN 0010-4620 PU OXFORD UNIV PRESS C1 Nanyang Technol Univ, Sch Appl Sci, Ctr Adv Informat Syst, Singapore 639798, Singapore Nanyang Technol Univ, Sch Appl Sci, Ctr Adv Informat Syst, Singapore 639798, Singapore Univ Missouri, Dept Comp Sci, Rolla, MO 65409 USA AB The effective representation and manipulation of web data is currently an active area of research in databases. In a data warehouse specially designed for web information, web information coupling provides the means to derive useful information from the WWW. Web information is materialized in the form of web tuples and stored in web tables. In this paper, we discuss web data visualization operators such as web nest, web unnest, web coalesce, web expand, web pack, web unpack and wet, sort in the context of our web warehousing system called WHOWEDA (Warehouse of Web Data) to provide users with the flexibility to view sets of web documents in perspectives which may be more meaningful. CR ABITEBOUL S, 1997, J DIGITAL LIB, V1, P68 ABITEBOUL S, 1997, LECT NOTES COMPUT SC, V1186, P1 AROCENA G, 1998, ICDE 98, P24 ATZENI P, 1997, SIGMOD REC, V26, P16 BEERI C, 1990, P EUR C HYP VERS FRA, P67 BHOWMICK S, 1998, P 17 INT C CONC MOD, P92 BHOWMICK S, 1998, P 5 INT C FDN DAT OR BHOWMICK S, 1998, P 9 INT C DAT EXP SY, P647 BHOWMICK S, 1998, P INT WORKSH DAT WAR, P93 BUNEMAN P, 1996, P ACM SIGMOD INT C M FERNANDEZ M, 1997, P WORKSH SEM STRUCT FERNANDEZ M, 1997, SIGMOD RECORD, V26 FIEBIG T, 1997, P WORKSH MAN SEM DAT FLORESCU D, 1998, SIGMOD REC, V27, P59 KONOPNICKI D, 1998, P 21 INT C VER LARG, P54 LAKSHMANAN LVS, 1996, P 6 INT WORKSH RES I, P12 LIU M, 1998, P 17 INT C CONC MOD, P107 LUAH AK, 1999, P INT WORKSH INT DAT, P716 MENDELZON AO, 1996, IEEE P INT C PAR DIS, P80 MIHAILA GA, 1996, THESIS U TORONTO NG WK, 1998, P IEEE INT FORUM RES, P228 PRIYADARSHINI P, 1999, P INT WORKSH INT DAT, P744 QIN FQ, 1999, THESIS NANYANG TECHN TC 0 BP 364 EP 385 PG 22 JI Comput. J. PY 2000 VL 43 IS 5 GA 396EN PI OXFORD RP Bhowmick SS Nanyang Technol Univ, Sch Appl Sci, Ctr Adv Informat Syst, Singapore 639798, Singapore J9 COMPUT J PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND UT ISI:000166623500002 ER PT Journal AU Moltenbrey, K TI Going the extra mile - Goodyear advances tire R&D with high- performance data visualization SO COMPUTER GRAPHICS WORLD LA English DT Article NR 0 SN 0271-4159 PU PENNWELL PUBL CO TC 0 BP 26 EP + PG 4 JI Comput. Graph. World PY 1999 PD SEP VL 22 IS 9 GA 394UV PI NASHUA J9 COMPUT GRAPH WORLD PA 98 SPIT BROOK RD, NASHUA, NH 03062-2801 USA UT ISI:000166542400015 ER PT Journal AU Huh, Y Kim, YR Ryu, WY TI Three-dimensional analysis of migration and staple yarn structure SO TEXTILE RESEARCH JOURNAL LA English DT Article NR 20 SN 0040-5175 PU TEXTILE RESEARCH INST C1 Kyung Hee Univ, Dept Text Engn, Yongin 449701, Kyunggi Do, South Korea Kyung Hee Univ, Dept Text Engn, Yongin 449701, Kyunggi Do, South Korea ID FRICTION SPUN YARNS AB This study describes a visualization and data analysis method for quantitatively analyzing the migration behavior of staple fiber yams. The fiber migration of ring spun yarns with different levels of twist is measured and analyzed, and the results confirm the usefulness of the proposed method. The analysis shows reproducibility and generates new information on yarn structure. Furthermore, the data analysis processes are also able to be efficiently automated. The major migration frequency of the fibers along the yarn axis is obtained by frequency analysis. At the same time, the distributions of fiber position and orientation angle of the constituent individual fiber segments, according to the radial position of the yam cross section, are evaluated. Finally, a parameter, the migration factor, to assess the migration effect as a whole is suggested. The experimental results show that migration reaches a saturation point as yam twist increases. CR ALAGHA MJ, 1994, J TEXT I, V85, P383 ALAGHA MJ, 1994, TEXT RES J, V64, P185 HEARLE JWS, 1969, STRUCTURAL MECH FIBE, V1, PCH2 HEARLE JWS, 1968, TEXT RES J, V38, P780 HEARLE JWS, 1965, TEXT RES J, V35, P329 HEARLE JWS, 1965, TEXT RES J, V35, P693 KIM YR, 1998, J KOR FIBER SOC, V35, P618 KIM YR, 1997, P 4 AS TEXT C, V1, P402 KOMORI T, 1978, TEXT RES J, V48, P309 LORD PR, 1971, TEXT RES J, V41, P778 LOUIS GL, 1985, TEXT RES J, V55, P344 LUNENSCHLOSS J, 1986, INT TEXTILE B YARN F, P7 MORTON WE, 1952, J TEXT I, V43, PT60 MORTON WE, 1956, TEXT RES J, V26, P325 PAN N, 1992, TEXT RES J, V62, P749 RIDING G, 1964, J TEXT I, V55, PT9 SETT SK, 1995, MELLIAND TEXTILBER, V76, PE129 SOELL W, 1990, MELLIAND INT, V80, PE124 TRELOAR LRG, 1965, J TEXT I, V56, PT359 TRELOAR LRG, 1965, J TEXT I, V56, PT381 TC 0 BP 81 EP 90 PG 10 JI Text. Res. J. PY 2001 PD JAN VL 71 IS 1 GA 392CM PI PRINCETON RP Huh Y Kyung Hee Univ, Dept Text Engn, Yongin 449701, Kyunggi Do, South Korea J9 TEXT RES J PA PO BOX 625, PRINCETON, NJ 08540 USA UT ISI:000166393100013 ER PT Journal AU Badal, J Sabadell, FJ Seron, FJ TI An attempt at 3-D imaging of a small domain (Almeria, southern Spain) using a POCS algorithm SO PHYSICS OF THE EARTH AND PLANETARY INTERIORS LA English DT Article NR 32 SN 0031-9201 PU ELSEVIER SCIENCE BV C1 Univ Zaragoza, Pedro Cerbuna 12, E-50009 Zaragoza, Spain Univ Zaragoza, E-50009 Zaragoza, Spain Univ Zaragoza, Dept Comp Sci, CPS, Zaragoza 50015, Spain ID SHEAR-WAVE VELOCITY; RAYLEIGH-WAVES; NEW-ENGLAND; RG WAVES; CRUSTAL STRUCTURE; SHALLOW STRUCTURE; DISPERSION; EARTHQUAKES; EXPLOSIONS; INVERSION AB When applying a methodology for obtaining the 3-D shear-wave velocity structure of a medium from surface wave dispersion data, the problem must be considered with caution since one inverts path-averaged velocities and the use of any inversion method entails some drawbacks such as lack of uniqueness, unwarranted stability and constraints affecting the data. Several imaging techniques aimed at volumetric modeling and the visualization of data can be used to overcome these drawbacks. Actually, some spatial prediction techniques are especially useful for analyzing short-range variability between scattered points. We use here a pathwise reconstruction by means of an algorithm that, from a mathematical viewpoint, can be understood through the application of the orthogonal projection theorem onto convex sets (POCS). In particular, we are interested in exploring the possibilities of a POCS algorithm operating on a very unfavorable case constrained by a lack of available data. In this paper, we have tackled a small-sized problem and we present the results based on ray-path seismic velocities that we have obtained in the case of a sparsely sampled study area like Almeria (southeastem Spain) by way of tomographic images obtained by application of such an algorithm. The main goal of this procedure is the reconstruction of the very shallow Rg-wave velocity structure of a small domain strongly constrained by the data. The method has allowed us to examine the sharply contrasting geology between neighboring geological formations. Although the relationship between lateral changes in Rg-wave dispersion and geologic structure may not be straightforward, we have observed a correlation between the velocity structure of very shallow soils and the local geology at surface. The good agreement between our results and the held observations prove the versatility of the method and the reliability of the imaging. (C) 2000 Elsevier Science B.V. All rights reserved. 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PY 2000 PD NOV VL 122 IS 1-2 SI SI GA 389HB PI AMSTERDAM RP Badal J Univ Zaragoza, Pedro Cerbuna 12, E-50009 Zaragoza, Spain J9 PHYS EARTH PLANET INTERIORS PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000166231600006 ER PT Journal AU Diolaiti, E Bendinelli, O Bonaccini, D Close, L Currie, D Parmeggiani, G TI Analysis of isoplanatic high resolution stellar fields by the StarFinder code SO ASTRONOMY & ASTROPHYSICS SUPPLEMENT SERIES LA English DT Article NR 30 SN 0365-0138 PU E D P SCIENCES C1 Univ Bologna, Dipartimento Astron, Via Ranzani 1, I-40127 Bologna, Italy Univ Bologna, Dipartimento Astron, I-40127 Bologna, Italy European So Observ, D-85748 Garching, Germany Osservatorio Astron Bologna, I-40127 Bologna, Italy DE instrumentation : adaptive optics; method : data analysis; techniques : image processing; techniques : photometric; astrometry; stars : imaging ID ADAPTIVE OPTICS; PHOTOMETRY; PERFORMANCE; IMAGES AB We describe a new code for the deep analysis of stellar fields, designed for Adaptive Optics (AO) Nyquist-sampled images with high and low Strehl ratio. The Point Spread Function (PSF) is extracted directly from the image frame, to take into account the actual structure of the instrumental response and the atmospheric effects. The code is written in IDL language and organized in the form of a self-contained widget-based application, provided with a series of tools for data visualization and analysis. A description of the method and some applications to AO data are presented. 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Astrophys. Suppl. Ser. PY 2000 PD DEC VL 147 IS 2 GA 385UD PI LES ULIS CEDEXA RP Diolaiti E Univ Bologna, Dipartimento Astron, Via Ranzani 1, I-40127 Bologna, Italy J9 ASTRON ASTROPHYS SUPPL SERIES PA 7, AVE DU HOGGAR, PARC D ACTIVITES COURTABOEUF, BP 112, F-91944 LES ULIS CEDEXA, FRANCE UT ISI:000166021400016 ER PT Journal AU Li, WQ Alidaee, B TI Dynamics of local search heuristics for the traveling salesman problem SO IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS LA English DT Article NR 40 SN 1083-4427 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 Univ Michigan, Sch Management, Flint, MI 48502 USA Univ Michigan, Sch Management, Flint, MI 48502 USA Univ Mississippi, Sch Business, University, MS 38677 USA DE combinatorial optimization; data visualization; dynamical complexity; heuristics ID CHAOTIC TIME-SERIES; RECONSTRUCTION; SYSTEMS AB This paper experimentally investigates the dynamical behavior of a search process in a local heuristic search system for a combinatorial optimization problem. Or-opt heuristic algorithm is chosen as the study subject, and the well-known traveling salesman problem (TSP) serves as a problem testbed. This study constructs the search trajectory by using the time-delay method, evaluates the dynamics of the local search system by estimating the correlation dimension for the search trajectory, and illustrates the transition of the local search process from high-dimensional stochastic to low-dimensional chaotic behavior. CR 1988, MANAGE SCI, V34, P363 ABARBANEL HDI, 1996, ANAL OBSERVED CHAOTI ABARBANEL HDI, 1989, INFORMATION THEORETI ABARBANEL HDI, 1993, REV MOD PHYS, V65, P1331 ALLIGOOD KT, 1997, CHAOS INTRO DYNAMICA BOESE KD, 1994, OPER RES LETT, V16, P101 BOESE KD, 1993, TR930015 U CAL COMP BROCK WA, 1986, J ECON THEORY, V40, P168 BROOMHEAD DS, 1986, PHYSICA D, V20, P217 BUZUG T, 1990, EUROPHYS LETT, V13, P605 CENYS A, 1988, PHYS LETT A, V129, P227 DING M, 1993, PHYS REV LETT, V70, P3972 ELMAGHRABY SE, 1971, NAV RES LOG, V18, P339 EVANS JR, 1987, COMPUT OPER RES, V14, P465 FRAZER AM, 1989, IEEE T INFORM THEORY, V35, P245 FRAZER AM, 1986, PHYS REV A, V33, P1134 GALLAGER RG, 1968, INFORMATION THEORY R GENT P, 1993, EMPIRICAL ANAL SEARC GRASSBERGER P, 1983, PHYS REV LETT, V50, P5 GROVER LK, 1992, OPER RES LETT, V12, P235 HILBORN RC, 1994, CHAOS NONLINEAR DYNA HOLZFUSS J, 1986, DIMENSIONS ENTROPIES, P114 KENNEL MB, 1992, PHYS REV A, V45, P3403 KURAMOTO Y, 1984, CHAOS STAT METHODS LIEBERT W, 1989, PHYS LETT A, V142, P107 OR I, 1976, THESIS NW U EVANSTON OTT E, 1994, COPING CHAOS ANAL CH PACKARD NH, 1980, PHYS REV LETT, V45, P712 PAPADIMITRIOU CH, 1982, COMBINATORIAL OPTIMI PAPADIMITRIOU CH, 1977, SIAM J COMPUT, V6, P76 PARKER TS, 1990, PRACTICAL NUMERICAL RAMSEY JB, 1987, 8720 NEW YORK U C V REEVES CR, 1993, MODERN HEURISTIC TEC SAUER T, 1991, J STAT PHYS, V65, P579 SHANNON CE, 1949, MATH THEORY COMMUNIC SHAW RS, 1985, DRIPPING FAUCET MODE TAKEN F, 1985, DYNAMICAL SYSTEMS BI, P99 TAKENS F, 1981, LECT NOTES MATH, V898, P366 TONG H, 1980, J ROY STAT SOC B MET, V42, P245 ZANAKIS SH, 1989, EUR J OPER RES, V43, P88 TC 0 BP 173 EP 184 PG 12 JI IEEE Trans. Syst. Man Cybern. Paart A-Syst. Hum. PY 2002 PD MAR VL 32 IS 2 GA 580ZF PI NEW YORK RP Li WQ Univ Michigan, Sch Management, Flint, MI 48502 USA J9 IEEE TRANS SYST MAN CYBERN A PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000177266100001 ER PT Journal AU Iyer, A Kapoor, SG DeVor, RE TI CAD data visualization for machining simulation using the STEP standard SO JOURNAL OF MANUFACTURING SYSTEMS LA English DT Article NR 13 SN 0278-6125 PU SOC MANUFACTURING ENGINEERS C1 Univ Illinois, Dept Mech & Ind Engn, 1206 W Green St, Urbana, IL 61801 USA Univ Illinois, Dept Mech & Ind Engn, Urbana, IL 61801 USA DE neutral file; STEP product data exchange; AP203; CAD; CAM; visualization ID MODEL AB There is a need for the integration of computer-aided design (CAD) information with manufacturing models, such as machining process simulation models, upstream with concurrent engineering activities. Because it is often difficult to interface various CAD formats with such applications, there is a need to standardize input file formats, which then can be used by any module that has the appropriate interface. The STEP standard is a very useful standard for the neutral file format specification. The present work describes in detail the building of a STEP parser for the Application Protocol for Configuration Controlled Design of 3-D mechanical parts (AP203). Such a STEP parser is complemented with a visualization front end in the form of a Java applet, which is used to display the STEP entities in 3-D within a web browser over the Internet. The current work outlines a generic framework for a STEP parser module. Its use in the design and development of CAD-based process engineering applications is illustrated by example of a CAD interface to a machining process simulation program, which requires the workpiece CAD information, namely the surface geometry, to be machined to determine chip loads and hence cutting forces. The applicability of the STEP parser in the building of a CAD database cataloging system is also discussed. CR CHANG TC, 1990, EXPERT PROCESS PLANN CLARK A, 1995, 3 S SOL MOD APPL NEW GILBERT M, 1994, N601 ISO TC184SC4AST GU F, 1997, J MANUF SCI E-T ASME, V119, P467 HELPENSTEIN HJ, 1993, CAD GEOMETRY DATA EX KROSZYNSKI UI, 1989, IEEE COMPUT GRAPH, V9, P56 LIU TH, 1995, ASME T, V8, P33 MAROPOULOS PG, 1995, COMPUTER INTEGRATED, V8, P12 QIAO L, 1993, COMPUT IND, P11 SCHENK DA, 1994, INFORMATION MODELING SHAH JJ, 1991, COMPUT AIDED DESIGN, V23, P282 ZHANG S, 1995, COMPUTERS ENG ZHANG S, P 1995 DAT S NEW YOR, P687 TC 0 BP 198 EP 209 PG 12 JI J. Manuf. Syst. PY 2001 VL 20 IS 3 GA 578DK PI DEARBORN RP Iyer A Univ Illinois, Dept Mech & Ind Engn, 1206 W Green St, Urbana, IL 61801 USA J9 J MANUF SYST PA ONE SME DRIVE, PO BOX 930, DEARBORN, MI 48121-0930 USA UT ISI:000177102000005 ER PT Journal AU Patterson, DA Basham, RE TI A data visualization procedure for the evaluation of group treatment outcomes across units of analysis SO SMALL GROUP RESEARCH LA English DT Article NR 32 SN 1046-4964 PU SAGE PUBLICATIONS INC C1 Univ Tennessee, Coll Social Work, Knoxville, TN 37996 USA Univ Tennessee, Coll Social Work, Knoxville, TN 37996 USA ID EXPERIENCES; DESIGNS AB This study presents a novel method for the collection and graphical representation of group evaluation data that enables the simultaneous display of outcomes across units of analysis. This method affords the researcher/practitioner improved options for simultaneously evaluating and comparing individual and group change over time. Data for this study are drawn from 10 experiential, group psychotherapy training groups that met on a weekly basis. A total of 185, second-year graduate social work students evaluated their satisfaction with their personal and group experience across eight dimensions. A multiple page Excel spreadsheet was developed for the collection, computation, and visual representation of scores from the evaluation instrument. Three-dimensional surface plots are used here to simultaneously represent individual and group outcomes. Surface plots provide a visual display of the topography of group and individual change overtime. This data visualization method utilizes readily available technology to display group outcomes at multiple levels of analysis. CR BECK AP, 2000, PROCESS GROUP PSYCHO BLOOM M, 1999, EVALUATING PRACTICE DELUCIAWAACK JL, 1997, J SPECIALISTS GROUP, V22, P277 GLISSON C, 1986, RES SOCIAL GROUP WOR, V9, P15 HABER RB, 1988, P ACM COMPUTER GRAPH, V22, P89 HILL CE, 1990, COUNS PSYCHOL, V18, P126 JAYARATNE S, 1977, SOC WORK RES ABSTR, V13, P35 KAZI MAF, 1997, RES SOCIAL WORK PRAC, V7, P311 MACKENZIE R, 1995, EFFECTIVE USE GROUP MARSH CL, 1931, MENT HYG, V15, P328 MATTAINI MA, 1993, MORE 1000 WORDS GRAP MATTILA M, 1996, INT J HUM FACTOR MAN, V6, P1 MONETTE DR, 1990, APPL SOCIAL RES TOOL NUGENT WR, 1996, J APPL BEHAV SCI, V32, P209 OBRIEN WH, 1999, RES SOCIAL WORK PRAC, V9, P608 ORME JG, 1991, SOC SERV REV, V65, P468 PATTERSON DA, 2000, PERSONAL COMPUTER AP PRATT J, 1945, SOCIOMETRY, V8, P323 REID KE, 1997, SOCIAL WORK PRACTICE ROSE SD, 1989, WORKING ADULTS GROUP RUBIN A, 1997, RES METHODS SOCIAL W SCHNEIDER B, 1990, ORG CLIMATE CULTURE SMITH KK, 1983, PERSONALITY SOCIAL P, V9, P65 SMOKOWSKI PR, 1999, RES SOCIAL WORK PRAC, V9, P555 SPITZ HI, 1996, GROUP PSYCHOTHERAPY THYER BA, 1992, RES SOCIAL WORK PRAC, V2, P99 TOLMAN RM, 1994, RES SOCIAL WORK PRAC, V4, P142 TOSELAND RW, 1995, INTRO GROUP WORK PRA TRIPODI T, 1994, PRIMER SINGLE SUBJEC TUFTE ER, 1983, VISUAL DISPLAY QUANT YALOM ID, 1995, THEORY PRACTICE GROU YU CH, 1995, APPL MULTIVARIATE VI TC 0 BP 209 EP 232 PG 24 JI Small Group Res. PY 2002 PD APR VL 33 IS 2 GA 576KV PI THOUSAND OAKS RP Patterson DA Univ Tennessee, Coll Social Work, Knoxville, TN 37996 USA J9 SMALL GROUP RES PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 USA UT ISI:000177004400003 ER PT Journal AU Keim, DA Hao, MC Dayal, U TI Hierarchical pixel bar charts SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS LA English DT Article NR 24 SN 1077-2626 PU IEEE COMPUTER SOC C1 AT&T Corp, Res, Florham Pk, NJ USA AT&T Corp, Res, Florham Pk, NJ USA Univ Constance, D-7750 Constance, Germany Hewlett Packard Res Labs, Palo Alto, CA USA DE information visualization; multidimensional data visualization; visual data exploration and data mining; very large multiattributes data sets; hierarchical visualization ID VISUALIZATION AB Simple presentation graphics are intuitive and easy-to-use, but only show highly aggregated data. Bar charts, for example, only show a rather small number of data values and x-y-plots often have a high degree of overlap. Presentation techniques are often chosen depending on the considered data type-bar charts, for example, are used for categorical data and x-y plots are used for numerical data. In this article, we propose a combination of traditional bar charts and x-y-plots, which allows the visualization of large amounts of data with categorical and numerical data. The categorical data dimensions are used for the partitioning into the bars and the numerical data dimensions are used for the ordering arrangement within the bars. The basic idea is to use the pixels within the bars to present the detailed information of the data records. Our so-called pixel bar charts retain the intuitiveness of traditional bar charts while applying the principle of x-y charts within the bars. In many applications, a natural hierarchy is defined on the categorical data dimensions such as time, region, or product type. In hierarchical pixel bar charts, the hierarchy is exploited to split the bars for selected portions of the hierarchy. Our application to a number of real-world e-business and Web services data sets shows the wide applicability and usefulness of our new idea. CR AHLBERG C, 1992, ACM CHI INTL C HUM F, P619 ANKERST M, 1996, P IEEE S VIS 96 ANUPAM V, 1995, P INT S INF VIS ATL, P82 BATTISTA GD, 1999, GRAPH DRAWING ALGORI BEDDOW J, 1990, P VISUALIZATION 90, P238 BUJA A, 1991, P VISUALIZATION 91, P156 CHIMERA R, 1992, P ACM SIGCHI 92 C HU, P293 EICK SG, 1999, VISUALIZING MULTIDIM HAO M, 1999, P IEEE S INF VIS 99, P124 HOFMANN H, 2000, METRIKA, V51, P11 INSELBERG A, 1990, P VISUALIZATION 90, P361 INSELBERG A, 1985, VISUAL COMPUT, V1, P69 KEIM DA, 1994, COMPUTER GRAPHIC SEP, P40 KEIM DA, 2000, IEEE T VIS COMPUT GR, V6, P59 KEIM DA, 2002, INFORMATION VISUALIZ, V1 KEIM DA, 2001, P IEEE S INF VIS 200 LAMPING J, 1994, ACM S US INT SOFTW T, P13 LAMPING J, 1995, P ACM SIGCHI C HUM F, P401 LEBLANC J, 1990, P VISUALIZATION 90, P230 PICKETT RM, 1988, P IEEE C SYST MAN CY, P514 RAO R, 1994, P ACM SIGCHI C HUM F, P318 ROBERTSON DE, 1991, BIOFACTORS, V3, P1 SHNEIDERMAN B, 1992, ACM T GRAPHIC, V11, P92 SHNEIDERMAN B, 1996, P VIS LANG TC 0 BP 255 EP 269 PG 15 JI IEEE Trans. Vis. Comput. Graph. PY 2002 PD JUL-SEP VL 8 IS 3 GA 577GA PI LOS ALAMITOS RP Keim DA AT&T Corp, Res, Florham Pk, NJ USA J9 IEEE TRANS VISUAL COMPUT GR PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000177052100005 ER PT Journal AU Bellazzi, R Larizza, C Montani, S Riva, A Stefanelli, M d'Annunzio, G Lorini, R Gomez, EJ Hernando, E Brugues, E Cermeno, J Corcoy, R de Leiva, A Cobelli, C Nucci, G Del Prato, S Maran, A Kilkki, E Tuominen, J TI A telemedicine support for diabetes management: the T-IDDM project SO COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE LA English DT Article NR 35 SN 0169-2607 PU ELSEVIER SCI IRELAND LTD C1 Univ Pavia, Dipartimento Informat & Sistemist, Via Ferrata 1, I-27100 Pavia, Italy Univ Pavia, Dipartimento Informat & Sistemist, I-27100 Pavia, Italy Policlin San Matteo, IRCCS, I-27100 Pavia, Italy Fdn Gaslini, Genoa, Italy Univ Politecn Madrid, E-28660 Madrid, Spain Fdn Diabem, Barcelona, Spain Padova Univ Hosp, Padua, Italy Helsinki Univ Hosp, FIN-00170 Helsinki, Finland DE telemedicine; diabetes management; demonstration study; clinical outcomes ID GLUCOSE CONTROL; CARE; SYSTEM; TRIAL AB In the context of the EU funded Telematic Management of Insulin-Dependent Diabetes Mellitus (T-IDDM) project, we have designed, developed and evaluated a telemedicine system for insulin dependent diabetic patients management. The system relies on the integration of two modules, a Patient Unit (PU) and a Medical Unit (MU), able to communicate over the Internet and the Public Switched Telephone Network. Using the PU, patients are allowed to automatically download their monitoring data from the blood glucose monitoring device, and to send them to the hospital data-base; moreover, they are supported in their every day self monitoring activity. The MU provides physicians with a set of tools for data visualization, data analysis and decision support, and allows them to send messages and/or therapeutic advice to the patients. The T-IDDM service has been evaluated through the application of a formal methodology, and has been used by European patients and physicians for about 18 months. The results obtained during the project demonstration, even if obtained on a pilot study of 12 subjects, show the feasibility of the T-IDDM telemedicine service, and seem to substantiate the hypothesis that the use of the system could present an advantage in the management of insulin dependent diabetic patients, by, improving communications and, potentially, clinical outcomes. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved. CR *DIAB CONTR COMPL, 1993, NEW ENGL J MED, V327, P977 *T IDDM CONS, US NEEDS T IDDM DEL AHRING KK, 1992, DIABETES CARE, V15, P971 ALBISSER AM, 1996, MED INFORM, V21, P297 AMBROSO C, T IDDM FUNCTIONAL SP BELLAZZI R, 2000, DIABETES NUTR METAB, V13, P249 BELLAZZI R, 1998, INTELL DATA ANAL, V2, P97 BELLAZZI R, MED WORKSTATION FINA BELLAZZI R, 1995, P HT 95, P271 BIERMANN E, P MIE 2000, P327 BILLIARD A, 1991, DIABETES CARE, V14, P130 COBELLI C, 1998, DIABETES NUTR METAB, V11, P78 EDMONDS M, 1998, INT J MED INFORM, V52, P117 GOMEZ EJ, 1992, 14 ANN INT C IEEE EN, P1238 GOMEZ EJ, 1996, MED INFORM, V21, P283 GOMEZ EJ, 1992, MEDICON 92, P915 HERNANDO E, PATIENT UNIT FINAL R HOVORKA R, 1996, COMPUT METH PROG BIO, V32, P303 KOLODNER JL, 1993, CASE BASED REASONING LEHMANN ED, 1997, DIABETES NUTR METAB, V10, P45 LIESENFELD B, 1998, DIABETES NUTR METAB, V11, P63 MARRERO DG, 1989, DIABETES CARE, V12, P345 MARRERO DG, 1995, DIABETES EDUCATOR, V21, P313 MONTANI S, DEMONSTRATION PHASE MONTANI S, 2000, INT J MED INFORM, V58, P243 MONTANI S, 1999, INT J MED INFORM, V53, P61 MORRISH NJ, 1989, DIABETIC MED, V6, P591 PELLEGRINI S, FINAL EXPLOITATION P PIETTE JD, 2000, MED CARE, V38, P218 RAMONI M, 1997, ADV INTELLIGENT DATA, P537 RIVA A, 1996, COMPUT NETWORKS ISDN, V28, P953 ROSENFALCK AM, 1993, DIABETES METAB, V19, P25 SHULTZ EK, 1992, ANN NY ACAD SCI, V670, P141 THOMPSON DM, 1999, CAN MED ASSOC J, V161, P959 WHITLOCK WL, 2000, MIL MED, V165, P579 TC 0 BP 147 EP 161 PG 15 JI Comput. Meth. Programs Biomed. PY 2002 PD AUG VL 69 IS 2 SI SI GA 577FG PI CLARE RP Bellazzi R Univ Pavia, Dipartimento Informat & Sistemist, Via Ferrata 1, I-27100 Pavia, Italy J9 COMPUT METHOD PROGRAM BIOMED PA CUSTOMER RELATIONS MANAGER, BAY 15, SHANNON INDUSTRIAL ESTATE CO, CLARE, IRELAND UT ISI:000177050400006 ER PT Journal AU Peng, RD Hengartner, NW TI Quantitative analysis of literary styles SO AMERICAN STATISTICIAN LA English DT Article NR 18 SN 0003-1305 PU AMER STATISTICAL ASSOC C1 Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA Los Alamos Natl Lab, Los Alamos, NM 87544 USA DE authorship; canonical discriminant analysis; data visualization; function words; high-dimensional data; principal component analysis AB Writers are often viewed as having an inherent style that can serve as a literary fingerprint. By quantifying relevant features related to literary style, one may hope to classify written works and even attribute authorship to newly discovered texts. Beyond its intrinsic interest, the study of literary styles presents the opportunity to introduce and motivate many standard multivariate statistical techniques. Today the statistical analysis of literary styles is made much simpler by the wealth of real data readily available from the Internet. This article presents an overview and brief history of the analysis of literary styles. In addition we use canonical discriminant analyis and principal component analysis to identify structure in the data and distinguish authorship. CR BRINEGAR C, 1963, J AM STAT ASSOC, V58, P85 GIFI A, 1990, NONLINEAR MULTIVARIA HOLMES DI, 1985, J ROY STAT SOC A GEN, V148, P328 HOLMES DI, 1992, J ROY STAT SOC A STA, V155, P91 IHAKA R, 1996, J COMPUTATIONAL GRAP, V5, P299 JOHNSON RA, 1982, APPL MULTIVARIATE ST JOLLIFE IT, 1986, PRINCIPAL COMPONENT KLECKA WR, 1980, DISCRIMINANT ANAL LACHENBRUCH PA, 1975, DISCRIMINANT ANAL LACHENBRUCH PA, 1968, TECHNOMETRICS, V10, P1 MORTON AQ, 1965, J ROYAL STATISTICA A, V128, P169 MOSTELLER F, 1964, APPL BAYESIAN CLASSI MOSTELLER F, 1963, J AM STAT ASSOC, V58, P275 SARNDAL CE, 1967, APPL STAT, V16, P251 THISTED R, 1987, BIOMETRIKA, V74, P445 WILLIAMS CB, 1975, BIOMETRIKA, V62, P207 WILLIAMS CB, 1956, BIOMETRIKA, V43, P248 WILLIAMS CB, 1940, BIOMETRIKA, V31, P356 TC 0 BP 175 EP 185 PG 11 JI Am. Stat. PY 2002 PD AUG VL 56 IS 3 GA 577JP PI ALEXANDRIA RP Peng RD Univ Calif Los Angeles, Dept Stat, Los Angeles, CA 90095 USA J9 AMER STATIST PA 1429 DUKE ST, ALEXANDRIA, VA 22314 USA UT ISI:000177058000003 ER PT Journal AU Quek, F Bryll, R Kirbas, C Arslan, H McNeill, D TI A multimedia system for temporally situated perceptual psycholinguistic analysis SO MULTIMEDIA TOOLS AND APPLICATIONS LA English DT Article NR 20 SN 1380-7501 PU KLUWER ACADEMIC PUBL C1 Wright State Univ, Dept Comp Sci & Engn, Vis Interfaces & Syst Lab, Dayton, OH 45435 USA Wright State Univ, Dept Comp Sci & Engn, Vis Interfaces & Syst Lab, Dayton, OH 45435 USA Univ Chicago, Dept Psychol, Chicago, IL 60637 USA Univ Chicago, Dept Linguist, Chicago, IL 60637 USA DE multimedia data visualization; temporal analysis; user interface; multiple; linked representation; gesture coding; gesture; speech and gaze analysis ID ATTENTION; DISCOURSE AB Perceptual analysis of video (analysis by unaided ear and eye) plays an important role in such disciplines as psychology, psycholinguistics, linguistics, anthropology, and neurology. In the specific domain of psycholinguistic analysis of gesture and speech, researchers micro-analyze videos of subjects using a high quality video cassette recorder that has a digital freeze capability down to the specific frame. Such analyses are very labor intensive and slow. We present a multimedia system for perceptual analysis of video data using a multiple, dynamically linked representation model. The system components are linked through a time portal with a current time focus. The system provides mechanisms to analyze overlapping hierarchical interpretations of the discourse, and integrates visual gesture analysis, speech analysis, video gaze analysis, and text transcription into a coordinated whole. The various interaction components facilitate accurate multi-point access to the data. While this system is currently used to analyze gesture, speech and gaze in human discourse, the system described may be applied to any other field where careful analysis of temporal synchronies in video is important. CR ANSARI R, 1999, 1999 WORKSH NONL SIG BOBICK AF, 251 MIT MED LAB PERC BOBICK AF, 1993, P 27 ANN AS C SIGN S BRENNAN SE, 1995, LANG COGNITIVE PROC, V10, P137 DELIN J, 1995, LANG COGNITIVE PROC, V10, P97 GORDON PC, 1993, COGNITIVE SCI, V17, P311 KENDON A, 1986, BIOL FDN GESTURES MO, P23 KOZMA RB, 1994, ED TECHNOLOGY RES DE, V42, P1 KOZMA RB, 1996, INT PERSPECTIVES DES MAYHEW D, 1992, PRINCIPLES GUIDELINE MCNEILL D, 1992, HAND MIND WHAT GESTU NAKATANI CH, 1995, TR2195 HARV U CTR RE NOBE S, 2000, LANGUAGE GESTURE NOBE S, 1996, THESIS U CHICAGO QUEK F, 1997, 60053353, US, APPL QUEK F, 1998, 9815063, US, APPL QUEK F, 1999, ICCV 99 WKSP RATFG R, P64 QUEK F, 2000, UNPUB IEEE C CVPR HI QUEK F, 2001, VISLAB0100 WRIGHT ST YEO BL, 1997, COMMUN ACM, V40, P43 TC 0 BP 91 EP 114 PG 24 JI Multimed. Tools Appl. PY 2002 PD NOV VL 18 IS 2 GA 572ZB PI DORDRECHT RP Quek F Wright State Univ, Dept Comp Sci & Engn, Vis Interfaces & Syst Lab, Dayton, OH 45435 USA J9 MULTIMED TOOLS APPL PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS UT ISI:000176803900001 ER PT Journal AU Corkery, JJ TI Visualization and data analysis with VIDA. SO ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY LA English DT Meeting Abstract NR 0 SN 0065-7727 PU AMER CHEMICAL SOC C1 OpenEye Sci Software, Cambridge, MA 02139 USA TC 0 BP 57-CINF PG 1 JI Abstr. Pap. Am. Chem. Soc. PY 2002 PD APR 7 VL 223 PN 1 GA 564CD PI WASHINGTON RP OpenEye Sci Software, Cambridge, MA 02139 USA J9 ABSTR PAP AMER CHEM SOC PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA UT ISI:000176296701999 ER PT Journal AU Davis, W Roney, P Carroll, T Gibney, T Mastrovito, D TI The use of MDSplus on NSTX at PPPL SO FUSION ENGINEERING AND DESIGN LA English DT Article NR 11 SN 0920-3796 PU ELSEVIER SCIENCE SA C1 Princeton Plasma Phys Lab, POB 451, Princeton, NJ 08543 USA Princeton Plasma Phys Lab, Princeton, NJ 08543 USA DE data acquisition; data visualization; data management; MDSplus; NSTX AB The MDSplus data acquisition system has been used successfully since the 1999 startup of NSTX for control, data acquisition and analysis for diagnostic subsystems. For each plasma "shot" on NSTX about 75 MBs of data is acquired and loaded into MDSplus hierarchical data structures in 2-3 min. Physicists adapted to the MDSplus software tools with no real difficulty. Some locally developed tools are described. The support from the developers at MIT was timely and insightful. The use of MDSplus has resulted in significant cost savings for NSTX. (C) 2002 Elsevier Science B.V. All rights reserved. CR *FAS, FAS US GUID *IDL, IDL INT DAT LANG *MDSW, IDL WIDG PLOTT MDSPL *NSTX, WEB BAS TOOLS NSTX GATES DA, 1999, IEEE T NUCL SCI KAYE SM, 1999, FUSION TECHNOL, V36, P16 MANDUCHI G, 2000, FUSION ENG DES, V48, P163 MASTROVITO D, 2001, IN PRESS FUSION ENG SCHACHTER J, 2000, FUSION ENG DES, V48, P91 SICHTA P, 1999, 18 S FUS ENG ALB NM STILLERMAN JA, 1997, REV SCI INSTRUM 2, V68, P939 TC 0 BP 247 EP 251 PG 5 JI Fusion Eng. Des. PY 2002 PD JUN VL 60 IS 3 GA 566XB PI LAUSANNE RP Davis W Princeton Plasma Phys Lab, POB 451, Princeton, NJ 08543 USA J9 FUSION ENG DES PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND UT ISI:000176452600005 ER PT Journal AU Ebert, DS Favre, JM Peikert, R TI Data visualization - Introduction SO COMPUTERS & GRAPHICS-UK LA English DT Editorial Material NR 0 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Purdue Univ, Sch Elect & Comp Engn, 1285 EE Bldg, W Lafayette, IN 47907 USA Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA Swiss Ctr Sci Comp, Manno, Switzerland Swiss Fed Inst Technol, Inst Comp Sci, Zurich, Switzerland TC 0 BP 207 EP 208 PG 2 JI Comput. Graph.-UK PY 2002 PD APR VL 26 IS 2 GA 566GR PI OXFORD RP Ebert DS Purdue Univ, Sch Elect & Comp Engn, 1285 EE Bldg, W Lafayette, IN 47907 USA J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000176419000001 ER PT Journal AU Liu, ZY Finkelstein, A Li, K TI Improving progressive view-dependent isosurface propagation SO COMPUTERS & GRAPHICS-UK LA English DT Article NR 13 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Princeton Univ, Dept Comp Sci, 35 Olden St, Princeton, NJ 08544 USA Princeton Univ, Dept Comp Sci, Princeton, NJ 08544 USA DE data visualization; isosurface extraction; view dependent; level of detail; ray casting AB Recently, we proposed a new isosurface extraction algorithm that extracts portions of the isosurface in a view-dependent manner by ray casting and propagation. The algorithm casts rays through a volume to find visible active cells as seeds and then propagates their polygonal isosurface into the neighboring cells. Small pieces of the isosurface are generated by distance-limited propagation and joined together to form the final surface. This paper presents our evaluation of several design choices of the algorithm. We have implemented these design choices and showed that by making right design decisions, we can substantially reduce the time to obtain most (such as 99.9%) of the isosurface. (C) 2002 Elsevier Science Ltd. All rights reserved. CR BAJAJ CL, 1997, P 13 ANN ACM S COMP, P212 CIGNONI P, 1997, IEEE T VIS COMPUT GR, V3, P158 ITOH T, 1995, IEEE T VIS COMPUT GR, V1, P319 LI K, 2000, IEEE COMPUT GRAPH, V20, P671 LIU Z, 2000, P JOINT EUR IEEE TCV, P223 LIVNAT Y, 1996, IEEE T VIS COMPUT GR, V2, P73 LIVNAT Y, 1998, P IEEE 1998 C VIS, P175 LIVNAT Y, 1999, THESIS U UTAH LIVNAT Y, UNPUB VIEW DEPENDENT LORENSEN WE, 1987, P ACM SIGGRAPH 87, P163 MOLLER T, 1997, J GRAPHIC TOOLS, V2, P21 PARKER S, 1998, P IEEE VIS 98, P233 WILHELMS J, 1992, ACM T GRAPHIC, V11, P201 TC 0 BP 209 EP 218 PG 10 JI Comput. Graph.-UK PY 2002 PD APR VL 26 IS 2 GA 566GR PI OXFORD RP Liu ZY Princeton Univ, Dept Comp Sci, 35 Olden St, Princeton, NJ 08544 USA J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000176419000002 ER PT Journal AU Djurcilov, S Kim, K Lermusiaux, P Pang, A TI Visualizing scalar volumetric data with uncertainty SO COMPUTERS & GRAPHICS-UK LA English DT Article NR 18 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Univ Calif Santa Cruz, Dept Comp Sci, 4186 Glenwood Dr, Scotts Valley, CA 95066 USA Univ Calif Santa Cruz, Dept Comp Sci, Scotts Valley, CA 95066 USA Harvard Univ, Div Engn & Appl Sci, Cambridge, MA 02138 USA DE uncertainty representations; ocean modeling; volume rendering; transfer function; speckle; noise; textures AB Increasingly, more importance is placed on the uncertainty information of data being displayed. This paper focuses on techniques for visualizing 3D scalar data sets with corresponding uncertainty information at each point which is also represented as a scalar value. In Djurcilov (in: D. Ebert, J.M. Favre, R. Peikert (Eds.), Data Visualization 2001, Springer, Berlin, 2001), we presented two general methods (inline DVR approach and a post-processing approach) for carrying out this task. The first method involves incorporating the uncertainty information directly into the volume rendering equation. The second method involves post-processing information of volume rendered images to composite uncertainty information. Here, we provide further improvements to those techniques primarily by showing the depth cues for the uncertainty, and also better transfer function selections. (C) 2002 Elsevier Science Ltd. All rights reserved. CR BEARD MK, 1991, 9126 NAT CTR GEOGR I CEDILNIK A, 2000, P VIS 00, P77 DJURCILOV S, 2001, DATA VISUALIZATION 2, P243 DJURCILOV S, 2000, IEEE COMPUT GRAPH, V20, P52 GOODCHILD M, 1994, VISUALIZATION GEOGRA, P141 INTERRANTE V, 2000, IEEE COMPUT GRAPH, V20, P6 KLIR G, 1999, UNCERTAINTY BASED IN LERMUSIAUX PE, 1999, MON WEATHER REV, V127, P1408 LKINDLMANN G, 1998, IEEE S VOL VIS, P79 MOELLERING H, AM CARTOGRAPHER, V15, P11 MOWRER HT, 2000, QUANTIFYING SPATIAL PANG A, 2001, WORKSH INT GEOSP INF PANG AT, 1997, VISUAL COMPUT, V13, P370 ROBINSON AR, 1996, EARTH-SCI REV, V40, P3 TARANTOLA A, 1987, INVERSE PROBLEM THEO TAYLOR BN, 1993, 1297 NAT I STAND TEC WITTENBRINK CM, 1996, IEEE T VIS COMPUT GR, V2, P266 WITTENBRINK CM, 1995, IEEE VISUALIZATIN 95, P77 TC 0 BP 239 EP 248 PG 10 JI Comput. Graph.-UK PY 2002 PD APR VL 26 IS 2 GA 566GR PI OXFORD RP Djurcilov S Univ Calif Santa Cruz, Dept Comp Sci, 4186 Glenwood Dr, Scotts Valley, CA 95066 USA J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000176419000005 ER PT Journal AU Pastizzo, MJ Erbacher, RF Feldman, LB TI Multidimensional data visualization SO BEHAVIOR RESEARCH METHODS INSTRUMENTS & COMPUTERS LA English DT Article NR 12 SN 0743-3808 PU PSYCHONOMIC SOC INC C1 Haskins Labs Inc, 270 Crown St, New Haven, CT 06511 USA Haskins Labs Inc, New Haven, CT 06511 USA SUNY Albany, Albany, NY 12222 USA ID MULTIVARIATE DATA; EXPLORATORY ANALYSIS; DYNAMIC GRAPHICS; PSYCHOLOGY AB Historically, data visualization has been limited primarily to two dimensions (e.g., histograms or scatter plots). Available software packages (e.g., Data Desk 6.1, MatLab 6. 1, SASJMP 4.04, SPSS 10.0) are capable of producing three-dimensional scatter plots with (varying degrees of) user interactivity. We constructed our own data visualization application with the Visualization Toolkit (Schroeder, Martin, & Lorensen, 1998) and Tcl/Tk to display multivariate data through the application of glyphs (Ware, 2000). a glyph is a visual object onto which many data parameters may be mapped, each with a different visual attribute (e.g., size or color). We used our Multi-Dimensional Data Viewer to explore data from several psycholinguistic experiments. The graphical interface provides flexibility when users dynamically explore the multidimensional image rendered from raw experimental data. We highlight advantages of multidimensional data visualization and consider some potential limitations. CR CASTELLAN NJ, 1991, BEHAV RES METH INSTR, V23, P106 FELDMAN LB, 2001, I MORPH WORKSH NIJM LOFTUS GR, 1993, BEHAV RES METH INSTR, V25, P250 MARCHAK FM, 1994, BEHAV RES METH INSTR, V26, P177 MARCHAK FM, 1992, BEHAV RES METH INSTR, V24, P253 MARCHAK FM, 1991, BEHAV RES METH INSTR, V23, P296 MARCHAK FM, 1990, BEHAV RES METH INSTR, V22, P176 PASTIZZO MJ, 2002, J EXP PSYCHOL LEARN, V28, P244 SCHROEDER W, 1998, VISUALIZATION TOOLKI SMITH AF, 1993, HDB DATA ANAL BEHAV, P349 WAINER H, 1993, HDB DATA ANAL BEHAV, P391 WARE C, 2000, INFORMATION VISUALIZ TC 0 BP 158 EP 162 PG 5 JI Behav. Res. Methods Instr. Comput. PY 2002 PD MAY VL 34 IS 2 GA 568AL PI AUSTIN RP Pastizzo MJ Haskins Labs Inc, 270 Crown St, New Haven, CT 06511 USA J9 BEHAV RES METHOD INSTRUM COMP PA 1710 FORTVIEW RD, AUSTIN, TX 78704 USA UT ISI:000176520200004 ER PT Book in series AU Papiernik, DK Nanda, D Cassada, RO Morris, WH TI Data warehouse strategy to enable performance analysis SO TRANSPORTATION DATA, STATISTICS, AND INFORMATION TECHNOLOGY LA English DT Article NR 8 SN 0361-1981 PU TRANSPORTATION RESEARCH BOARD NATL RESEARCH COUNCIL C1 TransCore, 8614 Westwood Ctr Dr,Suite 310, Vienna, VA 22182 USA TransCore, Vienna, VA 22182 USA TransCore, Richmond, VA 23219 USA Virginia Dept Transportat, Programming & Scheduling Div, Richmond, VA 23219 USA AB The Virginia Department of Transportation (VDOT) has engaged to implement an enterprise data warehouse as part of a strategic investment in its information technology (IT) infrastructure. Data warehousing provides an information architecture that serves as the enterprisewide source of data for performance analysis and organizational reporting. To assist VDOT in achieving its strategic outcome area objectives, a programming and scheduling (P&S) data mart Is being developed to track preconstruction project activities. This data mart and subsequent data marts function as departmental decision support platforms, enabling VDOT's operating divisions to perform their own enhanced analytical processing, visualization, and data mining for more informed business decision capabilities. Presented is a case study based on the enterprise data warehouse and P&S data mart being developed and implemented for VDOT by TransCore. Explicitly described is how one VDOT division, Programming and Scheduling, will benefit by investing in IT to achieve its strategic goals. The design approach, methodology, and implementation procedure for the P&S decision support data mart are detailed. The methodology for capturing the performance measures that have been defined by the P&S division in the context of its strategic outcome areas is highlighted. Recommended future direction and the technologies that the agency should adopt to continue to maximize their IT investment are outlined. CR *VIRG DEP TRANSP, 1998, STRAT OUTC AR PERF M CHAUDHURI S, 1997, ACM SIGMOD RECORD, V26, P65 COREY MJ, 1998, ORACLE 8 DATA WARE H DEVLIN B, 1997, DATA WAREHOUSE ARCHI EDELSTEIN H, 1998, INTRO DATA MINING KN HACKNEY D, 1997, UNDERSTANDING IMPLEM SCHERER WT, 1999, TRANSPORT RES REC, P84 YOUNGWORTH P, 1999, DATA VISUALIZATION I TC 0 BP 175 EP 183 PG 9 SE TRANSPORTATION RESEARCH RECORD PY 2000 IS 1719 GA BU58N PI WASHINGTON RP Papiernik DK TransCore, 8614 Westwood Ctr Dr,Suite 310, Vienna, VA 22182 USA J9 TRANSP RES REC PA 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA UT ISI:000176425400023 ER PT Journal AU Ball, P TI Data visualization: Picture this SO NATURE LA English DT News Item NR 4 SN 0028-0836 PU NATURE PUBLISHING GROUP CR EISEN MB, 1998, P NATL ACAD SCI USA, V95, P14863 KARR TL, 2000, TRENDS GENET, V16, P231 VANTVEER LJ, 2002, NATURE, V415, P530 WEGMAN E, IN PRESS STAT MED TC 0 BP 11 EP 13 PG 3 JI Nature PY 2002 PD JUL 4 VL 418 IS 6893 GA 569JL PI LONDON J9 NATURE PA MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND UT ISI:000176599200012 ER PT Journal AU Condon, E Golden, B Lele, S Raghavan, S Wasil, E TI A visualization model based on adjacency data SO DECISION SUPPORT SYSTEMS LA English DT Article NR 7 SN 0167-9236 PU ELSEVIER SCIENCE BV C1 Univ Maryland, Rh Smith Sch Business, College Pk, MD 20742 USA Univ Maryland, Rh Smith Sch Business, College Pk, MD 20742 USA Univ Maryland, Inst Res Elect & Appl Phys, College Pk, MD 20742 USA American Univ, Kogod Sch Business, Washington, DC 20016 USA DE visualization; Sammon map; multidimensional scaling AB In this paper, we describe a model whose focus is on data visualization. We assume the data are provided in adjacency format, as is frequently the case in practice. As an example, individuals who buy item a are likely to buy or consider buying items b, c, and d, also. We present a simple technique for obtaining distance measures between data points. Armed with the resulting distance matrix, we show how Sammon maps can be used to visualize the data points. An application to the college selection process is discussed in detail. (C) 2002 Elsevier Science B.V. All rights reserved. CR BERRY M, 1997, DATA MINING TECHNIQU COX T, 1994, MULTIDIMENSIONAL SCA FISKE E, 1999, FISKE GUIDE COLL 200 LAWLER E, 1976, COMBINATORIAL OPTIMI RIPLEY B, 1996, PATTERN RECOGNITION SAMMON JW, 1969, IEEE T COMPUT, V5, P401 SCHAFER JB, 2001, DATA MIN KNOWL DISC, V5, P115 TC 0 BP 349 EP 362 PG 14 JI Decis. Support Syst. PY 2002 PD AUG VL 33 IS 4 GA 564EG PI AMSTERDAM RP Golden B Univ Maryland, Rh Smith Sch Business, College Pk, MD 20742 USA J9 DECIS SUPPORT SYST PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000176301600001 ER PT Journal AU Janicke, W TI Evolutionary approach to production and requirements planning in systems of chemical multipurpose plants SO CHEMICAL ENGINEERING & TECHNOLOGY LA English DT Article NR 0 SN 0930-7516 PU WILEY-V C H VERLAG GMBH C1 OR Soft Janicke GmbH, Geusaer Str FH, D-06217 Merseburg, Germany OR Soft Janicke GmbH, D-06217 Merseburg, Germany AB This article will propose a planning approach for chemical multipurpose plants that is based on an evolutionary improvement of the current situation in the future. The basic idea is to keep the functions of the planning roles alive (requirements planning, strategic planning, operative planning, supply chain management/supply chain coordination), but to give all roles access to the information and to allow each planning role the simulation of planning situations in an integrated system. This way, the typical hierarchical planning approach can be altered to respond to the more flexible nature of the planning situation. The article will also present data visualization and data processing techniques as well as procedures for requirements and production planning that support such a planning approach. TC 0 BP 603 EP 606 PG 4 JI Chem. Eng. Technol. PY 2002 PD JUN VL 25 IS 6 GA 566JU PI WEINHEIM RP Janicke W OR Soft Janicke GmbH, Geusaer Str FH, D-06217 Merseburg, Germany J9 CHEM ENG TECHNOL PA PO BOX 10 11 61, D-69451 WEINHEIM, GERMANY UT ISI:000176425100002 ER PT Journal AU Fujishiro, I Chen, L Takeshima, Y Nakamura, H Suzuki, Y TI Parallel visualization of gigabyte datasets in GeoFEM SO CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE LA English DT Article NR 16 SN 1532-0626 PU JOHN WILEY & SONS LTD C1 Res Org Informat Sci & Technol, Tokyo, Japan Res Org Informat Sci & Technol, Tokyo, Japan Ochanomizu Univ, Tokyo 112, Japan Tohoku Univ, Sendai, Miyagi 980, Japan DE scientific visualization; parallel visualization; large-scale data visualization; volume visualization; flow visualization; polygonal simplification; feature analysis ID FIELDS; VOLUME AB An initial overview of parallel visualization in the GeoFEM software system is provided. Our visualization subsystem offers many kinds of parallel visualization methods for the users to visualize their huge finite-element analysis datasets for scalar, vector and/or tensor fields at a reasonable cost. A polygonal simplification scheme is developed to make the transmission and rendition of output graphic primitives more efficient. A salient feature of the subsystem lies in its capability in the automatic setting of visualization parameter values based on the analysis of scalar/flow field topology and volumetric coherence, to improve the quality of visualization results with a minimized number of batch re-executions. Representative experimental results illustrate the effectiveness of our subsystem. Copyright (C) 2002 John Wiley Sons, Ltd. CR CABRAL B, 1993, P SIGGRAPH 93, P263 CHEN L, 2000, P 3 INT S HIGH PERF, P537 CHONG MS, 1990, PHYS FLUIDS A-FLUID, V2, P765 DELMARCELLE T, 1993, IEEE COMPUT GRAPH, V13, P25 DOI S, 1997, SPEEDUP J, V11, P59 FUJISHIRO I, 2000, IEEE COMPUT GRAPH, V20, P46 FUJISHIRO I, 1996, IEEE T VIS COMPUT GR, V2, P144 HAIMES R, 1994, 940321 AIAA HESSELINK L, 1994, IEEE COMPUT GRAPH, V14, P76 HOPPE H, 1999, P IEEE VIS 99 OCT, P59 LORENSEN WE, 1987, COMPUT GRAPHICS, V21, P163 NAKAMURA H, 2000, P WORK PROGR IEEE VI OKUDA H, 2000, CD ROM P 4 INT C SUP SCHROEDER WJ, 1992, COMPUT GRAPHICS, V26, P65 SHINAGAWA Y, 1991, IEEE COMPUT GRAPH, V11, P41 ZOECKLER M, 1996, P IEEE VIS, P107 TC 0 BP 521 EP 530 PG 10 JI Concurr. Comput.-Pract. Exp. PY 2002 PD MAY-JUN VL 14 IS 6-7 GA 561LV PI W SUSSEX RP Chen L Res Org Informat Sci & Technol, Tokyo, Japan J9 CONCURR COMPUT-PRACT EXP PA BAFFINS LANE CHICHESTER, W SUSSEX PO19 1UD, ENGLAND UT ISI:000176142900010 ER PT Journal AU Hart, SJ Hall, GJ Kenny, JE TI A laser-induced fluorescence dual-fiber optic array detector applied to the rapid HPLC separation of polycyclic aromatic hydrocarbons SO ANALYTICAL AND BIOANALYTICAL CHEMISTRY LA English DT Article NR 38 SN 1618-2642 PU SPRINGER-VERLAG BERLIN C1 Naval Res Lab, Div Chem, 4555 Overlook Ave, Washington, DC 20375 USA Tufts Univ, Dept Chem, Medford, MA 02155 USA DE hydrocarbons; polycyclic aromatic; fluorescence; laser-induced; fiber optic array; chromatography; HPLC ID EXCITATION/EMISSION MATRIX SPECTROFLUOROMETER; QUANTIFYING CHEMICAL-COMPOSITION; RANK ANNIHILATION METHOD; LIQUID- CHROMATOGRAPHY; OCEAN WATER; CHEMILUMINESCENCE; SYSTEM; URINE AB A multi-channel detection system utilizing fiber optics has been developed for the laser-induced fluorescence (LIF) analysis of chromatographic eluents. It has been applied to the detection of polycyclic aromatic hydrocarbons (PAH) in a chromatographically overlapped standard mixture and to a complex soil sample extract obtained during fieldwork. The instrument utilizes dual-fiber optic arrays, one to deliver multiple excitation wavelengths (258-3421 nm) generated by a Raman shifter, and the other to collect fluorescence generated by the sample at each excitation wavelength; the collected fluorescence is dispersed and detected with a spectrograph/CCD combination. The resulting data were arranged into excitation emission matrices (EEM) for visualization and data analysis. Rapid characterization of PAH mixtures was achieved under isocratic chromatographic conditions (1.5 mL min(-1) and 80% acetonitrile in water), with mid mug L-1 detection limits, in less than 4 minutes. The ability of the instrument to identify co-eluting compounds was demonstrated by identifying and quantifying analytes in the rapid analysis of a 17 component laboratory-prepared PAH mixture and a soil extracted sample. Identification and quantification were accomplished using rank annihilation factor analysis (RAFA) using pure component standards and the EEMs of mixtures measured during the rapid high-performance liquid chromatography (HPLC) method as the unknowns. The percentage errors of the retention times (RTs) determined using RAFA compared to the known RTs measured with a standard absorbance detector were between 0 and 11%. For the standard PAH mixture, all 17 components were identified correctly and for the soil extracted sample, all 8 analytes present were correctly identified with only one false positive. Overall, the system achieved excellent qualitative performance with semi-quantitative results in the concentration predictions of both the standard mixture and the real-world sample. Electronic supplementary material to this paper can be obtained by using the Springer LINK server located at http://dx.doi.org/10.1007/s00216001-1125-6. 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Bioanal. Chem. PY 2002 PD JAN VL 372 IS 1 GA 560VV PI BERLIN RP Hart SJ Naval Res Lab, Div Chem, 4555 Overlook Ave, Washington, DC 20375 USA J9 ANAL BIOANAL CHEM PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000176102800042 ER PT Journal AU Cummings, J Aivazis, M Samtaney, R Radovitzky, R Mauch, S Meiron, D TI A virtual test facility for the simulation of dynamic response in materials SO JOURNAL OF SUPERCOMPUTING LA English DT Article NR 11 SN 0920-8542 PU KLUWER ACADEMIC PUBL C1 CALTECH, 1200 E Calif Blvd, Pasadena, CA 91125 USA CALTECH, Pasadena, CA 91125 USA Princeton Plasma Phys Lab, Princeton, NJ 08543 USA MIT, Cambridge, MA 02139 USA DE parallel computing; shock physics simulation AB The Center for Simulating Dynamic Response of Materials at the California Institute of Technology is constructing a virtual shock physics facility for studying the response of various target materials to very strong shocks. The Virtual Test Facility (VTF) is an end-to-end, fully three-dimensional simulation of the detonation of high explosives (HE), shock wave propagation, solid material response to pressure loading, and compressible turbulence. The VTF largely consists of a parallel fluid solver and a parallel solid mechanics package that are coupled together by the exchange of boundary data. The Eulerian fluid code and Lagrangian solid mechanics model interact via a novel approach based on level sets. The two main computational packages are integrated through the use of Pyre, a problem solving environment written in the Python scripting language. Pyre allows application developers to interchange various computational models and solver packages without recompiling code, and it provides standardized access to several data visualization engines and data input mechanisms. In this paper, we outline the main components of the VTF, discuss their integration via Pyre, and describe some recent accomplishments in large-scale simulation using the VTF. CR COHEN RE, 2000, THERMAL EQUATION STA GLAISTER P, 1988, J COMPUT PHYS, V74, P382 GODUNOV SK, 1959, MAT SBORNIK, V47, P271 GUITINO AM, 2001, UNPUB J COMPUTER AID LEW A, 2001, UNPUB J COMPUTER AID MORANO E, UNPUB LEVEL SET APPR PARASHAR M, 1999, DAGH DATA MANAGEMENT PULLIN DI, 1980, J COMPUT PHYS, V34, P231 SAMTANEY R, 1994, J FLUID MECH, V269, P45 SAMTANEY R, 1997, PHYS FLUIDS, V9, P1783 VANLEER B, 1977, J COMPUT PHYS, V23, P276 TC 0 BP 39 EP 50 PG 12 JI J. Supercomput. PY 2002 PD AUG VL 23 IS 1 GA 558CF PI DORDRECHT RP Cummings J CALTECH, 1200 E Calif Blvd, Pasadena, CA 91125 USA J9 J SUPERCOMPUT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS UT ISI:000175947100004 ER PT Journal AU Buja, A Swayne, DF TI Visualization methodology for multidimensional scaling SO JOURNAL OF CLASSIFICATION LA English DT Article NR 33 SN 0176-4268 PU SPRINGER-VERLAG C1 Univ Penn, Wharton Sch, Dept Stat, Philadelphia, PA 19104 USA Univ Penn, Wharton Sch, Dept Stat, Philadelphia, PA 19104 USA AT&T Labs Res, Florham Pk, NJ 07932 USA DE proximity data; multivariate analysis; data visualization; interactive graphics AB We discuss the application of interactive visualization techniques to multidimensional scaling (MDS). MDS in its conventional batch implementations is prone to uncertainties with regard to (a) local minima in the underlying optimization, (b) sensitivity to the choice of the optimization criterion, (c) artifacts in point configurations, and (d) local inadequacy of the point configurations. These uncertainties will be addressed by the following interactive techniques: (a) algorithm animation, random restarts, and manual editing of configurations, (b) interactive control over parameters that determine the criterion and its minimization, (c) diagnostics for pinning down artifactual point configurations, and (d) restricting MDS to subsets of objects and subsets of pairs of objects. A system, called "XGvis", which implements these techniques, is freely available with the "XGobi" distribution. XGobi is a multivariate data visualization system that is used here for visualizing point configurations. 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Classif. PY 2002 VL 19 IS 1 GA 556UE PI NEW YORK RP Buja A Univ Penn, Wharton Sch, Dept Stat, Philadelphia, PA 19104 USA J9 J CLASSIF PA 175 FIFTH AVE, NEW YORK, NY 10010 USA UT ISI:000175866700001 ER PT Journal AU Schofield, O Bergmann, T Bissett, P Grassle, JF Haidvogel, DB Kohut, J Moline, M Glenn, SM TI The long-term ecosystem observatory: An integrated coastal observatory SO IEEE JOURNAL OF OCEANIC ENGINEERING LA English DT Article NR 23 SN 0364-9059 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 Rutgers State Univ, Inst Marine & Coastal Sci, Cosastal Ocean Observ Lab, New Brunswick, NJ 08901 USA Rutgers State Univ, Inst Marine & Coastal Sci, Cosastal Ocean Observ Lab, New Brunswick, NJ 08901 USA Florida Environm Res Inst, Tampa, FL 33611 USA Calif Polytech State Univ San Luis Obispo, Dept Biol, San Luis Obispo, CA 93407 USA DE forecasting; rapid environmental assessment ID NUMERICAL-SIMULATION; SARGASSO SEA; UPPER WATERS; APPARENT; CARBON; OCEAN; MODEL AB An integrated ocean observatory has been developed and operated in the coastal waters off the central coast of New Jersey, USA. One major goal for the Long-term Ecosystem Observatory (LEO) is to develop a real-time capability for rapid environmental assessment and physical/biological forecasting in coastal waters. To this end, observational data are collected from satellites, aircrafts, ships, fixed/relocatable moorings and autonomous underwater vehicles. The majority of the data are available in real-time allowing for adaptive sampling of episodic events and are assimilated into ocean forecast models. In this observationally rich environment, model forecast errors are dominated by uncertainties in the model physics or future boundary conditions rather than initial conditions. Therefore, ensemble forecasts with differing model parameterizations provide a unique opportunity for model refinement and validation. The system has been operated during three annual coastal predictive skill experiments from 1998 through 2000. To illustrate the capabilities of the system, case studies on coastal upwelling and small-scale biological slicks will be discussed. This observatory is one part of the expanding network of ocean observatories that will form the basis of a national observation network. These regional efforts should be linked through satellite remote sensing and surface current radar systems. Data on the ocean interior will be provided from subsurface AUVs and moorings. The combined data should be available through a network of virtual labs capable of rapid data visualization and dissemination. CR BARRETT GR, 1997, AM J KNEE SURG, V10, P2 BISCAYE PE, 1994, DEEP SEA RES 2, V41, P231 BISSETT WP, 1999, DEEP-SEA RES PT I, V46, P205 BISSETT WP, 1999, DEEP-SEA RES PT I, V46, P271 DELANEY JR, 2000, OCEANOGRAPHY, V13, P71 DICKEY T, 1993, SEA TECH, V34, P47 FALKOWSKI PG, 1994, DEEP-SEA RES PT II, V41, P583 GLENN S, 2000, OCEANOGRAPHY, V13, P24 GLENN SM, 2000, OCEANOGRAPHY, V13, P12 HAIDVOGEL DB, 2000, OCEANOGRAPHY, V13, P35 HALLEGRAEFF GM, 1993, PHYCOLOGIA, V32, P79 HOLLIGAN PM, 1992, ADV ECOL RES, V22, P211 KARL DM, 1996, DEEP-SEA RES PT II, V43, P129 LARGE WG, 1994, REV GEOPHYS, V32, P363 MELLOR GL, 1982, REV GEOPHYS SPACE PH, V20, P851 MICHAELS AF, 1994, DEEP-SEA RES, V41, P1013 PRICE JF, 1986, J GEOPHYS RES-OCEANS, V91, P8411 ROEMMICH D, 1995, SCIENCE, V267, P1324 SCHOFIELD O, 1999, J PHYSL, V35, P125 SIMONETTI P, 1998, SEA TECH, V38, P17 SMITH RC, 1987, APPL OPTICS, V26, P2068 STYLES R, 2000, J GEOPHYS RES, P119 VONALT C, 1994, P 1994 AUT UND VEH T, P13 TC 0 BP 146 EP 154 PG 9 JI IEEE J. Ocean. Eng. PY 2002 PD APR VL 27 IS 2 GA 554UC PI NEW YORK RP Schofield O Rutgers State Univ, Inst Marine & Coastal Sci, Cosastal Ocean Observ Lab, New Brunswick, NJ 08901 USA J9 IEEE J OCEANIC ENG PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000175754700002 ER PT Journal AU Bastin, L Fisher, PF Wood, J TI Visualizing uncertainty in multi-spectral remotely sensed imagery SO COMPUTERS & GEOSCIENCES LA English DT Article NR 45 SN 0098-3004 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Univ Leicester, Dept Geog, Leicester LE1 7RH, Leics, England Univ Leicester, Dept Geog, Leicester LE1 7RH, Leics, England Univ Nottingham, Dept Geog, Nottingham NG7 2RD, England City Univ London, Dept Informat Sci, London EC1V 0HB, England DE uncertainty; sub-pixel phenomena; visualization; exploratory analysis ID SPATIAL DATA; CLASSIFICATION; RELIABILITY; EXPLORATION; PIXEL; ERROR AB Error and uncertainty in remotely sensed data come from several sources, and can be increased or mitigated by the processing to which that data is subjected (e.g. resampling, atmospheric correction). Historically the effects of such uncertainty have only been considered overall and evaluated in a confusion matrix which becomes high-level meta-data, and so is commonly ignored. However, some of the sources of uncertainty can be explicitly identified and modelled, and their effects (which often vary across space and time) visualized. Others can be considered overall, but their spatial effects can still be visualized. This process of visualization is of particular value for users who need to assess the importance of data uncertainty for their own practical applications. This paper describes a Java-based toolkit, which uses interactive and linked views to enable visualization of data uncertainty by a variety of means. This allows users to consider error and uncertainty as integral elements of image data, to be viewed and explored, rather than as labels or indices attached to the data. (C) 2002 Elsevier Science Ltd. All rights reserved. CR *SUN COMP, 1997, JAV BEANS STAND BASTIN L, 1997, INT J REMOTE SENS, V18, P3629 BASTIN L, 1999, P INT S SPAT DAT QUA, P243 BASTIN L, 1999, SPATIAL ACCURACY ASS, P151 BEARD MK, 1999, GEOGRAPHICAL INFORMA, V1, P219 BELNKINSOP S, IN PRESS CARTOGRAPHI CAMPBELL JB, 1996, INTRO REMOTE SENSING, P622 DAVIS TJ, 1997, COMPUT GEOSCI, V23, P397 DICARLO W, 2000, GEOGRAPHICAL ENV MOD, V4, P7 DYKES JA, 1997, COMPUT GEOSCI, V23, P345 EHLSCHLAEGER CR, 1997, COMPUT GEOSCI, V23, P387 EVANS BJ, 1997, COMPUT GEOSCI, V23, P409 FISHER P, 1997, INT J REMOTE SENS, V18, P679 FISHER PF, 1994, CARTOGRAPHY GEOGRAPH, V21, P31 FISHER PF, 1994, PHOTOGRAMM ENG REM S, V60, P905 FISHER PF, 1991, REMOTE SENS ENVIRON, V34, P121 FOODY GM, 1996, INT J REMOTE SENS, V17, P1317 FOODY GM, 1992, PHOTOGRAMMETRIC ENG, V60, P61 FRIEDMAN JH, 1974, IEEE T COMPUT, V23, P881 GOODCHILD M, 1994, VISUALIZATION GEOGRA, P141 HEARNSHAW H, 1994, VISUALIZATION GEOGRA HOOTSMANS RM, 1996, THESIS U UTRECHT NET HUGHES M, 1999, SPATIAL ACCURACY ASS, P319 HUNTER GJ, 1996, J URBAN REG INF SYST, V8, P51 HUNTER GJ, 1995, PHOTOGRAMM ENG REM S, V61, P529 JENSEN TK, 1996, VET INFORMATION, V2, P3 JUSTICE CO, 1989, INT J REMOTE SENS, V10, P1539 KEREKES JP, 1989, IEEE T GEOSCI REMOTE, V6, P762 KRAAK MJ, 1999, INT J GEOGR INF SCI, V13, P285 LUNETTA RS, 1991, PHOTOGRAMM ENG REM S, V57, P677 MACEACHREN AM, 1992, CARTOGRAPHIC PERSPEC, V13, P10 MACEACHREN AM, 1997, COMPUT GEOSCI, V23, P335 MACEACHREN AM, 1994, VISUALIZATION MODERN MACEACHREN AM, 1994, VISUALIZATION MODERN, P1 MARKHAM BL, 1985, IEEE T GEOSCI REMOTE, V6, P864 MATHER P, 1999, COMPUTER PROCESSING MONMONIER M, 1989, GEOGR ANAL, V21, P81 PETRAKOS M, 1999, P IGARSS C HAMB, P2498 SADOWSKI FA, 1976, 10960071F ENV RES I SHI WZ, 1999, T GIS, V3, P119 VANDERWEL FJM, 1998, COMPUT GEOSCI, V24, P335 VANDERWEL FJM, 1993, P 16 INT CART C COL, V2, P881 VANDERWEL FJM, 1994, VISUALIZATION MODERN, P313 VANKOOTWIJK EJ, 1995, INT J REMOTE SENS, V16, P97 WEGMAN EJ, 1990, J AM STAT ASSOC, V85, P664 TC 0 BP 337 EP 350 PG 14 JI Comput. Geosci. PY 2002 PD APR VL 28 IS 3 GA 552XL PI OXFORD RP Fisher PF Univ Leicester, Dept Geog, Leicester LE1 7RH, Leics, England J9 COMPUT GEOSCI PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000175645300005 ER PT Journal AU Lin, CF Yang, DL Chung, YC TI Parallel shear-warp factorization volume rendering using efficient 1-D and 2-D partitioning schemes for distributed memory multicomputers SO JOURNAL OF SUPERCOMPUTING LA English DT Article NR 25 SN 0920-8542 PU KLUWER ACADEMIC PUBL C1 Feng Chia Univ, Dept Informat Engn, Taichung 407, Taiwan Feng Chia Univ, Dept Informat Engn, Taichung 407, Taiwan DE volume rendering; data partitioning; image compositing; shear- warp factorization; distributed memory multicomputer ID VISUALIZATION AB 3-D data visualization is very useful for medical imaging and computational fluid dynamics. Volume rendering can be used to exhibit the shape and volumetric properties of 3-D objects. However, volume rendering requires a considerable amount of time to process the large volume of data. To deliver the necessary rendering rates, parallel hardware architectures such as distributed memory multicomputers offer viable solutions. The challenge is to design efficient parallel algorithms that utilize the hardware parallelism effectively. In this paper, we present two efficient parallel volume rendering algorithms, the 1D-partition and 2D-partition methods, based on the shear-warp factorization for distributed memory multicomputers. The 1D- partition method has a performance bound on the size of the volume data. If the number of processors is less than a threshold, the 1D-partition method can deliver a good rendering rate. If the number of processors is over a threshold, the 2D- partition method can be used. To evaluate the performance of these two algorithms, we implemented the proposed methods along with the slice data partitioning, volume data partitioning, and sheared volume data partitioning methods on an IBM SP2 parallel machine. Six volume data sets were used as the test samples. The experimental results show that the proposed methods outperform other compatible algorithms for all test samples. When the number of processors is over a threshold, the experimental results also demonstrate that the 2D-partition method is better than the 1D-partition method. CR 1994, MPI FOR MPI MESS PAS *IBM, IBM AIX PAR ENV PAR AMIN MB, 1995, P 1995 PAR REND S AT, P7 CORRIE B, 1993, P 1993 PAR REND S SA, P23 DREBIN RA, 1988, COMPUT GRAPHICS, V22, P65 GROELLER E, 1995, 1862 TU VIENN GROELLER E, 1995, TR18629504 TU VIENN HSU WM, 1993, P 1993 PAR REND S SA, P7 KAUFMAN A, 1991, VOLUME VISUALIZATION LACROUTE P, 1996, IEEE T VIS COMPUT GR, V2, P218 LACROUTE P, 1995, P 1995 PAR REND S AT, P15 LACROUTE P, 1994, P SIGGRAPH 94, P451 LACROUTE P, 1995, THESIS STANFORD U LAUR D, 1991, COMPUT GRAPHICS, V25, P285 LEVOY M, 1990, ACM T GRAPHIC, V9, P245 MA KL, 1994, IEEE COMPUT GRAPH, V14, P59 MA KL, 1993, P 1993 PAR REND S SA, P15 PORTER T, 1984, COMPUT GRAPHICS, V18, P253 SANO K, 1997, P 1997 PAR REND S OC SINGH JP, 1994, COMPUTER, V27, P45 UPSON C, 1988, COMPUT GRAPHICS, V22, P59 WESTOVER L, 1990, COMP GRAPH, V24, P367 WILHELMS J, 1991, COMPUT GRAPHICS, V25, P275 WITTENBRINK CM, 1993, PAR REND S VIS 93 MC, P57 YOO TS, 1992, IEEE COMPUT GRAPH, V12, P63 TC 0 BP 277 EP 302 PG 26 JI J. Supercomput. PY 2002 PD JUL VL 22 IS 3 GA 547WX PI DORDRECHT RP Yang DL Feng Chia Univ, Dept Informat Engn, Taichung 407, Taiwan J9 J SUPERCOMPUT PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS UT ISI:000175356700004 ER PT Journal AU Hedley, NR Billinghurst, M Postner, L May, R Kato, H TI Explorations in the use of augmented reality for geographic visualization SO PRESENCE-TELEOPERATORS AND VIRTUAL ENVIRONMENTS LA English DT Article NR 31 SN 1054-7460 PU M I T PRESS C1 Univ Washington, Human Interface Technol Lab, Box 352-142, Seattle, WA 98195 USA Univ Washington, Human Interface Technol Lab, Seattle, WA 98195 USA Hiroshima City Univ, Fac Informat Sci, Asaminami Ku, Hiroshima 7313194, Japan ID ENVIRONMENT; INTERFACE; WORKBENCH AB In this paper, we describe two explorations in the use of hybrid user interfaces for collaborative geographic data visualization. Our first interface combines three technologies: augmented reality (AR), immersive virtual reality (VR), and computer vision-based hand and object tracking. Wearing a lightweight display with an attached camera, users can look at a real map and see three-dimensional virtual terrain models overlaid on the map. From this AIR interface, they can fly in and experience the model immersively, or use free hand gestures or physical markers to change the data representation. Building on this work, our second interface explores alternative interface techniques, including a zoomable user interface, paddle interactions, and pen annotations. We describe the system hardware and software and the implications for GIS and spatial science applications. CR BEDERSON BB, 1996, J VISUAL LANG COMPUT, V7, P3 BIER E, 1993, P SIGGRAPH 93, P73 BILLINGHURST M, 2001, COMPUT GRAPH-UK, V25, P745 BILLINGHURST M, 1998, P 2 INT S WEAR COMP, P76 BILLINGHURST M, 1998, VIRTUAL REALITY, V3, P25 BUTZ A, 1999, P 2 INT WORKSH AUGM, P35 DEDE C, 1999, COMPUTER MODELING SI, P282 ERICKSON T, 1993, VIRTUAL REALITY APPL, P3 FITZMAURICE G, 1997, P CHI 97 ACM, P43 FJELD M, 1999, P HUC99, P102 GORBET M, 1998, P CHI 98, P49 HEDLEY N, 1998, R993 HUM INT TECHN L HEDLEY NR, 2001, ASS AM GEOGR ANN M M HEDLEY NR, 1999, INFORMATICA, V23, P155 HEDLEY NR, 2001, P 20 INT CART C BEIJ, V4, P2606 HOLLERER T, 1999, COMPUT GRAPH-UK, V23, P779 ISHII H, 1997, P CHI 97, P234 KATO H, 2000, P INT S AUGM REAL 20, P111 KIYOKAWA K, 1999, P IEEE INT C SYST MA, V6, P48 KRAAK M, 1994, VISUALIZATION MODERN, P269 KRUEGER M, 1983, ARTIFICIAL REALITY KRUGER W, 1995, COMPUTER, V28, P42 LEIBE B, 2000, IEEE COMPUT GRAPH, V20, P54 LIN C, 1998, P ACM S VIRT REAL SO, P187 MACEACHREN AM, 2001, CARTOGRAPHY GEOGRAPH, V28 MACEACHREN AM, 1999, P ACM WORKSH NEW PAR, P35 NYERGES T, 1998, HUM-COMPUT INTERACT, V13, P127 REKIMOTO J, 2000, P DES AUGM REAL ENV REKIMOTO J, 1998, P IEEE INT S WEAR CO, P68 SLOCUM TA, 2001, CARTOGRAPHY GEOGRAPH, V28, P61 ULLMER B, 1997, P ACM S US INT SOFTW, P223 TC 0 BP 119 EP 133 PG 15 JI Presence-Teleoper. Virtual Env. PY 2002 PD APR VL 11 IS 2 GA 546KE PI CAMBRIDGE RP Hedley NR Univ Washington, Human Interface Technol Lab, Box 352-142, Seattle, WA 98195 USA J9 PRESENCE-TELEOPER VIRTUAL ENV PA FIVE CAMBRIDGE CENTER, CAMBRIDGE, MA 02142 USA UT ISI:000175272600003 ER PT Journal AU Penterman, R Klink, SL de Koning, H Nisato, G Broer, DJ TI Single-substrate liquid-crystal displays by photo-enforced stratification SO NATURE LA English DT Article NR 15 SN 0028-0836 PU NATURE PUBLISHING GROUP C1 Philips Res Labs, Prof Holstlaan 4, NL-5656 AA Eindhoven, Netherlands Philips Res Labs, NL-5656 AA Eindhoven, Netherlands Eindhoven Univ Technol, Dept Polymer Chem & Technol, NL-5600 MB Eindhoven, Netherlands ID SEPARATED COMPOSITE FILMS; POLYMER AB Data visualization plays a crucial role in our society, as illustrated by the many displays that surround us. In the future, displays may become even more pervasive, ranging from individually addressable image-rendering wall hangings to data displays integrated in clothes(1). Liquid-crystal displays (LCDs) provide most of the flat-panel displays currently used. To keep pace with the ever-increasing possibilities afforded by developments in information technology, we need to develop manufacturing processes that will make LCDs cheaper and larger, with more freedom in design. Existing batch processes for making and filling LCD cells(2,3) are relatively expensive, with size and shape limitations. Here we report a cost- effective, single-substrate technique in which a coated film is transformed into a polymer-covered liquid-crystal layer. This approach is based on photo-enforced stratification: a two-step photopolymerization-induced phase separation of a liquid- crystal blend and a polymer precursor. The process leads to the formation of micrometre-sized containers filled with a switchable liquid-crystal phase. In this way, displays can be produced on a variety of substrates using current coating technology. The developed process may be an important step towards new technologies such as 'display-on-anything' and 'paintable displays'. CR BOBROV YA, 1998, MATER RES SOC SYMP P, V508, P225 BOWLEY CC, 1999, MOL CRYST LIQ CRYS 4, V331, P2069 BROER DJ, 1995, NATURE, V378, P467 DOANE JW, 1986, APPL PHYS LETT, V48, P269 FARRINGDON J, 1999, P 3 INT S WEAR COMP, P107 HIRAI Y, 1990, P SOC PHOTO-OPT INS, V1257, P2 KAMIYA H, 2001, SID 01 DIG TECHN PAP, V32, P1354 KIEFER R, 1992, P 12 INT DISPL RES C, P547 MOROZUMI S, 1990, LIQUID CRYSTALS APPL, V1, P181 OHE M, 1995, P 15 INT DISPL RES C, P577 PARK EY, 2000, SID 00 DIG TECHN PAP, V31, P782 QIAN TZ, 2000, PHYS REV E B, V61, P4007 VAZ NA, 1987, MOL CRYST LIQ CRYST, V146, P1 VORFLUSEV V, 1999, SCIENCE, V283, P1903 YAMAMOTO T, 1995, J GLAUCOMA, V4, P158 TC 0 BP 55 EP 58 PG 5 JI Nature PY 2002 PD MAY 2 VL 417 IS 6884 GA 546ZM PI LONDON RP Broer DJ Philips Res Labs, Prof Holstlaan 4, NL-5656 AA Eindhoven, Netherlands J9 NATURE PA MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND UT ISI:000175307200035 ER PT Journal AU Tino, P Nabney, I TI Hierarchical GTM: Constructing localized nonlinear projection manifolds in a principled way SO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE LA English DT Article NR 23 SN 0162-8828 PU IEEE COMPUTER SOC C1 Aston Univ, Neural Computat Res Grp, Birmingham B4 7ET, W Midlands, England Aston Univ, Neural Computat Res Grp, Birmingham B4 7ET, W Midlands, England DE hierarchical probabilistic model; generative topographic mapping; data visualization; EM algorithm; density estimation; directional curvature ID ALGORITHM AB It has been argued that a single two-dimensional visualization plot may not be sufficient to capture all of the interesting aspects of complex data sets and, therefore, a hierarchical visualization system is desirable. In this paper, we extend an existing locally linear hierarchical visualization system PhiVis [1] in several directions: 1) We allow for nonlinear projection manifolds. The basic building block is the Generative Topographic Mapping (GTM). 2) We introduce a general formulation of hierarchical probabilistic models consisting of local probabilistic models organized in a hierarchical tree. General training equations are derived, regardless of the position of the model in the tree. 3) Using tools from differential geometry, we derive expressions for local directional curvatures of the projection manifold. Like PhiVis, our system is statistically principled and is built interactively in a top-down fashion using the EM algorithm. It enables the user to interactively highlight those data in the ancestor visualization plots which are captured by a child model. We also incorporate into our system a hierarchical, locally selective representation of magnification factors and directional curvatures of the projection manifolds. Such information is important for further refinement of the hierarchical visualization plot, as well as for controlling the amount of regularization imposed on the local models. We demonstrate the principle of the approach on a toy data set and apply our system to two more complex 12- and 18-dimensional data sets. CR AMARI SI, 1985, DIFFERENTIAL GEOMETR AURENHAMMER F, 1991, ACM COMPUT SURV, V3, P345 BATES DM, 1980, J ROY STAT SOC B MET, V42, P1 BISHOP CM, 1998, IEEE T PATTERN ANAL, V20, P281 BISHOP CM, 1998, NEURAL COMPUT, V10, P215 BISHOP CM, 1995, NEURAL NETWORKS PATT BISHOP CM, 1998, NEUROCOMPUTING, V21, P203 BISHOP CM, 1997, P 1997 WORKSH SELF O BISHOP CM, 1997, P IEE 5 INT C ART NE, P64 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 HORN RA, 1985, MATRIX ANAL KOHONEN T, 1990, P IEEE, V78, P1464 MIIKKULAINEN R, 1990, CONNECT SCI, V2, P83 PRESS WH, 1988, NUMERICAL RECIPES C ROSE K, 1990, PHYS REV LETT, V65, P945 SEBER GAF, 1989, NONLINEAR REGRESSION TIPPING ME, 1999, NEURAL COMPUT, V11, P443 ULTSCH A, 1993, INFORMATION CLASSIFI, P301 ULTSCH A, 1990, P INNC 90 INT NEUR N, P305 VERSINO C, 1996, P ICANN 96 INT C ART, P221 VERSINO C, 1996, P ICONIP 96 INT C NE, V2, P921 VESANTO J, 1999, INTELL DATA ANAL, V3, P111 WILLIAMS CKI, 2000, ADV NEUR IN, V12, P680 TC 0 BP 639 EP 656 PG 18 JI IEEE Trans. Pattern Anal. Mach. Intell. PY 2002 PD MAY VL 24 IS 5 GA 544XU PI LOS ALAMITOS RP Tino P Aston Univ, Neural Computat Res Grp, Birmingham B4 7ET, W Midlands, England J9 IEEE TRANS PATT ANAL MACH INT PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000175187800006 ER PT Journal AU Inyang, HI TI Visualization of data within an environmental risk management framework SO JOURNAL OF ENVIRONMENTAL ENGINEERING-ASCE LA English DT Editorial Material NR 0 SN 0733-9372 PU ASCE-AMER SOC CIVIL ENGINEERS TC 0 BP 301 EP 302 PG 2 JI J. Environ. Eng.-ASCE PY 2002 PD APR VL 128 IS 4 GA 540YR PI RESTON J9 J ENVIRON ENG-ASCE PA 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA UT ISI:000174958900001 ER PT Journal AU Arnold, R TI Visualization of data within an environmental risk management framework - Editor's note SO JOURNAL OF ENVIRONMENTAL ENGINEERING-ASCE LA English DT Editorial Material NR 0 SN 0733-9372 PU ASCE-AMER SOC CIVIL ENGINEERS TC 0 BP 303 EP 303 PG 1 JI J. Environ. Eng.-ASCE PY 2002 PD APR VL 128 IS 4 GA 540YR PI RESTON J9 J ENVIRON ENG-ASCE PA 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA UT ISI:000174958900002 ER PT Journal AU Su, AI Cooke, MP Ching, KA Hakak, Y Walker, JR Wiltshire, T Orth, AP Vega, RG Sapinoso, LM Moqrich, A Patapoutian, A Hampton, GM Schultz, PG Hogenesch, JB TI Large-scale analysis of the human and mouse transcriptomes SO PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA LA English DT Article NR 25 SN 0027-8424 PU NATL ACAD SCIENCES C1 Novartis Res Fdn, Genom Inst, 3115 Merryfield Row, San Diego, CA 92121 USA Novartis Res Fdn, Genom Inst, San Diego, CA 92121 USA Scripps Clin & Res Inst, Dept Chem, La Jolla, CA 92037 USA Scripps Clin & Res Inst, Dept Cell Biol, La Jolla, CA 92037 USA ID SACCHAROMYCES-CEREVISIAE; EXPRESSION; GENES; IDENTIFICATION; DATABASE; ADULT AB High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http:/ / expression.gnf.org) that integrates data visualization and curation of current gene annotations. 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Natl. Acad. Sci. U. S. A. PY 2002 PD APR 2 VL 99 IS 7 GA 539DY PI WASHINGTON RP Hogenesch JB Novartis Res Fdn, Genom Inst, 3115 Merryfield Row, San Diego, CA 92121 USA J9 PROC NAT ACAD SCI USA PA 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA UT ISI:000174856000061 ER PT Journal AU Malki, HA Umeh, CG TI Design of a fuzzy logic-based level controller SO JOURNAL OF ENGINEERING TECHNOLOGY LA English DT Article NR 14 SN 0747-9964 PU AMER SOC ENG EDUC C1 Univ Houston, Dept Elect Elect Technol, Houston, TX 77004 USA Univ Houston, Dept Elect Elect Technol, Houston, TX 77004 USA AB This paper presents the design and application of a fuzzy logic controller for controlling the liquid level in a tank. A proposed fuzzy controller and a conventional proportional- integral (PI) controller were applied to a real tank setup. Comparison of the control results from these two systems indicated that the fuzzy logic controller significantly reduced overshoot and steady-state error. The fuzzy logic controller used in this study was designed with LabVIEW(R) a product of National Instruments Corporation. LabVIEW(R) Is an icon-based graphical programming tool with front panel user Interfaces for control and data visualization and block diagrams for programming. CR BERENJI HR, 1992, FUZZY LOGIC CONTROLL BRUBAKER D, 1992, EDN 0618, P111 BUCEK DJ, 1989, CONTROL SYSTEMS CONT CHAUNG L, 1998, NUCL TECHNOL, V122, P318 CHAUNG L, 1997, NUCL TECHNOL, V118, P254 CONSTANTIN VA, 1996, P 5 IEEE INT C FUZZ, P1845 GUILLEMIN P, 1994, FUZZY SET SYST, V63, P339 KAVAKLIOGLU K, 1999, NUCL TECHNOL, V125, P70 MALKI HA, 1999, FUZZY THEORY SYSTEMS MALKI HA, 1994, IEEE T FUZZY SYST, V2, P345 MAMDANI EA, 1973, IEEE T SYST MAN CYB, P28 TANAKA Y, 1994, OVERVIEW FUZZY LOGIC WHITE D, 1992, HDB INTELLIGENT CONT ZADEH LA, 1965, INFORM CONTR, V8, P338 TC 0 BP 32 EP 38 PG 7 JI J. Eng. Technol. PY 2000 PD SPR VL 17 IS 1 GA 538UN PI HATTIESBURG RP Malki HA Univ Houston, Dept Elect Elect Technol, Houston, TX 77004 USA J9 J ENG TECHNOLOGY PA C/O CECIL A HARRISION, UNIV SOUTHER MISSISSIPPI, BOX 5137, HATTIESBURG, MS 39406-5137 USA UT ISI:000174833600006 ER PT Journal AU Chen, CH TI Generalized association plots: Information visualization via iteratively generated correlation matrices SO STATISTICA SINICA LA English DT Article NR 25 SN 1017-0405 PU STATISTICA SINICA C1 Acad Sinica, Inst Stat Sci, Taipei 115, Taiwan Acad Sinica, Inst Stat Sci, Taipei 115, Taiwan DE data visualization; divisive clustering tree; latent structure; perfect symmetry; proximity matrices; seriation ID NEGATIVE SYMPTOMS; SCHIZOPHRENIA; SANS; SAPS AB Given a p-dimensional proximity matrix D-pxp, a sequence of correlation matrices, R = (R-(l), R-(2),...), is iteratively formed from it. Here R-(1) is the correlation matrix of the original proximity matrix D and R-(n) is the correlation matrix of R(n-1), n > 1. This sequence was first introduced by McQuitty (1968), Breiger, Boorman and Arabie (1975) developed an algorithm, CONCOR, based on their rediscovery of its convergence. The sequence R often converges to a matrix R- (infinity) whose elements are +1 or -1. This special pattern of R-(infinity) partitions the p objects into two disjoint groups and so can be recursively applied to generate a divisive hierarchical clustering tree. While convergence is itself useful, we are more concerned with what happens before convergence. Prior to convergence, we note a rank reduction property with elliptical structure: when the rank of R-(n) reaches two, the column vectors of R-(n) fall on an ellipse in a two-dimensional subspace. The unique order of relative positions for the p points on the ellipse can be used to solve seriation problems such as the reordering of a Robinson matrix. A software package, Generalized Association Plots (GAP), is developed which utilizes computer graphics to retrieve important information hidden in the data or proximity matrices. 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Sin. PY 2002 PD JAN VL 12 IS 1 GA 530RM PI TAIPEI RP Chen CH Acad Sinica, Inst Stat Sci, Taipei 115, Taiwan J9 STAT SINICA PA C/O DR H C HO, INST STATISTICAL SCIENCE, ACADEMIA SINICA, TAIPEI 115, TAIWAN UT ISI:000174372800002 ER PT Journal AU Friendly, M TI A brief history of the mosaic display SO JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS LA English DT Article NR 52 SN 1061-8600 PU AMER STATISTICAL ASSOC C1 York Univ, Dept Psychol, 4700 Keele St, N York, ON M3J 1P3, Canada York Univ, Dept Psychol, N York, ON M3J 1P3, Canada DE cartogram; data visualization; log-linear models; mosaic matrix; space-filling displays; thematic cartography; tree map ID CATEGORICAL-DATA; GRAPHICAL PERCEPTION; CONTINGENCY-TABLES; MODELS AB This article provides an illustrated history of the visual and conceptual ideas leading to the development of mosaic displays. We trace the origins of the use of rectangles and area to depict data quantities and their relations, of early forms of mosaic displays including subdivided bar-like charts and various cartograms, to the modem forms used in log-linear analysis and in space-filling tree maps. 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Comput. Graph. Stat. PY 2002 PD MAR VL 11 IS 1 GA 530YK PI ALEXANDRIA RP Friendly M York Univ, Dept Psychol, 4700 Keele St, N York, ON M3J 1P3, Canada J9 J COMPUT GRAPH STAT PA 1429 DUKE ST, ALEXANDRIA, VA 22314 USA UT ISI:000174386900005 ER PT Journal AU Wegman, EJ Symanzik, J TI Immersive projection technology for visual data mining SO JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS LA English DT Review NR 104 SN 1061-8600 PU AMER STATISTICAL ASSOC C1 George Mason Univ, Ctr Computat Stat, MS 4A7,4400 Univ Dr, Fairfax, VA 22030 USA George Mason Univ, Ctr Computat Stat, Fairfax, VA 22030 USA Utah State Univ, Dept Math & Stat, Logan, UT 84322 USA DE C2; CAVE; IPT; MiniCAVE; PlatoCAVE; virtual reality; visualization; VR; VRGobi ID VIRTUAL-REALITY ENVIRONMENT; DATA VISUALIZATION; DYNAMIC GRAPHICS; LINKED SOFTWARE; XGOBI; CAVE; GIS AB The PlatoCAVE, the MiniCAVE, and the C2 are immersive stereoscopic projection-based virtual reality environments oriented toward group interactions. As such they are particularly suited to collaborative efforts in data analysis and visual data mining. In this article, we provide an overview of virtual reality in general, including immersive projection technology, and the use of stereoscopic displays for data visualization. We discuss design considerations for the construction of these immersive environments including one-wall versus four-wall implementations, augmented reality, stereoscopic placement, head tracking, the use of LCD devices, polarized light stereo, voice control, and image synchronization. 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Comput. Graph. Stat. PY 2002 PD MAR VL 11 IS 1 GA 530YK PI ALEXANDRIA RP Wegman EJ George Mason Univ, Ctr Computat Stat, MS 4A7,4400 Univ Dr, Fairfax, VA 22030 USA J9 J COMPUT GRAPH STAT PA 1429 DUKE ST, ALEXANDRIA, VA 22314 USA UT ISI:000174386900008 ER PT Book in series AU Ogi, T Yamamoto, K Yamada, T Hirose, M TI Experience of immersive virtual world using cellular phone interface SO ADVANCES IN MUTLIMEDIA INFORMATION PROCESSING - PCM 2001, PROCEEDINGS LA English DT Article NR 6 SN 0302-9743 PU SPRINGER-VERLAG BERLIN AB The cellular phone has become a popular portable information device. In this study, the i-mode of the cellular phone was applied to the interface with the immersive virtual world. By using the cellular phone interface, the user can experience the immersive virtual world easily using his own device. The interaction using the i-mode was experimentally evaluated in the walk-through application. In addition, by integrating the cellular phone interface with the transparent immersive projection display, an invisible immersive interface that enables the user to experience the virtual environment in the real work place was constructed. This system was applied to several fields of application such as the visualization of data and the telecommunication. CR BROWNING DR, 1994, INPUT INTERFACING CA CRUZNEIRA C, 1993, P SIGGRAPH 93, P135 HIROSE M, 1999, IEEE MULTIMEDIA, V6, P14 OGI T, 2000, 10 INT C ART REAL TE, P98 OGI T, 1999, 3 INT IMM PROJ TECHN, P223 OGI T, 2000, INET 2000 INT GLOB S TC 0 BP 32 EP 39 PG 8 SE LECTURE NOTES IN COMPUTER SCIENCE PY 2001 VL 2195 GA BT88V PI BERLIN J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000174348000005 ER PT Journal AU Syroid, ND Agutter, J Drews, FA Westenskow, DR Albert, RW Bermudez, JC Strayer, DL Prenzel, H Loeb, RG Weinger, MB TI Development and evaluation of a graphical anesthesia drug display SO ANESTHESIOLOGY LA English DT Article NR 32 SN 0003-3022 PU LIPPINCOTT WILLIAMS & WILKINS C1 Univ Utah, Dept Anesthesiol, Sch Med, 3C444,SOM,30 N 1900 E, Salt Lake City, UT 84132 USA Univ Utah, Dept Anesthesiol, Sch Med, Salt Lake City, UT 84132 USA Univ Arizona, Dept Anesthesiol, Tucson, AZ USA ID CONTROLLED INFUSION PUMP; ALVEOLAR CONCENTRATION; PLASMA- CONCENTRATIONS; FENTANYL; PROPOFOL; PHARMACOKINETICS; REDUCTION; PHARMACODYNAMICS; ALFENTANIL; ISOFLURANE AB Background: Usable real-time displays of intravenous anesthetic concentrations and effects could significantly enhance intraoperative clinical decision-making. Pharmacokinetic models are available to estimate past, present, and future drug effect-site concentrations, and pharmacodynamic models are available to predict the drug's associated physiologic effects. Methods: An interdisciplinary research team (bioengineering, architecture, anesthesiology, computer engineering, and cognitive psychology) developed a graphic display that presents the real-time effect-site concentrations, normalized to the drugs' EC95, of intravenous drugs. Graphical metaphors were created to show die drugs' pharmacodynamics. To evaluate the effect of the display on the management of total intravenous anesthesia, 15 anesthesiologists participated in a computer- based simulation study. The participants cared for patients during two experimental conditions: with and without the drug display. Results: With the drug display, clinicians administered more bolus doses of remifentanil during anesthesia maintenance. There was a significantly lower variation in the predicted effect-site concentrations for remifentanil and propofol, and effect-site concentrations were maintained closer to the drugs' EC95. There was no significant difference in the simulated patient heart rate and blood pressure with respect to experimental condition. The perceived performance for the participants was increased with the drug display, whereas mental demand, effort, and frustration level were reduced. In a postsimulation questionnaire, participants rated the display to be a useful addition to anesthesia monitoring. Conclusions: The drug display altered simulated clinical practice. These results, which will inform the next iteration of designs and evaluations, suggest promise for this approach to drug data visualization. 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General aviation (GA) flights account for 92% of all the aviation accidents. Researchers are addressing this problem from various perspectives including improving meteorological forecasting techniques, collecting additional weather data automatically via on-board sensors and "flight" modems, and improving weather data dissemination (often available only in the textual format) and visualization techniques. We approach the problem from the improved dissemination perspective and propose weather visualization methods tailored for general aviation pilots. Although some aviation weather data, such as possible icing (Airman's Meteorological Information (AIRMETs)) or turbulence conditions (Significant Meteorological Conditions (SIGMETs)), or information about precipitation intensity and movement, has already been presented well by existing systems, there is still an urgent need for visualizing several critical weather elements neglected so far. Our system, Aviation Weather Data Visualization Environment (AWE), focuses on graphical displays of these weather elements, namely, meteorological observations, terminal area forecasts, and winds aloft forecasts and maps them onto a cartographic grid specific to the pilot's area of interest. Additional weather graphics such as icing (AIRMETs) or turbulence conditions (SIGMETs) can easily be added to our system to provide a pilot with a more complete visual weather briefing. Decisions regarding the graphical display and design are made based on careful consideration of user needs. Integral visual display of these elements of weather reports is designed for the use of GA pilots as a weather briefing and route selection tool, AWE provides linking of the weather information to the flight's path and schedule. The pilot can interact with the system to obtain aviation-specific weather for the entire area or for his specific route to explore what-if scenarios including the selection of alternates, and make "go/no-go" decisions. AWE, as evaluated by some pilots at National Aeronautics and Space Administration Ames Research Center, was found to be useful. (C) 2002 Elsevier Science Ltd. All rights reserved. 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Graph.-UK PY 2002 PD FEB VL 26 IS 1 GA 525HB PI OXFORD RP Spirkovska L NASA, Ames Res Ctr, MS 269-3, Moffett Field, CA 94035 USA J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000174061900017 ER PT Journal AU Sarfraz, M TI Some remarks on a rational cubic spline for the visualization of monotonic data SO COMPUTERS & GRAPHICS-UK LA English DT Letter NR 4 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 King Fahd Univ Petr & Minerals, Dept Informat & Comp Sci, KFUPM 1510, Dhahran 31261, Saudi Arabia King Fahd Univ Petr & Minerals, Dept Informat & Comp Sci, Dhahran 31261, Saudi Arabia DE data visualization; rational spline; interpolation; shape preserving; monotone AB A smooth rational cubic spline interpolation scheme, to preserve the shape of monotonic data, was developed by Sarfraz (Computers Graphics 2000;24(4): 509-16). The paper (Computers Graphics 2000;24(4): 509-16), after a further study, motivated to mention few remarks. This article is devoted towards the compilation of those remarks. (C) 2002 Elsevier Science Ltd. All rights reserved. CR GREGORY JA, 1986, COMPUT AIDED DESIGN, V18, P53 PRESS WH, 1992, NUMERICAL RECIPES C PRESS WH, 1996, NUMERICAL RECIPES FO SARFRAZ M, 2000, COMPUT GRAPH-UK, V24, P509 TC 0 BP 193 EP 197 PG 5 JI Comput. Graph.-UK PY 2002 PD FEB VL 26 IS 1 GA 525HB PI OXFORD RP Sarfraz M King Fahd Univ Petr & Minerals, Dept Informat & Comp Sci, KFUPM 1510, Dhahran 31261, Saudi Arabia J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000174061900018 ER PT Journal AU Merritt, RK TI From memory arts to the new code paradigm - The artist as engineer of virtual information space and virtual experience SO LEONARDO LA English DT Article NR 21 SN 0024-094X PU M I T PRESS C1 Luther Coll, Decorah, IA USA Luther Coll, Decorah, IA USA AB This paper examines contemporary developments in the creation and experience of immersive 3D art projects in the context of spatial and information design. it takes into consideration historic forebears, particularly the ancient Greek art of memory, contemporary theorists, and current new media artists who are pushing code and application design to new limits. the essay specifically addresses the role of the artist as 'coder' and application engineer and anticipates concerns and possible technological developments in data visualization and virtual spaces. As new media artists write their own code, current boundaries between disciplines and sectors become blurred and new aesthetic judgments become pivotal. Additionally, current management and organizational structures are challenged to confront a world that increasingly visualizes and communicates in 3D. CR ANDERS P, 1999, ENVISIONING CYBERSPA BRUNO G, 1582, DE UMBRO IDEORUM BRUNO G, MEMORY WHEEL, ILL CANNON S, 2000, ITS ALL RELATIVE JAN, P94 FIREBAUGH MW, 1993, COMPUTER GRAPHICS TO FRY B, VALENCE SOFTWARE, ILL GELERTNER D, 1981, MIRROR WORLD DAYS OF GIBSON W, COUNT ZERO GIBSON W, MONA LISA OVERDRIVE GIBSON W, NEUROMANCER KONDO E, 2000, ID JAN, P90 MOLTENBREY K, 1999, COMPUT GRAPH WORLD, P40 SCHWARZ HP, 1997, MEDI ART HIST STEPHENSON N, 2000, SNOW CRASH STERLING B, 1988, ISLANDS NET TOMLINSON K, 1735, ART DANCING TUFTE ER, 1997, VISUAL EXPLANATION I VADNERBILT T, 2000, ID JAN, P96 WACHOWSKI A, 1999, MATRIX WATTENBERG M, 2001, APARTMENT, ILL YATES FA, 1997, ART MEMORY TC 0 BP 403 EP 408 PG 6 JI Leonardo PY 2001 VL 34 IS 5 GA 502JC PI CAMBRIDGE RP Merritt RK Luther Coll, Decorah, IA USA J9 LEONARDO PA FIVE CAMBRIDGE CENTER, CAMBRIDGE, MA 02142 USA UT ISI:000172739600003 ER PT Journal AU Rabow, AA Shoemaker, RH Sausville, EA Covell, DG TI Mining the National Cancer Institute's tumor-screening database: Identification of compounds with similar cellular activities SO JOURNAL OF MEDICINAL CHEMISTRY LA English DT Review NR 113 SN 0022-2623 PU AMER CHEMICAL SOC C1 NCI, Sci Applicat Int Corp, NIH, DCTD, Frederick, MD 21702 USA NCI, Sci Applicat Int Corp, NIH, DCTD, Frederick, MD 21702 USA NCI, Dev Therapeut Program, NIH, DCTD, Frederick, MD 21702 USA ID PROTEIN-KINASE-C; GENE-EXPRESSION DATA; ANTICANCER DRUG SCREEN; SELF-ORGANIZING MAPS; DOWN-REGULATION; MOLECULAR PHARMACOLOGY; ELLIPTICINE ANALOGS; EPITHELIAL-CELLS; CLUSTER-ANALYSIS; HIGH- THROUGHPUT AB In an effort to enhance access to information available in the National Cancer Institute's (NCI) anticancer drug-screening database, a new suite of Internet accessible (http://spheroid.ncifcrf.gov) computational tools has been assembled for self-organizing map-based (SOM) cluster analysis and data visualization. A range of analysis questions were initially addressed to evaluate improvements in SOM cluster quality based on the data-conditioning procedures of Z-score normalization, capping, and treatment of missing data as well as completeness of drug cell-screening data. These studies established a foundation for SOM cluster analysis of the complete set of NCIs publicly available antitumor drug- screening data. This analysis identified relationships between chemotypes of screened agents and their effect on four major classes of cellular activities: mitosis, nucleic acid synthesis, membrane transport and integrity, and phosphatase- and kinase-mediated cell cycle regulation. Validations of these cellular activities, obtained from literature sources, found (i) strong evidence supporting within cluster memberships and shared cellular activity, (ii) indications of compound selectivity between various types of cellular activity, and (iii) strengths and weaknesses of the NCI's antitumor drug screen data for assigning compounds to these classes of cellular activity. Subsequent analyses of averaged responses within these tumor panel types find a strong dependence on chemotype for coherence among cellular response patterns. The advantages of a global analysis of the complete screening data set are discussed. 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Med. Chem. PY 2002 PD FEB 14 VL 45 IS 4 GA 523YN PI WASHINGTON RP Rabow AA NCI, Sci Applicat Int Corp, NIH, DCTD, Frederick, MD 21702 USA J9 J MED CHEM PA 1155 16TH ST, NW, WASHINGTON, DC 20036 USA UT ISI:000173985300008 ER PT Journal AU Tobiska, WK TI Validating the solar EUV proxy, E-10.7 SO JOURNAL OF GEOPHYSICAL RESEARCH-SPACE PHYSICS LA English DT Article NR 14 SN 0148-0227 PU AMER GEOPHYSICAL UNION C1 Space Wx Space Environm Technol, Los Angeles, CA USA Space Wx Space Environm Technol, Los Angeles, CA USA ID IRRADIANCE MODEL; FLUX MODEL AB A demonstration of the improvement in thermospheric densities using the daily E-10.7 proxy compared to F-10.7 is shown. The daily altitude decay for the Solar Mesosphere Explorer (SME) satellite from April 1, 1982 through August 9, 1983, using both proxies and the actual altitude data, are compared. The F-10.7 case finished 2 km lower than each of the E-10.7 and actual altitude cases which were nearly identical. During active solar conditions, daily F-10.7 can overestimate the EUV energy input into the atmosphere by up to 60% and also underestimate it by as much as 50%. Progress is shown towards validating E-10.7 as a more accurate proxy compared to F-10.7 for use in atmospheric density calculations that are applicable to satellite drag problems. In support of the validation of the E-10.7 proxy, an operational prototype hardware/software platform for visualizing of the near-Earth space environment was created. This platform uses a data-driven, data visualization environment. Platform development continues so as to accommodate not only historical data but also nowcast and forecast data streams. Upgrades to the SOLAR2000 Research Grade model are continuing in order to improve the correlation coefficients from multiple linear regressions in several wavelength regions. E-10.7 is used in applications that incorporate F-10.7, including empirical thermospheric models, ionospheric models, and general representations of solar activity ranging from climate research to engineering applications. CR COVINGTON AE, 1948, P IRE, V36, P454 HINTEREGGER HE, 1981, GEOPHYS RES LETT, V8, P1147 LEAN J, 1987, J GEOPHYS RES-ATMOSP, V92, P839 LEAN J, 1991, REV GEOPHYS, V29, P505 NUSINOV AA, 1984, GEOMAGN AERON, V24, P439 RICHARDS PG, 1994, J GEOPHYS RES, V99, P8981 ROTTMAN GJ, 1987, P WORKSH SOL RAD OUT, P71 SCHMIDTKE G, 1976, GEOPHYS RES LETT, V3, P573 TOBISKA WK, 2000, J ATMOS SOL-TERR PHY, V62, P1233 TOBISKA WK, 1991, J ATMOS TERR PHYS, V53, P1005 TOBISKA WK, 1993, J GEOPHYS RES, V98, P18879 TOBISKA WK, 2000, PHYS CHEM EARTH PT C, V25, P383 TOBISKA WK, 1998, SOL PHYS, V177, P147 TOBISKA WK, 1988, THESIS U COLORADO BO TC 0 BP 29969 EP 29978 PG 10 JI J. Geophys. Res-Space Phys. PY 2001 PD DEC 1 VL 106 IS A12 GA 519LV PI WASHINGTON RP Tobiska WK Space Wx Space Environm Technol, Los Angeles, CA USA J9 J GEOPHYS RES-SPACE PHYS PA 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA UT ISI:000173728500096 ER PT Journal AU Schageman, JJ Basit, M Gallardo, TD Garner, HR Shohet, RV TI MarC-V: A spreadsheet-based tool for analysis, normalization, and visualization of single cDNA microarray experiments SO BIOTECHNIQUES LA English DT Article NR 9 SN 0736-6205 PU EATON PUBLISHING CO C1 Univ Texas, SW Med Ctr, Mcdermott Ctr Human Growth & Dev, 5323 Harry Hines Blvd, Dallas, TX 75390 USA Univ Texas, SW Med Ctr, Mcdermott Ctr Human Growth & Dev, Dallas, TX 75390 USA Univ Texas, SW Med Ctr, Ryburn Cardiol Ctr, Dallas, TX 75390 USA ID EXPRESSION PATTERNS; GENE-EXPRESSION AB The comprehensive analysis and visualization of data extracted from cDNA microarrays can be a time-consuming and error-prone process that becomes increasingly tedious with increased number of gene elements on a particular microarray. With the increasingly large number of gene elements on today's microarrays, analysis tools must be developed to meet this challenge. Here, we present MarC-V a Microsoft(R) Excel(R) spreadsheet tool with Visual Basic(R) macros to automate much of the visualization and calculation involved in the analysis process while providing the familiarity and flexibility of Excel. Automated features of this tool include (i) lower-bound thresholding, (ii) data normalization, (iii) generation of ratio frequency distribution plots, (iv) generation of scatter plots color-coded by expression level, (v) ratio scoring based on intensity measurements, (vi)filtering of data based on expression level or specific gene interests, and (vii) exporting data for subsequent multi-array analysis. MarC-V also, has an importing function included for GenePix(R) results (GPR) raw data files. CR CHENG L, 2001, P NATL ACAD SCI USA, V98, P31 DYSVIK B, 2001, BIOINFORMATICS, V17, P369 EISEN MB, 1998, P NATL ACAD SCI USA, V95, P14863 EPSTEIN CB, 2001, METHOD CELL BIOL, V65, P439 HEGDE P, 2000, BIOTECHNIQUES, V29, P548 PATRIOTIS PC, 2001, BIOTECHNIQUES, V31, P862 SCHENA M, 1995, SCIENCE, V270, P467 TAMAYO P, 1999, P NATL ACAD SCI USA, V96, P2907 TUSHER VG, 2001, P NATL ACAD SCI USA, V98, P5116 TC 0 BP 338 EP + PG 5 JI Biotechniques PY 2002 PD FEB VL 32 IS 2 GA 520EK PI NATICK RP Schageman JJ Univ Texas, SW Med Ctr, Mcdermott Ctr Human Growth & Dev, 5323 Harry Hines Blvd, Dallas, TX 75390 USA J9 BIOTECHNIQUES PA 154 E. CENTRAL ST, NATICK, MA 01760 USA UT ISI:000173767500019 ER PT Journal AU Slottow, J Shahriari, A Stein, M Chen, X Thomas, C Ender, PB TI Instrumenting and tuning dataView - a networked application for navigating through large scientific datasets SO SOFTWARE-PRACTICE & EXPERIENCE LA English DT Article NR 10 SN 0038-0644 PU JOHN WILEY & SONS LTD C1 Univ Calif Los Angeles, Acad Technol Serv, Box 951557, Los Angeles, CA 90095 USA Univ Calif Los Angeles, Acad Technol Serv, Los Angeles, CA 90095 USA DE parallel visualization; scientific data visualization; virtual world; tuning; network performance; cluster; MPI; master slave; parallel code performance AB DataView is an application that allows scientists to fly visually through large, regularly-gridded, time-varying 3D datasets from their desktop computers. DataView works with data that has been divided into cubes and sub-cubes (which we call 'tiles' and 'subtiles'), sampled at three levels of detail and written to a terabyte data server built on a PC cluster. dataView is a networked application. The dataView client component that runs on the scientist's computer is used only for user interaction and rendering. The selection of data subtiles for any given scene, and the geometry computation performed on those subtiles to create the virtual world, are performed by dataView components run in parallel on nodes of the PC cluster. This paper describes how we instrumented and tuned the code for improved performance in a networked environment. We report on how we measured network performance, first by inducing network delay and then by running the dataView client component in Washington DC and the compute components in Los Angeles. We report on the effect that the size, level of detail, and client CPU speed have on performance. We analyze what happens when the geometry computation is performed in parallel using MPI (Message Passing Interface) vs. in serial, and discuss the effect on performance of adding additional computational nodes. Copyright (C) 2001 John Wiley Sons, Ltd. CR USERS GUIDE TCP WIND CHIANG YJ, 1997, P VISUALIZATION 97, P293 COX M, 1997, P IEEE VISUALIZATION, P235 ECKEL J, 2000, OPENGL PERFORMER GET LAW CC, 1999, P VIS 99, P225 MAHDAVI J, ENABLING HIGH PERFOR SCHROEDER W, 1998, VISUALIZATION TOOLKI SLOTTOW J, 2000, P SOC PHOTO-OPT INS, V3960, P14 TIERNEY B, TCP TUNING GUID DIST WESTPHAL H, 1994, INT C EXH P MUN GERM, V2, P36 TC 0 BP 165 EP 190 PG 26 JI Softw.-Pract. Exp. PY 2002 PD FEB VL 32 IS 2 GA 518AV PI W SUSSEX RP Slottow J Univ Calif Los Angeles, Acad Technol Serv, Box 951557, Los Angeles, CA 90095 USA J9 SOFTWARE-PRACT EXP PA BAFFINS LANE CHICHESTER, W SUSSEX PO19 1UD, ENGLAND UT ISI:000173644600004 ER PT Journal AU Russomanno, DJ Hicks, K TI A Prolog-based centroid algorithm for isovolume extraction from finite element torso simulations SO COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE LA English DT Article NR 18 SN 0169-2607 PU ELSEVIER SCI IRELAND LTD C1 Univ Memphis, Dept Elect & Comp Engn, Campus Box 526574, Memphis, TN 38152 USA Univ Memphis, Dept Elect & Comp Engn, Memphis, TN 38152 USA Rice Univ, Dept Bioengn, Houston, TX 77251 USA DE Prolog; defibrillation; isovolume extraction; data visualization; centroid ID DEFIBRILLATION AB Computer modeling and simulation of the human torso provides a rapid and non-invasive means to observe the effects of implanted defibrillators. The objective of this study was to improve a method of extracting data from an implanted defibrillator simulation for subsequent visualization. Electrical quantities, such as the potential and gradient fields, are computed at points throughout various regions of a three-dimensional (3-D) torso model via a finite element solution. Software is then implemented in the Prolog language to extract and visualize a subset of the data, from within any subregion of the model, satisfying a given declarative constraint. In past work, membership in these subsets had been determined solely by the electrical quantities at the vertices of the tetrahedral elements within the model along with an arbitrary choice made by the user. However, this study expands upon previous work to utilize an alternative means of classification, calculating the centroid of each tetrahedron and assigning electrical properties to these centroids based on the distances of each centroid to the four corners of the tetrahedron. After the modifications, it is expected that the extracted subsets of the model will represent the data in a more realistic and conservative manner and provide more insight into the process of defibrillation than previous methods of data extraction rind visualization. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved. 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PY 2002 PD FEB VL 67 IS 2 GA 519XD PI CLARE RP Russomanno DJ Univ Memphis, Dept Elect & Comp Engn, Campus Box 526574, Memphis, TN 38152 USA J9 COMPUT METHOD PROGRAM BIOMED PA CUSTOMER RELATIONS MANAGER, BAY 15, SHANNON INDUSTRIAL ESTATE CO, CLARE, IRELAND UT ISI:000173750000002 ER PT Journal AU Heckerman, D Chickering, DM Meek, C Rounthwaite, R Kadie, C TI Dependency networks for inference, collaborative filtering, and data visualization SO JOURNAL OF MACHINE LEARNING RESEARCH LA English DT Article NR 29 SN 1532-4435 PU M I T PRESS C1 One Microsoft Way, Microsoft Res, Redmond, WA 98052 USA One Microsoft Way, Microsoft Res, Redmond, WA 98052 USA DE dependency networks; Bayesian networks; graphical models; probabilistic inference; data visualization; exploratory data analysis; collaborative filtering; Gibbs sampling ID STATISTICAL-ANALYSIS; SYSTEMS AB We describe a graphical model for probabilistic relationships- an alternative to the Bayesian network-called a dependency network. 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PY 2001 PD WIN VL 1 IS 1 GA 512RE PI CAMBRIDGE RP Heckerman D One Microsoft Way, Microsoft Res, Redmond, WA 98052 USA J9 J MACH LEARN RES PA FIVE CAMBRIDGE CENTER, CAMBRIDGE, MA 02142 USA UT ISI:000173336700002 ER PT Journal AU Yin, HJ TI ViSOM - A novel method for multivariate data projection and structure visualization SO IEEE TRANSACTIONS ON NEURAL NETWORKS LA English DT Article NR 22 SN 1045-9227 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 Univ Manchester, Inst Sci & Technol, Dept Elect Engn & Elect, Manchester M60 1QD, Lancs, England Univ Manchester, Inst Sci & Technol, Dept Elect Engn & Elect, Manchester M60 1QD, Lancs, England DE dimension reduction; multidimensional scaling; multivariate data visualization; nonlinear mapping; self-organizing maps (SOMs) ID PRINCIPAL-COMPONENT ANALYSIS; SELF-ORGANIZING MAPS; NEURAL NETWORKS AB When used for visualization of high-dimensional data, the self- organizing map (SOM) requires a coloring scheme such as the U- matrix to mark the distances between neurons. Even so, the structures of the data clusters may not be apparent and their shapes are often distorted. In this paper, a visualization- induced SOM (ViSOM) is proposed to overcome these shortcomings. The algorithm constrains and regularizes the inter-neuron distance with a parameter that controls the resolution of the map. The mapping preserves the inter-point distances of the input data on the map as well as the topology. It produces a graded mesh in the data space such that the distances between mapped data points on the map resemble those in the original space, like in the Sammon mapping. However, unlike the Sammon mapping, the ViSOM can accommodate both training data and new arrivals and is much simpler in computational complexity. Several experimental results and comparisons with other methods are presented. 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PY 2002 PD JAN VL 13 IS 1 GA 514LE PI NEW YORK RP Yin HJ Univ Manchester, Inst Sci & Technol, Dept Elect Engn & Elect, Manchester M60 1QD, Lancs, England J9 IEEE TRANS NEURAL NETWORKS PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000173440100021 ER PT Journal AU Anderson, JT Gregory, RS Collins, WT TI Acoustic classification of marine habitats in coastal Newfoundland SO ICES JOURNAL OF MARINE SCIENCE LA English DT Article NR 7 SN 1054-3139 PU ACADEMIC PRESS LTD C1 Fisheries & Oceans Canada, Dept Fisheries & Ocean, POB 5667, St Johns, NF A1C 5X1, Canada Fisheries & Oceans Canada, Dept Fisheries & Ocean, St Johns, NF A1C 5X1, Canada Quester Tangent Corp, Sidney, BC V8L 5Y8, Canada DE acoustic; classification; coastal; cod; habitats; juvenile; Newfoundland; seabed AB A digital acoustic seabed classification system, QTC Vies (Series IV) was used in the coastal waters of Newfoundland to characterize and classify marine benthic habitats. The QTC View system was calibrated in Placentia Bay at sites identified independently during a submersible research program. Four different habitats were used for calibration of the QTC View system: mud, gravel, rock, and macroalgae on rock. These different habitats were used as a "training" catalogue for real-time classification of marine habitats carried out in Bonavista Bay. The classification data were based on over 2000 km of survey tracks ranging in depth from approximately 10-m to 220-m depth. Post classification analyses were carried out using data visualization techniques. simultaneously comparing the classification data in mathematical and geographic settings. Following post classification, eight different marine habitats were identified using the acoustic system: mud, loose gravel, gravel, rock, sparse algae/cobble, macroalgae, high relief/deep cobble, and wood chips. Throughout the surveyed area, rock habitat dominated, followed by sparse algae/cobble and high relief/cobble habitat types. The wood chip habitat type was identified within a small area that historically had been associated with logging in coastal Newfoundland. CR *GOLD SOFTW INC, 1999, SURF MAPP SYST V7 ANDERSON JT, 2001, IN PRESS SPATIAL PRO GREENSTREET SPR, 1997, ICES J MAR SCI, V54, P939 GREGORY RS, 1997, MAR ECOL-PROG SER, V146, P9 GREGORY RS, 1997, NAFO SCI COUNC STUD, V29, P3 SOTHERAN IS, 1997, ESTUAR COAST SHELF A, V44, P25 TODD BJ, 1999, MAR GEOL, V162, P165 TC 0 BP 156 EP 167 PG 12 JI ICES J. Mar. Sci. PY 2002 PD FEB VL 59 IS 1 GA 513BV PI LONDON RP Anderson JT Fisheries & Oceans Canada, Dept Fisheries & Ocean, POB 5667, St Johns, NF A1C 5X1, Canada J9 ICES J MAR SCI PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND UT ISI:000173358800015 ER PT Journal AU Melvin, G Li, YC Mayer, L Clay, A TI Commercial fishing vessels, automatic acoustic logging systems and 3D data visualization SO ICES JOURNAL OF MARINE SCIENCE LA English DT Article NR 31 SN 1054-3139 PU ACADEMIC PRESS LTD C1 Fisheries & Oceans Canada, Marine Fish Div, Biol Stn, 531 Brandy Cove Rd, St Andrews, NB E5B 2L9, Canada Fisheries & Oceans Canada, Marine Fish Div, Biol Stn, St Andrews, NB E5B 2L9, Canada Univ New Brunswick, Dept Geodesy & Geomat Engn, Fredericton, NB E3B 5A3, Canada Univ New Hampshire, Ctr Coastal & Ocean Mapping, Chase Ocean Engn Lab, Durham, NH 03824 USA Femto Elect Ltd, Lr Sackville, NS B4C 3J1, Canada DE Atlantic herring; fishing vessels; hydroacoustics; surveying; stock assessment; visualization ID ECHO-SOUNDER; BEHAVIOR; SCHOOLS; SONAR; SEA AB Over the past five years we have investigated and used commercial fishing vessels and their associated acoustic hardware as platforms for acoustic surveying and data collection. During this period we developed an automated acoustic logging system that will simultaneously record data from the ship's existing sounder, sonar, and navigation systems. The system was designed to be self contained and easy to activate. Once calibrated, the vessel's vertical echo sounder can be used for quantitative fish biomass estimates in a manner similar to a scientific echo sounder. Sonar data are collected in the form of digital images with a navigation file header. Post processing, editing, and visualization tools were developed to scale the sonar images according to range setting and tilt angle. Thereafter, both the sounder and sonar data are combined into a 3D visualization package for presentation, observation, and school area estimates. Industry based acoustic surveys of herring spawning grounds have been used to estimate spawning stock biomass and for near real-time decisions regarding harvest levels in NAFO Statistical Division 4WX since 1997. Currently, there are eight systems deployed on commercial purse seiners within the region. For the past four years data from structured surveys and fishing excursions have played a key role in the assessment of herring spawning stock biomass. While the application of the technology has been driven by a stock assessment mandate, its potential use is more far reaching. The spatial nature the data means that detailed and quantitative studies of fish behaviour, vessel avoidance, fish distribution, and target area can be undertaken from commercial fishing vessels with the addition of minimal equipment. However, quantification of sonar images is restricted to area/volume estimates as no digital amplitude data are available from the commercial fishing units. CR AGLEN A, 1994, MARINE FISH BEHAV CA, P107 BRANDT SB, 1996, FISHERIES TECHNIQUES, P385 CLAY A, 1998, 9896 CAN STOCK ASS S CRAIG RE, 1969, FISKDIR SKR SER HAVU, V15, P210 DINER N, 1987, FISH SCH BEHAV ECHO ENGAS A, 1995, FISH RES, V22, P243 FOOTE KG, 1987, 144 ICES COOP RES FOOTE KG, 1987, J ACOUST SOC AM, V82, P981 FOOTE KG, 1986, J ACOUST SOC AM, V80, P612 FORBES ST, 1972, FAO MANAGEMENT FISHE, V5 FREON P, 1990, EVALUATION INFLUENCE GERLOTTO F, 1994, EXHAUSTIVE OBSERVATI MACLENNAN DN, 1992, FISH FISHERIES SERIE, V5 MAYER L, 1998, APPL MULTIBEAM SONAR MAYER L, 2001, ICES J MARINE SCI MELVIN GD, 2000, 1999 HERR AC SURV NA MELVIN GD, 1998, 9881 DFO ATL FISH RE MELVIN GD, 2001, IN PRESS P HERR 2000 MISRUND OA, 1996, ICES J MAR SCI, V53, P383 MISUND OA, 1992, ICES J MAR SCI, V49, P325 MISUND OA, 1997, REV FISH BIOL FISHER, V7, P1 MOHR H, 1971, MODERN FISHING GEAR, V3, P368 PITCHER TJ, 1996, ICES J MAR SCI, V53, P449 SIMARD Y, 1997, CANADIAN TECHNICAL R, V2174 SIMMONDS EJ, 1992, ICES COOPERATIVE RES SIMMONDS EJ, 1996, ICES J MAR SCI, V53, P1054 SORIA M, 1996, ICES J MAR SCI, V53, P453 STEPHENSON RL, 1995, 95083 DFO ATL FISH R STEPHENSON RL, 1996, 9761 DFO FISH RES STEPHENSON RL, 1998, 9852 DFO ATL FISH RE STEPHENSON RL, 1999, ICES J MAR SCI, V56, P1005 TC 0 BP 179 EP 189 PG 11 JI ICES J. Mar. Sci. PY 2002 PD FEB VL 59 IS 1 GA 513BV PI LONDON RP Melvin G Fisheries & Oceans Canada, Marine Fish Div, Biol Stn, 531 Brandy Cove Rd, St Andrews, NB E5B 2L9, Canada J9 ICES J MAR SCI PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND UT ISI:000173358800017 ER PT Journal AU Megrey, BA Hinckley, S Dobbins, EL TI Using scientific visualization tools to facilitate analysis of multi-dimensional data from a spatially explicit, biophysical, individual-based model of marine fish early life history SO ICES JOURNAL OF MARINE SCIENCE LA English DT Article NR 20 SN 1054-3139 PU ACADEMIC PRESS LTD C1 NOAA, Natl Marine Fisheries Serv, Alaska Fisheries Sci Ctr, 7600 Sand Point Way, Seattle, WA 98115 USA NOAA, Natl Marine Fisheries Serv, Alaska Fisheries Sci Ctr, Seattle, WA 98115 USA Univ Washington, Joint Inst Study Atmosphere & Oceans, Seattle, WA 98115 USA DE biophysical simulation model; data visualization; fish larvae; scientific computing ID PLANKTON CONTACT RATES; SMALL-SCALE TURBULENCE; EDDY-RESOLVING MODEL; OF-ALASKA SHELF; CIRCULATION AB Individual-based models (IBM), as an ecological modeling paradigm, are being used widely in the analysis of fish populations in marine ecosystems. The flexibility and power of IBMs with respect to building detailed and realistic biological models have encouraged recent and important extensions, which include explicit spatial dynamics and biophysical forcing of certain life stage processes. Unfortunately, the usefulness of individual-based numerical simulation models is often negated by the difficulty in digesting and analyzing their voluminous and complicated output. Scientific visualization tools offer the capability to remedy this problem. In this paper we briefly describe our spatially explicit, biophysical, individual-based model, its data input and output characteristics and the off- the-shelf visualization tools we used to help facilitate analysis and interpretation of the model. A stand-alone, easy- to-use, post-processing, graphic user interface is described that permits rapid examination and integrated visualization of mufti-dimensional model output. Specific examples are provided showing how scientific visualization, as a research tool, provided valuable assistance in untangling complex model dynamics assisted with diagnostic analyses related to model validation, helped investigate trends, and apparent oddities in the data, and facilitated the communication of model results. CR DEANGELIS DL, 1992, INDIVIDUAL BASED MOD FRIEDHOFF RM, 1989, 2 COMPUTER REVOLUTIO HAIDVOGEL DB, 1991, J COMPUT PHYS, V94, P151 HAMMING RW, 1962, NUMERICAL METHODS SC HERMANN AJ, 1996, FISH OCEANOGR S1, V5, P39 HERMANN AJ, 2001, ICES J MAR SCI, V58, P1030 HERMANN AJ, 1996, J GEOPHYS RES-OCEANS, V101, P1129 HINCKLEY S, 2001, ICES J MAR SCI, V58, P1042 HINCKLEY S, 1996, MAR ECOL-PROG SER, V139, P47 HINCKLEY S, 1999, THESIS U WASHINGTON HUSTON M, 1988, BIOSCIENCE, V38, P682 JUDSON OP, 1994, TRENDS ECOL EVOL, V9, P9 MACKENZIE BR, 1995, LIMNOL OCEANOGR, V40, P1278 MACKENZIE BR, 1994, LIMNOL OCEANOGR, V39, P1790 MACKENZIE BR, 1991, MARINE ECOLOGY PROGR, V94, P207 MEGREY BA, 2001, ICES J MAR SCI, V58, P1015 ORR JN, 1990, COMPUTER GRAPHIC JUL, P80 ROTHSCHILD BJ, 1988, J PLANKTON RES, V10, P465 STABENO PJ, 1996, J GEOPHYS RES-OCEANS, V101, P1151 SUNDBY S, 1990, J PLANKTON RES, V12, P1153 TC 0 BP 203 EP 215 PG 13 JI ICES J. Mar. Sci. PY 2002 PD FEB VL 59 IS 1 GA 513BV PI LONDON RP Megrey BA NOAA, Natl Marine Fisheries Serv, Alaska Fisheries Sci Ctr, 7600 Sand Point Way, Seattle, WA 98115 USA J9 ICES J MAR SCI PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND UT ISI:000173358800019 ER PT Journal AU Basu, A Malhotra, S TI Error detection of bathymetry data by visualization using GIS SO ICES JOURNAL OF MARINE SCIENCE LA English DT Article NR 14 SN 1054-3139 PU ACADEMIC PRESS LTD C1 Univ Hawaii, Pacific Mapping Program, 2525 Correa Rd,HIG 407A, Honolulu, HI 96822 USA Univ Hawaii, Pacific Mapping Program, Honolulu, HI 96822 USA DE bathymetry; data errors; data visualization; GIS; Hawaiian Islands AB Graphical methods are very efficient means for error detection in large volumes of spatial data. Bathymetry data form the basis for nautical charts that are used by the fishing industry to utilize and manage the fishing resources, and by the fishing communities to study migration and habitat studies of fish. The bathymetry data of the world have been collected over a century and have a wide range of resolution and accuracy. The Geographic Information System (GIS) is a powerful toot to process, analyze, manage, and display spatial data. Marine GIS provides a mechanism for recording data with navigation, creating an efficient digital database, and plotting data on maps. Here, we have used ArcView 3.2 GIS software and its 3D Analyst extension module to visualize bathymetry data that were collected around the Hawaiian Islands. We have also identified some highly erroneous bathymetry data in this data set. (C) 2002 International Council for the Exploration of the Sea. CR *ESRI, 1996, ARCV GIS BASU A, IN PRESS MARINE GEOD BASU A, 1999, MAR GEOD, V22, P249 BASU A, 1997, MAR GEOD, V20, P255 BASU A, 1998, SURVEYING LAND INFOR, V58, P147 BEARD MK, 1999, GEOGRAPHICAL INFORMA, V1, P219 BLONDEL P, 1997, HDB SEAFLOOR SONAR I COX DR, 1978, APPL STAT, V27, P9 ESTEP L, 1993, HYDROGRAPHIC J, V67, P25 KLEINROCK MC, 1992, CRC HDB GEOPHYSICAL, P35 LEE SM, 2000, MAR GEOD, V23, P31 LEICK A, 1990, GPS SATELLITE SURVEY SMITH WHF, 1993, J GEOPHYS RES-SOLID, V98, P9591 WHITMAN EC, 1996, SEA TECHNOL, V37, P95 TC 0 BP 226 EP 234 PG 9 JI ICES J. Mar. Sci. PY 2002 PD FEB VL 59 IS 1 GA 513BV PI LONDON RP Basu A Univ Hawaii, Pacific Mapping Program, 2525 Correa Rd,HIG 407A, Honolulu, HI 96822 USA J9 ICES J MAR SCI PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND UT ISI:000173358800021 ER PT Journal AU Inselberg, A TI Visualization and data mining of high-dimensional data SO CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS LA English DT Article NR 22 SN 0169-7439 PU ELSEVIER SCIENCE BV C1 Tel Aviv Univ, Sch Math Sci, IL-69978 Tel Aviv, Israel Tel Aviv Univ, Sch Math Sci, IL-69978 Tel Aviv, Israel Multidimens Graphs Ltd, IL-43556 Raanana, Israel DE visualization; data mining; high-dimensional data ID PARALLEL AB Visualization provides insight through images and can be considered as a collection, of application specific mappings: ProblemDomain --> VisualRange. For the visualization of multivariate problems a multidimensional system of parallel coordinates (abbreviated as parallel to -coords) is constructed which induces a one-to-one mapping between subsets of N-space and subsets of 2-space. The result is a rigorous methodology for doing and seeing N-dimensional geometry. Starting with an the overview of the mathematical foundations, it is seen that the display of high-dimensional datasets and search for multivariate relations among the variables is transformed into a 2-D pattern recognition problem. This is the basis for the application to Visual Data Mining which is illustrated with real dataset of Very Large Scale Integration (VLSI-"chip") production. Then a recent geometric classifier is presented and applied to three real datasets. The results compared to those of 23 other classifiers have the least error. The algorithm has quadratic computational complexity in the size and number of parameters, provides comprehensible and explicit rules, does dimensionality selection-where the minimal set of original variables required to state the rule is found-and orders these variables so as to optimize the clarity of separation between the designated set and its complement. Finally, a simple visual economic model of a real country is constructed and analyzed in order to illustrate the special strength of parallel to -coords in modeling multivariate relations by means of hypersurfaces. (C) 2002 Published by Elsevier Science B.V. CR 1983, VISUAL DISPLAY QUANT AVIDAN T, 1998, COMPUT STAT, V13, P635 BASETT EW, 1995, IND COMPUT, V14, P23 BOLTYANSKII VG, 1964, ENVELOPES CHATTERJEE A, 1995, THESIS USC COXETER HSM, 1992, REAL PROJECTIVE PLAN DETERDING DH, 1989, THESIS CAMBRIDGE U EICKEMEYER J, 1992, THESIS UCLA FAYAD UM, 1996, ADV KNOWLEDGE DISCOE INSELBERG A, 1999, COMPUTATION STAT, V14, P53 INSELBERG A, 1987, J ASSOC COMPUT MACH, V34, P765 INSELBERG A, 1999, P 1999 IEEE INT C IN, P112 INSELBERG A, 1994, SIAM J APPL MATH, V54, P578 KEIM DA, 1996, IEEE T KNOWL DATA EN, V8, P923 MARTIN AR, 1995, P IEEE C VIS ATL GA, P271 MITCHELL TM, 1997, MACHINE LEARNING QUINLAN JR, 1993, C4 5 PROGRAMS MACHIN ROBINSON AC, 1998, P 1 EUR NEUR NETW SCHMID C, 1994, P 7 SSDBM IEEE COMP SHANG N, 1996, P 1 INT C NEUR INF P, V133 SPIEGELHALTER DJ, 1994, ELLIS HORWOOD SERIES SWAYNE DF, 1998, J COMPUT GRAPH STAT, V7, P113 TC 0 BP 147 EP 159 PG 13 JI Chemometrics Intell. Lab. Syst. PY 2002 PD JAN 28 VL 60 IS 1-2 GA 514UJ PI AMSTERDAM RP Inselberg A Tel Aviv Univ, Sch Math Sci, IL-69978 Tel Aviv, Israel J9 CHEMOMETR INTELL LAB SYST PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000173459200013 ER PT Journal AU Lumley, T Sutherland, P Rossini, A Lewin-Koh, N Cook, D Cox, Z TI Visualising high-dimensional data in time and space: ideas from the Orca project SO CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS LA English DT Article NR 14 SN 0169-7439 PU ELSEVIER SCIENCE BV C1 Univ Washington, NRCSE, Box 357232, Seattle, WA 98195 USA Univ Washington, NRCSE, Seattle, WA 98195 USA Univ Washington, Ctr AIDS Res, Seattle, WA 98195 USA Iowa State Univ, Iowa City, IA USA DE visualization; high-dimensional data; Orca project ID VISUALIZATION; SYSTEM AB Environmental data are frequently high-dimensional with measurements of multiple chemical constituents, plant or animal species, or meteorological variables. Environmental data are also frequently structured with interest in the patterns of variation over time and space. We describe some new data visualization methods from the Orca project that allow the analyst to reduce the dimension of the data without obscuring its basic structure and illustrate these on air pollution data. (C) 2002 Elsevier Science B.V. All rights reserved. CR ANSELIN L, 1996, SPATIAL ANAL PERSPEC, P111 ASIMOV D, 1985, SIAM J SCI STAT COMP, V6, P128 BUJA A, 1986, COMPUTING SCI STAT, V17, P63 COOK D, 1997, J STAT SOFTWARE, V2 GANSNER ER, 2000, SOFTWARE PRACT EXPER, V30, P1203 HIBBARD W, 2000, IN PRESS COMMUNICATI LUMLEY T, 2000, J COMPUT GRAPH STAT, V9, P738 NORRIS G, 1998, THESIS U WASHINGTON SEN PK, 1993, LARGE SAMPLE METHODS SHEPPARD L, 1999, EPIDEMIOLOGY, V10, P23 SWAYNE DF, 1998, J COMPUT GRAPH STAT, V7, P113 SYMANZIK J, 1996, COMPUTING SCI STAT, V28, P369 SYMANZIK J, 1994, COMPUTING SCI STAT, V26, P431 WILLS GJ, 1999, J COMPUT GRAPH STAT, V8, P190 TC 0 BP 189 EP 195 PG 7 JI Chemometrics Intell. Lab. Syst. PY 2002 PD JAN 28 VL 60 IS 1-2 GA 514UJ PI AMSTERDAM RP Lumley T Univ Washington, NRCSE, Box 357232, Seattle, WA 98195 USA J9 CHEMOMETR INTELL LAB SYST PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000173459200016 ER PT Journal AU Holmquist, S Narayanan, NH TI An integrated architecture for tightly coupled design and evaluation of educational multimedia SO INFORMATION SCIENCES LA English DT Article NR 17 SN 0020-0255 PU ELSEVIER SCIENCE INC C1 Auburn Univ, Dept Comp Sci & Software Engn, Intelligent & Interact Syst Lab, Auburn, AL 36849 USA Auburn Univ, Dept Comp Sci & Software Engn, Intelligent & Interact Syst Lab, Auburn, AL 36849 USA Univ Los Andes, Fac Ingn, Dept Calculo, Merida, Venezuela DE educational multimedia; iterative design; interaction data visualization; authoring; evaluation ID SYSTEM AB Multimedia technology is increasingly being used to create instructional environments for distance education. However, the design of educational multimedia is currently driven more by intuition than by empirically or theoretically derived design guidelines. In the absence of prescriptive design principles, iterative design - a cyclical process of design, test and redesign - becomes critically important for creating effective educational multimedia. This paper proposes a framework for the iterative design of a class of educational multimedia called Hypermedia Educational Manuals. The architecture of an authoring and evaluation platform designed to support this framework and ease the designer's task is presented. Two unique features of this tool are the automatic creation of a system structure definition of the multimedia system being authored, and the automatic incorporation of interact ion-logging elements in the system. This is done by an authoring component that assists the designer with the design and implementation of educational multimedia. An evaluation component then parses both the system structure definition and interaction logs created while students work with the multimedia system, in order to generate statistical analyses and graphical visualizations of how students interacted with the multimedia. The integrated architecture of the tool embodies a tight coupling between the design of educational multimedia and the evaluation of its effectiveness. This coupling reduces the time and effort required in repeating evaluation-redesign cycles in order to iteratively refine the initial design. We also briefly describe an experimental demonstration of the utility of this tool. (C) 2002 Elsevier Science Inc. All rights reserved. CR CHAVERO JC, 1998, J ED MULTIMEDIA HYPE, V7, P33 CHEN MS, 1998, IEEE T KNOWL DATA EN, V10, P209 FARADAY PM, 1998, P CHI 98 HUM FACT CO, P124 HARDMAN L, 1994, COMMUN ACM, V37, P50 HIX D, 1993, DEV USER INTERFACES HOLMQUIST S, 1999, THESIS AUBURN U LAWLESS KA, 1998, J ED MULTIMEDIA HYPE, V7, P51 LEE S, 1997, COMPUT EDUC, V29, P89 NARAYANAN NH, 1998, INT J HUM-COMPUT ST, V48, P267 PATERNO F, 1999, EMPIRICAL SOFTWARE E, V4, P11 RECKER MM, 1994, P WORLD C ED MULT HY ROSSI G, 1999, J DIGITAL INFORMATIO, V1 SHIH TK, 1997, IEEE MULTIMEDIA, V4, P67 SIOCHI AC, 1991, ACM T INFORM SYST, V9, P309 TRUDEL CI, 1996, INT J HUM-COMPUT ST, V45, P723 VANROSSUM G, 1993, P ACM MULTIMEDIA 93, P183 YAMADA S, 1995, ACM T COMPUTER HUMAN, V2, P284 TC 0 BP 127 EP 152 PG 26 JI Inf. Sci. PY 2002 PD JAN VL 140 IS 1-2 GA 512BU PI NEW YORK RP Narayanan NH Auburn Univ, Dept Comp Sci & Software Engn, Intelligent & Interact Syst Lab, Auburn, AL 36849 USA J9 INFORM SCIENCES PA 655 AVENUE OF THE AMERICAS, NEW YORK, NY 10010 USA UT ISI:000173301900007 ER PT Journal AU Conway, T Kraus, B Tucker, DL Smalley, DJ Dorman, AF McKibben, L TI DNA array analysis in a Microsoft (R) Windows (R) environment SO BIOTECHNIQUES LA English DT Article NR 20 SN 0736-6205 PU EATON PUBLISHING CO C1 Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73069 USA Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73069 USA ID ESCHERICHIA-COLI; GENE-EXPRESSION; FUNCTIONAL GENOMICS; MICROARRAYS AB Microsoft(R) Windows(R)-based computers have evolved to the point that they provide sufficient computational and visualization power for robust analysis of DNA array data. In fact, smaller laboratories might prefer to carry out some or all of their analyses and visualization in a Windows environment, rather than alternative platforms such as UNIX. We have developed a series of manually executed macros written in Visual Basic for Microsoft Excel(R) spreadsheets, that allows for rapid and comprehensive gene expression data analysis. The first macro assigns gene names to spots on the DNA array and normalizes individual hybridizations by expressing the signal intensity for each gene as a percentage of the stint of all gene intensities. The second macro streamlines statistical consideration of the confidence in individual gene measurements for sets of experimental replicates by calculating probability values with the Student's t test. The third macro introduces a threshold value, calculates expression ratios between experimental conditions, and calculates the standard deviation of the mean of the log ratio values. Selected columns of data are copied by a fourth macro to create a processed data set suitable for entry into a Microsoft Access(R) database. An Access database structure is described that allows simple queries across multiple experiments and export of data into third-party data visualization software packages. These analysis tools can be used in their present form by others working with commercial E. coli membrane arrays, or they may be adapted for use with other systems. The Excel spreadsheets with embedded Visual Basic macros and detailed instructions for their use are available tit http://www.ou.edu/microarray. CR ARFIN SM, 2000, J BIOL CHEM, V275, P29672 BARBOSA TM, 2000, J BACTERIOL, V182, P3467 BROWN PO, 1999, NAT GENET S, V21, P33 DERISI JL, 1999, CURR OPIN ONCOL, V11, P76 FEREA TL, 1999, CURR OPIN GENET DEV, V9, P715 KHODURSKY AB, 2000, P NATL ACAD SCI USA, V97, P12170 LONG AD, 2001, J BIOL CHEM, V276, P19937 NEIDHARDT FC, 1974, J BACTERIOL, V119, P736 NEIDHARDT FC, 1990, PHYSL BACTERIAL CELL POMPOSIELLO PJ, 2001, J BACTERIOL, V183, P3890 RICHMOND CS, 1999, NUCLEIC ACIDS RES, V27, P3821 SCHENA M, 1995, SCIENCE, V270, P467 SCHENA M, 1998, TRENDS BIOTECHNOL, V16, P301 SCHERF U, 2000, NAT GENET, V24, P236 SELINGER DW, 2000, NAT BIOTECHNOL, V18, P1262 SHERLOCK G, 2001, NUCLEIC ACIDS RES, V29, P152 TAO H, 2001, J BACTERIOL, V83, P2979 TAO H, 1999, J BACTERIOL, V181, P6425 WEI Y, 2001, J BACTERIOL, V183, P545 ZIMMER DP, 2000, P NATL ACAD SCI USA, V97, P14674 TC 1 BP 110 EP + PG 7 JI Biotechniques PY 2002 PD JAN VL 32 IS 1 GA 511VL PI NATICK RP Conway T Univ Oklahoma, Dept Bot & Microbiol, Norman, OK 73069 USA J9 BIOTECHNIQUES PA 154 E. CENTRAL ST, NATICK, MA 01760 USA UT ISI:000173286200022 ER PT Journal AU Gancia, E Manallack, DT Green, RH TI Implementation of a web-based reagent selector for combinatorial libraries SO INTERNET JOURNAL OF CHEMISTRY LA English DT Article NR 18 SN 1099-8292 PU INTERNET JOURNAL OF CHEMISTRY C1 Caltech R&D Ltd, Granta Pk, Cambridge CB1 6GS, England Caltech R&D Ltd, Cambridge CB1 6GS, England DE combinatorial chemistry; building blocks; unity; cgi-bin scripts ID MOLECULAR-SURFACE AREA; TRANSPORT PHENOMENA; INFORMATION- SYSTEM; RAPID CALCULATION; DRUG DISCOVERY; PREDICTION; PERMEABILITY; DESIGN AB This paper describes the implementation of a web-based system to help in the design of general-purpose combinatorial chemistry libraries, based on Tripos software (Unity and ChemEnlighten). The main feature of this system is a reagent selector, which utilises Unity executables to search databases of commercially available reagents and to apply chemistry and property filters to the resulting hit lists. The reagent selector has two components: a web interface writtten in HTML to define the searching and filtering options a cgi-bin script, written in PERL that launches the proper Unity executables to perform the database searching and filtering. Once the selection is done, an output HTML page is dynamically generated by the script, allowing the user to save the final list of reagents in a variety of formats. Thanks to other scripts written in-house, the list of reagents can be imported in ChemEnlighten for further data visualization and analysis. Despite the number of scripts and tools used and the behind- the-scene complexity, this reagent selector system is simple to use and intuitive for the end user. CR AJAY BGW, 1999, J MED CHEM, V42, P4942 BAXTER CA, 2000, J CHEM INF COMP SCI, V40, P254 BRAVI G, 2000, J CHEM INF COMP SCI, V40, P1441 BRONZETTI M, 1995, 216 ACS NAT M BOST M CLARK DE, 1999, J PHARM SCI, V88, P807 CLARK DE, 1999, J PHARM SCI, V88, P815 DEJULIANORTIZ JV, 1999, J MED CHEM, V42, P3308 ERTL P, 2000, J MED CHEM, V43, P3714 ERTL P, 1997, THEOCHEM-J MOL STRUC, V419, P113 HOPFINGER AJ, 1999, J CHEM INF COMP SCI, V39, P1151 LEACH AR, 1999, J CHEM INF COMP SCI, V39, P1161 LIPINSKI CA, 1997, ADV DRUG DELIVER REV, V23, P3 OPREA TI, 2000, J MOL GRAPH MODEL, V18, P512 SADOWSKI J, 1993, CHEM REV, V93, P2567 WALTERS WP, 1998, DRUG DISCOV TODAY, V3, P160 WEININGER D, 1988, J CHEM INFORMATION C, V28, P31 WINIWARTER S, 1998, J MED CHEM, V41, P4939 ZHENG WF, 1998, J CHEM INF COMP SCI, V38, P251 TC 0 BP art. no. EP 1 PG 11 JI Internet J. Chem. PY 2002 PD JAN 4 VL 5 IS 1 GA 508GH PI DE KALB RP Gancia E Caltech R&D Ltd, Granta Pk, Cambridge CB1 6GS, England J9 INTERNET J CHEM PA C/O STEVEN M BACHRACH, NORTHERN ILLINOIS UNIV, DEPT CHEMISTRY, DE KALB, IL 60115 USA UT ISI:000173080000001 ER PT Book in series AU Tino, P Nabney, I Sun, Y TI Using directional curvatures to visualize folding patterns of the GTM projection manifolds SO ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS LA English DT Article NR 11 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Aston Univ, Neural Comp Res Grp, Aston Triangle, Birmingham B4 7ET, W Midlands, England Aston Univ, Neural Comp Res Grp, Birmingham B4 7ET, W Midlands, England AB In data visualization, characterizing local geometric properties of non-linear projection manifolds provides the user with valuable additional information that can influence further steps in the data analysis. We take advantage of the smooth character of GTM projection manifold and analytically calculate its local directional curvatures. Curvature plots are useful for detecting regions where geometry is distorted, for changing the amount of regularization in non-linear projection manifolds, and for choosing regions of interest when constructing detailed lower-level visualization plots. CR BATES DM, 1980, J ROY STAT SOC B MET, V42, P1 BAUER HU, 1992, IEEE T NEURAL NETWOR, V3, P570 BISHOP CM, 1998, NEURAL COMPUT, V1, P215 BISHOP CM, 1995, NEURAL NETWORKS PATT BISHOP CM, 1997, P 1997 WORKSH SELF O BISHOP CM, 1997, P IEE 5 INT C ART NE, P64 HORN RA, 1985, MATRIX ANAL KOHONEN T, 1990, P IEEE, V9, P1464 SEBER GAF, 1989, NONLINEAR REGRESSION TINO P, 2000, NCRG2000011 AST U WILLMANN T, 1994, ICNN 94 P IEEE SERV, P645 TC 0 BP 421 EP 428 PG 8 SE LECTURE NOTES IN COMPUTER SCIENCE PY 2001 VL 2130 GA BT43Y PI BERLIN RP Tino P Aston Univ, Neural Comp Res Grp, Aston Triangle, Birmingham B4 7ET, W Midlands, England J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000173024600058 ER PT Journal AU LI, KC TI ON PRINCIPAL HESSIAN DIRECTIONS FOR DATA VISUALIZATION AND DIMENSION REDUCTION - ANOTHER APPLICATION OF STEINS LEMMA SO JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION LA English DT Article NR 46 SN 0162-1459 PU AMER STATISTICAL ASSOC C1 UNIV CALIF LOS ANGELES,LOS ANGELES,CA 90024 DE PROJECTION PURSUIT; SLICED INVERSE REGRESSION; STATISTICAL GRAPHICS; STEINS LEMMA ID GENERALIZED CROSS-VALIDATION; SLICED INVERSE REGRESSION; EXPLORATORY PROJECTION PURSUIT; ASYMPTOTIC OPTIMALITY; MULTIPLE-REGRESSION; DISPERSION; CL AB Modem graphical tools have enhanced our ability to learn many things from data directly. With much user-friendly graphical software available, we are encouraged to plot a lot more often than before. The benefits from direct interaction with graphics have been enormous. But trailing behind these high-tech advances is the issue of appropriate guidance on what to plot. There are too many directions to project a high-dimensional data set and unguided plotting can be time-consuming and fruitless. In a recent article, Li set up a statistical framework for study on this issue, based on a notion of effective dimension reduction (edr) directions. They are the directions to project a high dimensional input variable for the purpose of effectively viewing and studying its relationship with an output variable. A methodology, sliced inverse regression, was introduced and shown to be useful in finding edr directions. This article introduces another method for finding edr directions. It begins with the observation that the eigenvectors for the Hessian matrices of the regression function are helpful in the study of the shape of the regression surface. A notation of principal Hessian directions (pHd's) is defined that locates the main axes along which the regression surface shows the largest curvatures in an aggregate sense. We show that pHd's can be used to find edr directions. We further use the celebrated Stein lemma for suggesting estimates. The sampling properties of the estimated pHd's are obtained. A significance test is derived for suggesting the genuineness of a view found by our method. Some versions for implementing this method are discussed, and simulation results and an application to real data are reported. The relationship of this method with exploratory projection pursuit is also discussed. 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Am. Stat. Assoc. PY 1992 PD DEC VL 87 IS 420 GA KB896 PI ALEXANDRIA RP LI KC UNIV CALIF LOS ANGELES,LOS ANGELES,CA 90024 J9 J AMER STATIST ASSN PA 1429 DUKE ST, ALEXANDRIA, VA 22314 UT ISI:A1992KB89600014 ER PT Journal AU CARROLL, RJ LI, KC TI MEASUREMENT ERROR REGRESSION WITH UNKNOWN LINK - DIMENSION REDUCTION AND DATA VISUALIZATION SO JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION LA English DT Article NR 25 SN 0162-1459 PU AMER STATISTICAL ASSOC C1 TEXAS A&M UNIV SYST,COLL STN,TX 77843 DE DATA VISUALIZATION; DIMENSION REDUCTION; ERRORS IN VARIABLES; GENERALIZED LINEAR MODEL; LOGISTIC REGRESSION; SLICED INVERSE REGRESSION ID PROJECTION PURSUIT; SCORE TESTS; MODELS; BINARY AB A general nonlinear regression problem is considered with measurement error in the predictors. We assume that the response is related to an unknown linear combination of a multidimensional predictor through an unknown link function. Instead of observing the predictor, we instead observe a surrogate with the property that its expectation is linearly related to the true predictor with constant variance. We identify an important transformation of the surrogate variable. Using this transformed variable, we show that if one proceeds with the usual analysis ignoring measurement error, then both ordinary least squares and sliced inverse regression yield estimates which consistently estimate the true regression parameter, up to a constant of proportionality. We derive the asymptotic distribution of the estimates. A simulation study is conducted applying sliced inverse regression in this context. CR BRILLINGER DR, 1977, BIOMETRIKA, V64, P509 BRILLINGER DR, 1983, FESTSCHRIFT EL LEHMA, P97 CARROLL RJ, 1984, BIOMETRIKA, V71, P19 CARROLL RJ, 1990, J AM STAT ASSOC, V85, P652 CARROLL RJ, 1991, J ROY STAT SOC B MET, V53, P573 CARROLL RJ, 1989, STAT MED, V8, P1075 CARROLL RJ, 1988, TRANSFORMATION WEIGH CHEN H, 1991, ANN STAT, V19, P142 COOK RD, 1979, TECHNOMETRICS, V31, P277 DIACONIS P, 1984, ANN STAT, V12, P793 DUAN N, 1991, ANN STAT, V19, P505 FULLER WA, 1987, MEASUREMENT ERROR MO HALL P, 1989, ANN STAT, V17, P573 HALL P, UNPUB ANN STATISTICS HARDLE W, 1989, J AM STAT ASSOC, V84, P986 HSING TL, 1992, ANN STAT, V20, P1040 LI KC, 1989, ANN STAT, V17, P1009 LI KC, 1992, IN PRESS J AM STATIS LI KC, 1991, J AM STAT ASSOC, V86, P316 LI KC, 1990, UCLA STATISTICAL SER, V24 PEPE MS, 1991, J AM STAT ASSOC, V86, P108 PIERCE DA, 1992, J AM STAT ASSOC, V87, P351 STEFANSKI LA, 1990, J ROY STAT SOC B MET, V52, P345 TOSTESON T, 1988, BIOMETRIKA, V75, P507 TOSTESON TD, 1989, STAT MED, V8, P1139 TC 10 BP 1040 EP 1050 PG 11 JI J. Am. Stat. Assoc. PY 1992 PD DEC VL 87 IS 420 GA KB896 PI ALEXANDRIA RP TEXAS A&M UNIV SYST,COLL STN,TX 77843 J9 J AMER STATIST ASSN PA 1429 DUKE ST, ALEXANDRIA, VA 22314 UT ISI:A1992KB89600015 ER PT Journal AU POTTINGER, D TODD, S RODRIGUES, I MULLIN, T SKELDON, A TI PHASE PORTRAITS FOR PARAMETRICALLY EXCITED PENDULA - AN EXERCISE IN MULTIDIMENSIONAL DATA VISUALIZATION SO COMPUTERS & GRAPHICS LA English DT Article NR 17 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 UNIV OXFORD,CLARENDON LAB,OXFORD,ENGLAND IBM UNITED KINGDOM LTD,WINCHESTER SO23 9DR,ENGLAND AB Data visualisation techniques are used to investigate the five dimensional phase space of a system of two pendula excited by an oscillatory force. A sequence of phase portraits is obtained that vividly illustrates the system undergoing a bifurcation as the frequency of the excitation is varied in a certain range. Such pictures provide valuable information on the topological transformations that the system undergoes as it approaches chaotic motion. This data visualisation method can also be applied to other multidimensional problems where a phase portrait analysis is helpful. CR BROOMHEAD DS, 1986, PHYSICA D, V20, P217 BURGER P, 1989, INTERACTIVE COMPUTER BURRIDGE JM, 1989, IBM SYST J, V28, P548 GAMBAUDO JM, 1985, J PHYS LETT, V46, P653 GUCKENHEIMER J, 1983, NONLINEAR OSCILLATIO HEYWOOD TR, 1984, COMPUTER GRAPHICS FO, V3, P61 IOOSS G, 1980, ELEMENTARY STABILITY KELLER HB, 1977, APPL BIFURCATION THE, P359 MANTYLA M, 1988, INTRO SOLID MODELLIN MULLIN T, 1989, NATURE, V340, P294 MULLIN T, 1989, NEW SCI, V1689, P46 POTTINGER DEL, 1989, PENDULA MOTION POTTINGER DEL, UNPUB TEXTURE MULTID SKELDON AC, IN PRESS PHYS LETT A SKELDON AC, 1990, THESIS OXFORD U THOMPSON JMT, 1988, NONLINEAR DYNAMICS C TODD SJP, 1990, IBM TECHNICAL DISCLO, V33, P202 TC 2 BP 331 EP 337 PG 7 JI Comput. Graph. PY 1992 PD FAL VL 16 IS 3 GA JP715 PI OXFORD RP UNIV OXFORD,CLARENDON LAB,OXFORD,ENGLAND J9 COMPUT GRAPH PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1992JP71500012 ER PT Journal AU TOMBEUR, D DECONINCK, F TI A PROTOTYPE IMAC ENVIRONMENT WITH NETWORKWIDE DISTRIBUTED DATA ACCESS, DATA-PROCESSING, DATA VISUALIZATION AND GRAPHICAL USER INTERFACES SO EUROPEAN JOURNAL OF NUCLEAR MEDICINE LA English DT Meeting Abstract NR 0 SN 0340-6997 PU SPRINGER VERLAG C1 VRIJE UNIV BRUSSELS,B-1050 BRUSSELS,BELGIUM TC 0 BP 639 EP 639 PG 1 JI Eur. J. Nucl. Med. PY 1992 PD AUG VL 19 IS 8 GA JL496 PI NEW YORK RP VRIJE UNIV BRUSSELS,B-1050 BRUSSELS,BELGIUM J9 EUR J NUCL MED PA 175 FIFTH AVE, NEW YORK, NY 10010 UT ISI:A1992JL49600270 ER PT Journal AU MARIANI, JA LOUGHER, R TI TRIPLESPACE - AN EXPERIMENT IN A 3D GRAPHICAL INTERFACE TO A BINARY RELATIONAL DATABASE SO INTERACTING WITH COMPUTERS LA English DT Article NR 12 SN 0953-5438 PU BUTTERWORTH-HEINEMANN LTD C1 UNIV LANCASTER,DEPT COMP,LANCASTER LA1 4YR,ENGLAND DE DATA VISUALIZATION; CYBERSPACE; 3D GRAPHICAL INTERFACES; BINARY RELATIONAL DATABASES; BROWSING AB Novel techniques of data visualization will be required to take full advantage of the advanced human-computer interaction technology now being developed as part of the virtual reality movement. In particular, meaningful three-dimensional representations will be of interest to users wishing to explore the data topology. An experimental system which offers a three- dimensional topology is presented in this paper; three sets of data representing interesting situations are described and viewed through the system. CR CAMPBELL DM, 1987, DATA KHOWL ENG, V2, P89 ELGALAL BEMA, 1985, THESIS U STRATHCLYDE ELMASRI RA, 1985, 4 INT C ENT REL APPR, P236 FOGG D, 1984, LESSONS LIVING DATAB, P100 FROST RA, 1982, COMPUT J, V25, P358 HEROT CF, 1980, ACM T DATABASE SYST, V5, P493 LEONG MK, 1989, VISUAL DATABASE SYST, P465 MARIANI JA, 1992, CAMPUT J NORMAN DA, 1986, USER CENTRED SYSTEM RAMANATHAN J, 1989, IEEE SOFTWARE SEP, P50 SAWYER P, 1988, SOFTWARE ENG J, V3, P6 ZLOOF MM, 1977, IBM SYST J, V4, P324 TC 8 BP 147 EP 162 PG 16 JI Interact. Comput. PY 1992 PD AUG VL 4 IS 2 GA JL334 PI OXFORD RP MARIANI JA UNIV LANCASTER,DEPT COMP,LANCASTER LA1 4YR,ENGLAND J9 INTERACT COMPUT PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, OXON, ENGLAND OX5 1GB UT ISI:A1992JL33400001 ER PT Journal AU ERVIN, SM TI A HYPERMEDIA GIS - THE MASSACHUSETTS TURNPIKE STUDY SO COMPUTERS ENVIRONMENT AND URBAN SYSTEMS LA English DT Article NR 13 SN 0198-9715 PU PERGAMON-ELSEVIER SCIENCE LTD C1 HARVARD UNIV,GRAD SCH DESIGN,DEPT LANDSCAPE ARCHITECTURE,48 QUINCY ST,CAMBRIDGE,MA 02138 AB This article describes an information system developed at the Harvard Graduate School of Design in support of a landscape analysis, planning and design project concerned with the Massachusetts Turnpike. This system includes both vector and raster GIS database and analysis software; a video disk containing some 6000 sequential images of the study area; image processing software for producing and editing color bit-maps and producing hard-copy; hyper-media database management and exploration software; and spreadsheet software for statistical analysis and data visualization. The system is self-contained on a Macintosh IIcx workstation, using commercial software, and linked through a local-area network to other computers for access to a larger database, more complex GIS analysis, and other services such as CAD software. CR APPLEYARD D, 1961, VIEW ROAD BURROUGH P, 1986, PRINCIPLES GEOGRAPHI ERVIN SM, 1991, 1991 CELA C P, V3, P37 GIMBLETT RH, 1989, 1989 P GIS LIS LARSEN K, 1989, MACIS GEOGRAPHIC INF MCALEESE R, 1989, HYPERTEXT THEORY PRA NIELSEN J, 1990, HYPERTEXT HYPERMEDIA SIMKOWITZ H, 1989, COMPUTERS ENV URBAN, V12, P253 STEINITZ C, 1990, LANDSCALE J FALL, V9, P57 STEINITZ C, 1979, LANDSCAPE ARCHITECTU, V2 TOMLIN CD, 1990, GEOGRAPHIC INFORMATI TUFTIE E, 1990, ENVISIONING INFORMAT VANDERSCHANS R, 1990, INT J GEOGRAPHICAL I, V4, P225 TC 1 BP 375 EP 383 PG 9 JI Comput. Environ. Urban Syst. PY 1992 PD JUL-AUG VL 16 IS 4 GA JL300 PI OXFORD RP ERVIN SM HARVARD UNIV,GRAD SCH DESIGN,DEPT LANDSCAPE ARCHITECTURE,48 QUINCY ST,CAMBRIDGE,MA 02138 J9 COMPUT ENVIRON URBAN SYST PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1992JL30000012 ER PT Journal AU GRINSTEIN, G SIEG, JC SMITH, S WILLIAMS, MG TI VISUALIZATION FOR KNOWLEDGE DISCOVERY SO INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS LA English DT Article NR 28 SN 0884-8173 PU JOHN WILEY & SONS INC C1 UNIV MASSACHUSETTS,LOWELL,MA 01854 ID VISION AB Although the fields of data visualization and automated knowledge discovery (AKD) share many goals, workers in each field have been reluctant to adopt the tools and methods of the other field. Many AKD researchers discourage the use of visualization tools because they believe that dependence on human steering will impede the development of numerical or analytical descriptions of complex data. Many visualization researchers are concerned that their present platforms are being pushed to the limits of their performance by the most advanced visualization techniques and are therefore unwilling to incur the perceived overhead of having a database system mediate access to the data. We argue that these attitudes are somewhat short-sighted and that the techniques of these two communities are complementary. We discuss a specific visualization system that we have developed and describe the obstacles that must be overcome in integrating it into an AKD system. CR BECK J, ORG REPRESENTATION P, P285 BERGERON RD, 1989, EUROGRAPHICS 89, P393 BLY SA, 1982, P CHI 82 C HUMAN FAC, P371 BREGMAN AS, 1990, AUDITORY SCENE ANAL BREGMAN AS, 1975, J EXPT PSYCHOL HUMAN, V1, P263 ENNS JT, 1990, VISUAL SEARCH, P37 GRAEFE G, 1989, ACM SIGMOD INT C MAN, P358 GRINSTEIN G, 1989, GRAPHICS INTERFACE 8 HAAS LM, 1989, ACM SIGMOD INT C MAN, P377 JULESZ B, 1983, BELL SYST TECH J, V62, P1619 LEVKOWITZ H, 1991, P VIS SAN DIEG CAL, P164 LUNNEY D, 1990, P SPIE SPSE C ELECTR, V159, P140 MANSUR DL, 1985, J MED SYST, V9, P163 MEZRICH JJ, 1984, J AM STAT ASSOC, V79, P34 PICKETT RM, 1988, 1988 P IEEE C SYST M PICKETT RM, 1970, PICTURE PROCESSING P SCALETTI C, 1991, P SOC PHOTO-OPT INS, V1459, P207 SCIORE E, 1991, UNPUB MODULAR RULE B SMITH RD, 1990, P CHI 90 SEATTLE SMITH S, 1991, P VISUALIZATION 91 S TREISMAN A, 1988, PSYCHOL REV, V95, P15 TREISMAN A, 1986, SCI AM, V255, P106 WARREN RM, 1982, AUDITORY PERCEPTION WARREN WH, J EXP PSYCHOL, V10, P704 WILLIAMS MG, 1989 P IEEE VIS LANG, P62 WILLIAMS MG, 1990, ACM SIGCHI B, V21, P44 WOLFE JM, 1990, AI EYE YEUNG ES, 1980, ANAL CHEM, V52, P1120 TC 1 BP 637 EP 648 PG 12 JI Int. J. Intell. Syst. PY 1992 PD SEP VL 7 IS 7 GA JJ150 PI NEW YORK RP GRINSTEIN G UNIV MASSACHUSETTS,LOWELL,MA 01854 J9 INT J INTELL SYST PA 605 THIRD AVE, NEW YORK, NY 10158-0012 UT ISI:A1992JJ15000005 ER PT Journal AU ORLAND, B TI DATA VISUALIZATION TECHNIQUES IN ENVIRONMENTAL-MANAGEMENT - A WORKSHOP SO LANDSCAPE AND URBAN PLANNING LA English DT Editorial Material NR 11 SN 0169-2046 PU ELSEVIER SCIENCE BV C1 UNIV ILLINOIS,DEPT LANDSCAPE ARCHITECTURE,URBANA,IL 61801 CR COX DJ, 1990, ACAD COMPUTING, V4, P20 CULATTI A, 1992, LANDSCAPE URBAN PLAN DANIEL TC, 1992, LANDSCAPE URBAN PLAN DANIEL TC, 1990, RESOURCE TECHNOLOGY KOVACIC D, 1990, RESOURCE TECHNOLOGY, P1 LARSON SM, 1988, ENVIRON SCI TECHNOL, V22, P629 MALM W, 1980, ATMOS ENVIRON, V15, P1875 ONSTAD DW, 1988, ECOL MODEL, V43, P111 ORLAND B, 1991, DEV DESCRIPTION VISU ORLAND B, 1992, LANDSCAPE URBAN PLAN ORLAND B, 1990, RESOURCE TECHNOLOGY, P48 TC 4 BP 237 EP 239 PG 3 JI Landsc. Urban Plan. PY 1992 PD MAY VL 21 IS 4 GA JA577 PI AMSTERDAM RP ORLAND B UNIV ILLINOIS,DEPT LANDSCAPE ARCHITECTURE,URBANA,IL 61801 J9 LANDSCAPE URBAN PLAN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992JA57700001 ER PT Journal AU [Anon] TI DATA VISUALIZATION TECHNIQUES IN ENVIRONMENTAL-MANAGEMENT - A RESEARCH, DEVELOPMENT AND APPLICATION PLAN SO LANDSCAPE AND URBAN PLANNING LA English DT Article NR 0 SN 0169-2046 PU ELSEVIER SCIENCE BV TC 0 BP 241 EP 244 PG 4 JI Landsc. Urban Plan. PY 1992 PD MAY VL 21 IS 4 GA JA577 PI AMSTERDAM J9 LANDSCAPE URBAN PLAN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992JA57700002 ER PT Journal AU DANIEL, TC TI DATA VISUALIZATION FOR DECISION SUPPORT IN ENVIRONMENTAL- MANAGEMENT SO LANDSCAPE AND URBAN PLANNING LA English DT Article NR 0 SN 0169-2046 PU ELSEVIER SCIENCE BV C1 UNIV ARIZONA,DEPT PSYCHOL,TUCSON,AZ 85721 AB Today, environmental managers increasingly rely on advances in remote sensing, computer mapping, geographic information systems and quantitative modeling. These modem technologies have greatly increased the quantity and quality of data available to characterize existing and projected environmental conditions. However these data can be very abstract. The connection between data and the environmental conditions they represent is critical as the goal of environmental management is to achieve desired ends in the environment, even if decisions are largely based on data. TC 8 BP 261 EP 263 PG 3 JI Landsc. Urban Plan. PY 1992 PD MAY VL 21 IS 4 GA JA577 PI AMSTERDAM RP DANIEL TC UNIV ARIZONA,DEPT PSYCHOL,TUCSON,AZ 85721 J9 LANDSCAPE URBAN PLAN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992JA57700007 ER PT Journal AU PERKINS, NH TI 3 QUESTIONS ON THE USE OF PHOTO-REALISTIC SIMULATIONS AS REAL WORLD SURROGATES SO LANDSCAPE AND URBAN PLANNING LA English DT Article NR 5 SN 0169-2046 PU ELSEVIER SCIENCE BV C1 UNIV GUELPH,SCH LANDSCAPE ARCHITECTURE,GUELPH N1G 2W1,ONTARIO,CANADA AB As the technology of data visualization has become increasingly powerful and sophisticated it has also become more accessible, less expensive and simpler to use. This trend has allowed researchers and design practitioners to push the application of computer visualization, specifically photo-based computer- imaging, at a tremendous pace. Yet, there is a very real danger that some computer-imaging applications used in visual assessment research and/or visual image presentations have not been validated as research tools. In particular, computer- imaging applications that rely on the use of computer manipulated images as surrogates for real world conditions are based on untested assumptions and hypotheses. CR DANIEL TC, 1992, LANDSCAPE URBAN PLAN, V21, P261 ESTER M, 1990, DIGITAL IMAGE DIGITA, P51 PERKINS NH, 1991, 1991 P COUNC ED LAND SHEPPARD S, 1989, VISUAL SIMULATION US WRIGHT R, 1990, COMPUTER GRAPHICS AL, P65 TC 5 BP 265 EP 267 PG 3 JI Landsc. Urban Plan. PY 1992 PD MAY VL 21 IS 4 GA JA577 PI AMSTERDAM RP PERKINS NH UNIV GUELPH,SCH LANDSCAPE ARCHITECTURE,GUELPH N1G 2W1,ONTARIO,CANADA J9 LANDSCAPE URBAN PLAN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992JA57700008 ER PT Journal AU PITT, DG NASSAUER, JI TI VIRTUAL REALITY SYSTEMS AND RESEARCH ON THE PERCEPTION, SIMULATION AND PRESENTATION OF ENVIRONMENTAL-CHANGE SO LANDSCAPE AND URBAN PLANNING LA English DT Article NR 3 SN 0169-2046 PU ELSEVIER SCIENCE BV C1 UNIV MINNESOTA,DEPT LANDSCAPE ARCHITECTURE,212 N HALL,2005 BUFORD CIRCLE,ST PAUL,MN 55108 AB The ability of evolving data visualization techniques (e.g. video image-capture technology) to portray realistically both the sensuous and connotative properties of the molar environment and change within the environment has produces an abundance of environmental perception studies using various visualization technologies. Future growth in the application of these technologies to environmental change perception research requires consideration of three sets of issues: (1) the nature of the perceptual experiencing of environment; (2) simulation of this experience; and (3) presentation of environmental displays to people engaged in the studies. CR ITTELSON WH, 1973, ENV COGNITION SHEPPARD S, 1989, VISUAL SIMULATION US ZUBE EH, 1987, LANDSCAPE J, V6, P62 TC 2 BP 269 EP 271 PG 3 JI Landsc. Urban Plan. PY 1992 PD MAY VL 21 IS 4 GA JA577 PI AMSTERDAM RP PITT DG UNIV MINNESOTA,DEPT LANDSCAPE ARCHITECTURE,212 N HALL,2005 BUFORD CIRCLE,ST PAUL,MN 55108 J9 LANDSCAPE URBAN PLAN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992JA57700009 ER PT Journal AU STEINITZ, C TI SOME WORDS OF CAUTION SO LANDSCAPE AND URBAN PLANNING LA English DT Article NR 1 SN 0169-2046 PU ELSEVIER SCIENCE BV C1 HARVARD UNIV,SCH DESIGN,48 QUINCY ST,CAMBRIDGE,MA 02138 AB These observations are intended to raise some issues that should be addressed prior to embarking on any major research program in the area of data visualization. The environmental professions are not at the leading edge of technical development in data visualization, nor are they likely to be, within the foreseeable future. Most of the innovations of the last decade have come through military, aerospace, automotive, medical and entertainment oriented research and development. CR STEINITZ C, 1990, LANDSCAPE J, V9, P137 TC 1 BP 273 EP 274 PG 2 JI Landsc. Urban Plan. PY 1992 PD MAY VL 21 IS 4 GA JA577 PI AMSTERDAM RP STEINITZ C HARVARD UNIV,SCH DESIGN,48 QUINCY ST,CAMBRIDGE,MA 02138 J9 LANDSCAPE URBAN PLAN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992JA57700010 ER PT Journal AU WHITE, WB TI FUTURE FOR VISUALIZATION THROUGH THE INTEGRATED FOREST RESOURCE-MANAGEMENT SYSTEM (INFORMS) SO LANDSCAPE AND URBAN PLANNING LA English DT Article NR 2 SN 0169-2046 PU ELSEVIER SCIENCE BV C1 US FOREST SERV,FOREST PEST MANAGEMENT MAG,3852 E MULBERRY ST,FT COLLINS,CO 80524 AB Graphic and visual representation tools have always played a central role in environmental management and planning. It is difficult to imagine any significant natural resource management activity that does not rely to some extent on visual representations. Environmental managers are being exposed to technical advances in the areas of remote sensing, computer mapping, geographic information systems and quantitative modeling. As these tools have increased in sophistication, there is a commensurate need for improved data visualization tools, perhaps explaining why computer graphics is one of the most rapidly advancing of all computer technologies. It should be no surprise that resource managers are in a quandary as to how to manage these tools effectively. CR *AND CONS, 1989, INT FOR RES MAN SYST WHITE WB, 1991, P US AUSTR WORKSHOP TC 0 BP 277 EP 279 PG 3 JI Landsc. Urban Plan. PY 1992 PD MAY VL 21 IS 4 GA JA577 PI AMSTERDAM RP WHITE WB US FOREST SERV,FOREST PEST MANAGEMENT MAG,3852 E MULBERRY ST,FT COLLINS,CO 80524 J9 LANDSCAPE URBAN PLAN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992JA57700012 ER PT Journal AU HAMILTON, MP FLAXMAN, M TI SCIENTIFIC-DATA VISUALIZATION AND BIOLOGICAL DIVERSITY - NEW TOOLS FOR SPATIALIZING MULTIMEDIA OBSERVATIONS OF SPECIES AND ECOSYSTEMS SO LANDSCAPE AND URBAN PLANNING LA English DT Article NR 5 SN 0169-2046 PU ELSEVIER SCIENCE BV C1 UNIV CALIF IDYLLWILD,JAMES SAN JACINTO MT RESERVE,POB 1775,IDYLLWILD,CA 92349 AB Current knowledge of the biodiversity of protected ecosystems is often limited to museum collection specimen data, outdated or cursory inventories and anecdotal accounts. Beyond the inventory of biological diversity comes the need to monitor changes of many parameters and at many scales, and the need to incorporate this knowledge into an accessible information system for biodiversity management planning and conservation education. This paper briefly describes our continuing research efforts to develop new ways to collect, analyze, organize and distribute biodiversity data. CR *HIGH ED PAN, 1990, HIGH ED SURV SYST BI *NAT SCI BOARD, 1990, LOSS BIOL DIV GLOB C HAMILTON MP, 1989, FIRE TECHNOL, V25, P5 SOULE ME, 1989, RES PRIORITIES CONSE WILSON EO, 1988, BIODIVERSITY TC 3 BP 285 EP 287 PG 3 JI Landsc. Urban Plan. PY 1992 PD MAY VL 21 IS 4 GA JA577 PI AMSTERDAM RP HAMILTON MP UNIV CALIF IDYLLWILD,JAMES SAN JACINTO MT RESERVE,POB 1775,IDYLLWILD,CA 92349 J9 LANDSCAPE URBAN PLAN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992JA57700014 ER PT Journal AU LOH, DK HOLTFRERICH, DR CHOO, YK POWER, JM TI TECHNIQUES FOR INCORPORATING VISUALIZATION IN ENVIRONMENTAL ASSESSMENT - AN OBJECT-ORIENTED PERSPECTIVE SO LANDSCAPE AND URBAN PLANNING LA English DT Article NR 9 SN 0169-2046 PU ELSEVIER SCIENCE BV C1 TEXAS A&M UNIV SYST,DEPT RANGELAND ECOL & MANAGEMENT,STARR LAB,COLLEGE STN,TX 77843 PETAWAWA NATL FOREST RES INST,CHALK RIVER K0J 1J0,ONTARIO,CANADA AB Communication via data visualization is particularly useful in natural resource management. However, it is more difficult to implement than it is in other scientific or engineering fields. In most other fields, visualization is used for well-defined problems in a narrow domain, which usually deal with a limited number of data sets and highly structured application programs. This greatly simplifies the requirements on the computing environment and programming complexity. In that setting, visualization can be presented in a more objective fashion, based on certain commonly accepted principles or criteria. In contrast, the incorporation of visualization in natural resource management has to be dealt with on a broader basis. CR BOOCH G, 1991, OBJECT ORIENTED DESI COULSON RN, 1987, SCIENCE, V237, P262 COX BJ, 1986, OBJECT ORIENTED PROG DEFANTI TA, 1989, COMPUTER, V22, P12 HAMMING RW, 1962, NUMERICAL METHODS SC LOH DK, 1988, JUN P RES TECHN 88 I, P158 MCCORMICK BH, 1987, COMPUT GRAPHICS, V21, P1 NYE A, 1990, X PROTOCOL REFERENCE SAARENMAA H, 1990, NOV P RES TECHN 90 I, P569 TC 1 BP 305 EP 307 PG 3 JI Landsc. Urban Plan. PY 1992 PD MAY VL 21 IS 4 GA JA577 PI AMSTERDAM RP LOH DK TEXAS A&M UNIV SYST,DEPT RANGELAND ECOL & MANAGEMENT,STARR LAB,COLLEGE STN,TX 77843 J9 LANDSCAPE URBAN PLAN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992JA57700018 ER PT Journal AU WELLS, G RUDNICK, T MIYOSHI, D TI DATA VISUALIZATION IN THE SOIL CONSERVATION SERVICE SO LANDSCAPE AND URBAN PLANNING LA English DT Article NR 1 SN 0169-2046 PU ELSEVIER SCIENCE BV C1 USDA,SOIL CONSERV SERV,FED BLDG,100 CENTENNIAL MALL N,ROOM 152,LINCOLN,NE 68908 USDA,SOIL CONSERV SERV,FT COLLINS,CO 80522 AB Since its inception, the Soil Conservation Service (SCS) has been responsible for leadership in the conservation, development and productive use of the Nation's soil, water, and related resources. To provide this leadership, it has been necessary to develop robust computer applications for natural resource-management activities. These applications have primarily been text based. The agency is slowly moving toward graphical based systems to depict natural resources visually. This paper discusses both the present situation and the future needs of data visualization in SCS. CR VASILOPOULOS AD, 1991, COMPUT GRAPHICS WORL, V14, P74 TC 1 BP 333 EP 335 PG 3 JI Landsc. Urban Plan. PY 1992 PD MAY VL 21 IS 4 GA JA577 PI AMSTERDAM RP WELLS G USDA,SOIL CONSERV SERV,FED BLDG,100 CENTENNIAL MALL N,ROOM 152,LINCOLN,NE 68908 J9 LANDSCAPE URBAN PLAN PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992JA57700025 ER PT Journal AU DOMIK, GO MICKUSMICELI, KD TI DESIGN AND DEVELOPMENT OF DATA VISUALIZATION SYSTEMS IN A WORKSTATION ENVIRONMENT SO JOURNAL OF MICROCOMPUTER APPLICATIONS LA English DT Article NR 11 SN 0745-7138 PU ACADEMIC PRESS LTD C1 UNIV COLORADO,DEPT COMP SCI,BOULDER,CO 80309 NASA,AMES RES CTR,MOFFETT FIELD,CA 94035 CR DOMIK G, 1991, FEB P GES KLASS SALZ DOMIK GO, 1991, 1ST ANN C ASTR DAT A DOMIK GO, 1991, LECTURE NOTES COMPUT, V555, P91 DOMIK GO, 1991, NASA NAGW1902 FIN RE LEWIS C, 1989, 1989 WINT C HUM COMP LEWIS C, 1982, USING THINKING ALOUD MICKUSMICELI KD, 1991, PARTICIPATORY DESIGN MURRAY S, 1991, 1ST ANN C ASTR DAT A NADEAU DR, 1991, P VISUALIZATION 91 NIELSON GM, 1991, IEEE COMPUT, V24, P58 PRAWEL D, 1991, P VISUALIZATION 91 TC 1 BP 81 EP 88 PG 8 JI J. Microcomput. Appl. PY 1992 PD APR VL 15 IS 2 GA HY103 PI LONDON RP UNIV COLORADO,DEPT COMP SCI,BOULDER,CO 80309 J9 J MICROCOMPUT APPL PA 24-28 OVAL RD, LONDON, ENGLAND NW1 7DX UT ISI:A1992HY10300001 ER PT Journal AU LIEBICH, V EHRLICH, G HERRMANN, U SIEGERT, L KLUGE, W TI CHARACTERIZATION OF THE CHEMICAL HOMOGENEITY OF SOLID-STATE MATERIALS BY CHEMOMETRIC METHODS - MULTIVARIATE ASPECTS AND GENERAL RECOMMENDATIONS SO FRESENIUS JOURNAL OF ANALYTICAL CHEMISTRY LA English DT Article NR 17 SN 0937-0633 PU SPRINGER VERLAG C1 INST SOLID STATE & MAT RES EV,INST SOLIDS ANAL & STRUCT RES,HELMHOLTZSTR 20,O-8027 DRESDEN,GERMANY ID PATTERN-RECOGNITION METHODS AB Data from milliprobe analysis of a copper standard material by spark source mass spectrometry have been investigated using multivariate methods in addition to earlier - predominantly univariate - treatment in order to derive some general recommendations for characterizing the chemical homogeneity of solids. A "disarmed" MANOVA test with the locations as effect variables and the normalized concentration data of the diverse chemical elements as replicates provided a highly rigorous Yes- No decision on homogeneity from an overall point of view. For more detailed information, data visualization by raster graphics combined with cluster analysis, principal components analysis, and non-linear mapping proved a useful explorative tool, particularly in the (here assumed) case without any preinformation. Dependent on how these methods are used, information on locations of inhomogeneities or on correlations between the analytes results. It has been shown that seeking for correlations should be the first explorative step. Employing the further steps to correlating elements increases the rigour of detecting element-specific inhomogeneities. On the other hand, including all the determined elements enables a more critical judgment of "collective homogeneity". To confirm multivariate results on inhomogeneity by statistical means, the revealed groups of sub-samples should be tested by linear discriminant analysis. Deviations from a homogeneous distribution can be detected and/or characterized for any single element (or correlating elements) individually by univariate methods based on models of the spatial distributions, if these can be presumed a-priori or after preliminary investigations. CR AHRENS H, 1981, MEHRDIMENSIONALE VAR DANZER K, 1979, ANAL CHIM ACTA, V105, P1 DANZER K, 1990, COMMUNICATION DANZER K, 1985, MIKROCHIM ACTA, V1, P219 EHRLICH G, 1989, MIKROCHIM ACTA, V1, P145 GELADI P, 1991, APPLIED MULTIVARIATE HENRION G, 1988, BEISPIELE DATENANALY LACHENBRUCH PA, 1975, DISCRIMINANT ANAL LIEBICH V, 1989, FRESEN Z ANAL CHEM, V335, P945 LIEBICH V, 1989, MIKROCHIM ACTA, V2, P39 LIU XD, 1987, FRESEN Z ANAL CHEM, V327, P659 LIU XD, 1986, MIKROCHIM ACTA, V3, P49 MASSART DL, 1988, CHEMOMETRICS TXB NIKOLOVA L, 1988, FRESEN Z ANAL CHEM, V332, P22 SHARAF MA, 1986, CHEMOMETRICS SIEGERT L, 1988, BILD TON, V41, P373 WIENKE D, 1986, ANAL CHIM ACTA, V184, P107 TC 2 BP 251 EP 258 PG 8 JI Fresenius J. Anal. Chem. PY 1992 PD MAY VL 343 IS 3 GA HX527 PI NEW YORK RP LIEBICH V INST SOLID STATE & MAT RES EV,INST SOLIDS ANAL & STRUCT RES,HELMHOLTZSTR 20,O-8027 DRESDEN,GERMANY J9 FRESENIUS J ANAL CHEM PA 175 FIFTH AVE, NEW YORK, NY 10010 UT ISI:A1992HX52700001 ER PT Journal AU RANADE, S TI OPTICAL STORAGE FOR THE NETWORKED COMPUTER SO DOCUMENT & IMAGE AUTOMATION LA English DT Article NR 1 SN 1071-6130 PU PHILIPS BUSINESS INFOR AB The networked computer of the future will be a shared resource providing its users with a wide range of capabilities, ranging from numerical processor-intensive functions to visualization, electronic data storage and distribution, and mail and fax services. This article looks at data storage for this networked computer; in particular, it examines the ways in which optical storage will be used for this purpose. The likely role for the common forms of optical storage (CD-ROM, WORM, and optical tape) is explained and some of the problems in using optical technology for networked storage are discussed. Examples of some current network storage products using optical technology are provided. CR OLEAR BT, 1991, JUL P C MASS STOR FI TC 0 BP 13 EP 18 PG 6 PY 1992 PD SPR VL 12 IS 1 GA HV757 PI POTOMAC J9 DOC IMAGE AUTOM PA 1201 SEVEN LOCKS RD, POTOMAC, MD 20854 UT ISI:A1992HV75700005 ER PT Journal AU GLICK, M HIEFTJE, GM TI STEREOSCOPIC DATA VISUALIZATION - PATTERN-RECOGNITION IN 3 DIMENSIONS SO ANALYTICA CHIMICA ACTA LA English DT Article NR 8 SN 0003-2670 PU ELSEVIER SCIENCE BV C1 INDIANA UNIV,DEPT CHEM,BLOOMINGTON,IN 47405 DE PATTERN RECOGNITION; DATA VISUALIZATION; STEREOSCOPIC DATA VISUALIZATION AB A method of data visualization that uses stereoscopic displays for viewing data in three dimensions is described. It is based on an inexpensive stereoscopic system that uses a personal computer and commercially available, modulated liquid-crystal eyeglasses. Details about the hardware are given, and software written to take advantage of the stereoscopic effect is described. This method of scientific data visualization can assist in the interpretation of data in three dimensions for the purposes of data preprocessing, data analysis, and pattern recognition. CR FISHER RA, 1936, ANN EUGENICS 2, V7, P179 FOLEY JD, 1982, FUNDAMENTALS INTERAC GROTCH SL, 1983, CHEMOMETRICS MATH ST, P439 HUANG M, 1992, IN PRESS SPECTROCHIM KOWALSKI BR, 1972, ANAL CHEM, V44, P2176 KOWALSKI BR, 1973, J AM CHEM SOC, V95, P686 NELSON GM, 1990, VISUALIZATION SCI CO WOLFF RS, 1988, COMPUTERS PHYSIC MAY, P28 TC 1 BP 79 EP 87 PG 9 JI Anal. Chim. Acta PY 1992 PD APR 5 VL 259 IS 1 GA HM664 PI AMSTERDAM RP GLICK M INDIANA UNIV,DEPT CHEM,BLOOMINGTON,IN 47405 J9 ANAL CHIM ACTA PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992HM66400012 ER PT Journal AU CURRIE, LA TI IN PURSUIT OF ACCURACY - NOMENCLATURE, ASSUMPTIONS, AND STANDARDS SO PURE AND APPLIED CHEMISTRY LA English DT Article NR 64 SN 0033-4545 PU BLACKWELL SCIENCE LTD C1 NATL INST STAND & TECHNOL,GAITHERSBURG,MD 20899 ID MODELS AB Accuracy is the central measure of quality in Analytical Science. Three topics, essential in the pursuit of accuracy, especially in the exposure and reduction of the "big" errors in analytical measurements, are reviewed in this article. The first topic, Analytical Nomenclature, lies at the heart of any effort to realize and communicate the nature and level of accuracy of the Chemical Measurement Process (CMP). The discussion centers about the historic role of IUPAC in this endeavor, together with some outstanding problems in Analytical Nomenclature, particularly as related to detection and identification. The second topic comprises assumptions and standards (materials and data) that are crucial in the search for major errors and in the control of accuracy, both within and between laboratories. Standard (Certified) Reference Materials have long been central to this effort, but more recently they have been joined by Standard Test Data. The latter, data sets having known characteristics, designed to simulate the structure of complex analytical signals, show great promise for the control of quality in the computational phase of the CMP. The final topic relates to Revolutions in Analytical Measurement Science that can make the most profound impact on accuracy: revolutions in sampling, measurement, and computation. Two illustrations are presented: (1) Accelerator Mass Spectrometry, a fundamentally new approach to atomic and isotopic mass spectrometry that makes possible the direct measurement of element and isotope ratios as small as 10(-16); and (2) Exploratory Statistical Graphics, a data visualization tool that permits analytical scientists to directly apply their intuitive "vision" to the assessment of multivariate data quality, and to search for unsuspected relationships in complex chemical datasets. CR 1991, ANAL QUALITY CONTROL 1984, INT VOCABULARY BASIC 1991, SCIENCE, V253 1991, STANDARD REFERENCE D 1977, STATISTICS VOCABULAR 1989, TERMS DEFINITIONS US 1991, USE NIST STANDARD RE ALGEO ME, 1991, COMMUNICATION ALVAREZ LW, 1939, PHYS REV, V56, P379 ALVAREZ LW, 1939, PHYS REV, V56, P613 BEHNE D, 1981, J CLIN CHEM CLIN BIO, V19, P115 BONANI G, 1986, RADIOCARBON, V28, P246 BRASSEUR GP, 1988, CHANGING ATMOSPHERE, P235 BROSSMAN MW, 1988, DETECTION ANAL CHEM, V361, PCH17 CLARK TL, 1990, US EPA600390051 REP CURRIE LA, 1991, ACS S SER, V445, PCH6 CURRIE LA, 1988, ACS S SERIES, V361, PCH1 CURRIE LA, 1984, ATMOS ENVIRON, V18, P1517 CURRIE LA, 1991, CHEMOMETR INTELL LAB, V10, P59 CURRIE LA, 1991, IN PRESS CHEMOMETRIC CURRIE LA, 1985, J RES NATL BUR STAND, V90, P409 CURRIE LA, 1982, NBS REPORT ANAL 0719 CURRIE LA, 1983, RADIOCARBON, V25, P603 CURRIE LA, 1991, UNPUB CHEM INT EFRON B, 1991, SCIENCE, V253, P390 ELMORE D, 1987, NUCL INSTRUM METH B, V29, P207 FILLIBEN JJ, 1984, NBS SPECIAL PUBL, V667 FLURY B, 1988, MULTIVARIATE STATIST FREISER H, 1987, COMPENDIUM ANAL NOME GLESER LJ, 1991, CHEMOMETR INTELL LAB, V10, P45 GOLD V, 1987, COMPENDIUM CHEM TERM GORDON GE, 1988, ENVIRON SCI TECHNOL, V22, P1132 HORWITZ W, 1988, PURE APPL CHEM, V60, P856 IYENGAR GV, 1989, ELEMENTAL ANAL BIOL, V1 IYENGAR GV, 1991, SCI TOTAL ENVIRON, V100, P1 KLEIN J, 1984, NUCL INSTRUM METH B, V5, P129 KOPTYUG VA, 1990, CHEM ENV KUBIK PW, 1990, NUCL INSTRUM METH B, V52, P238 LAYLOFF TP, 1991, PHARM TECHNOL, V15, P146 LEIGH GJ, 1989, NOMENCLATURE INORGAN LONG A, 1989, 13TH INT RAD C RAD, V3, P229 MASSART DL, 1988, CHEMOMETRICS TXB MEGLEN RR, 1985, ACS S SER, V292, PCH18 MILLS I, 1988, QUANTITIES UNITS SYM MULLER RA, 1977, SCIENCE, V196, P489 PARR RM, 1990, 7 P S TRAC EL MAN AN PARR RM, 1979, COMPUTERS ACTIVATION, P544 PARR RM, 1985, IAEA2 PROGR REP POCKLINGTON WD, 1990, PURE APPL CHEM, V62, P149 PRIESNER C, 1991, 36TH IUPAC GEN ASS H PRIESNER C, 1989, CHEM INT, V11, P216 PURSER KH, 1977, REV PHYS APPL, V12, P1487 RAHN KA, 1985, SCIENCE, V228, P275 RAMDAHL T, 1984, SCI TOTAL ENVIRON, V36, P81 RIGAUDY J, 1979, NOMENCLATURE ORGANIC STAFFORD TW, 1990, QUATERNARY RES, V34, P111 SUBRAMANIAN KS, 1991, ACS S SER, V445 TUFTE ER, 1983, VISUAL DISPLAY QUANT TUKEY JW, 1977, EXPLORATORY DATA ANA UREY HC, 1952, PLANETS THEIR ORIGIN WAHLEN M, 1991, RADIOCARBON, V33, P257 WALDHOLZ M, 1987, WALL STREET J 0219, P51 YIOU F, 1990, 5TH INT C ACC MASS S, V52, P211 YOUDEN WJ, 1969, STATISTICAL TECHNIQU TC 11 BP 455 EP 472 PG 18 JI Pure Appl. Chem. PY 1992 PD APR VL 64 IS 4 GA HM383 PI OXFORD RP CURRIE LA NATL INST STAND & TECHNOL,GAITHERSBURG,MD 20899 J9 PURE APPL CHEM PA OSNEY MEAD, OXFORD, OXON, ENGLAND OX2 0EL UT ISI:A1992HM38300001 ER PT Journal AU LU, LJ SMITH, CR TI USE OF FLOW VISUALIZATION DATA TO EXAMINE SPATIAL - TEMPORAL VELOCITY AND BURST-TYPE CHARACTERISTICS IN A TURBULENT BOUNDARY-LAYER SO JOURNAL OF FLUID MECHANICS LA English DT Article NR 47 SN 0022-1120 PU CAMBRIDGE UNIV PRESS C1 LEHIGH UNIV,DEPT MECH ENGN & MECH,BETHLEHEM,PA 18015 ID LOW REYNOLDS-NUMBER; CHANNEL FLOW; WALL REGION; SHEAR-FLOW; STATISTICS; VORTICITY; SMOOTH AB It is well known that turbulence production in turbulent boundary layers occurs in short, energetic bursts; however, the relationship between the results of pointwise burst detection techniques and the spatial flow structure associated with such burst-type events has not been clearly established. To address this point, a study using VITA detection of burst-type events was done which allows the direct comparison between flow visualization results and quantitative, temporal velocity profile data for a flat-plate turbulent boundary layer. Using automated image processing of hydrogen-bubble flow visualization pictures, temporal velocity profile data are established using a corrected time-of-flight technique for velocity extraction. Using the visualization-derived data, spatial-temporal velocity-derived properties (partial derivative u/partial derivative y, partial derivative u/partial derivative t, etc.), as well as probe-type burst detection properties are established. In addition, temporal and ensemble-averaged burst-type characteristics are shown to be essentially identical to previous VITA-detected velocity probe results. The VITA approach is extended to establish the spatial extent of burst-type events and the ensemble-averaged spatial-temporal properties associated with VITA-based detection. By use of a regionalized detection procedure, the types of burst-type patterns are categorized and compared with the associated visualization sequences. CR ABERNATHY FH, 1977, 5TH P S TURB, P133 ALFREDSSON PH, 1984, PHYS FLUIDS, V27, P1974 BLACKWELDER RF, 1983, J FLUID MECH, V132, P87 BLACKWELDER RF, 1976, J FLUID MECH, V76, P89 BOGARD DG, 1986, J FLUID MECH, V162, P389 CERRA AW, 1983, FM11 LEH U DEPT ME M CLUTTER DW, 1961, AEROSPEACE ENG, V20 CORINO ER, 1969, J FLUID MECH, V37, P1 CORNELIUS K, 1977, 5TH P S TURB, P287 DAVIS W, 1966, ASME66WAE21 PAP ECKELMANN H, 1977, PHYS FLUIDS, V20, PS225 GRASS AJ, 1971, J FLUID MECH, V50, P233 HOGENES JHA, 1982, J FLUID MECH, V124, P363 JIMENEZ J, 1988, PHYS FLUIDS, V31, P1311 JOHANSEN JB, 1983, FM3 LEH U DEPT MECH JOHANSSON AV, 1987, J FLUID MECH, V175, P119 JOHANSSON AV, 1982, J FLUID MECH, V122, P259 JOHANSSON AV, 1989, P ZARIC INT SEMINAR KASAGI N, 1986, EXP FLUIDS, V4, P309 KIM HT, 1971, J FLUID MECH, V50, P133 KIM J, 1987, J FLUID MECH, V177, P133 KIM J, 1983, PHYS FLUIDS, V26, P2088 KLINE SJ, 1967, J FLUID MECH 4, V30, P741 LU LJ, 1985, EXP FLUIDS, V3, P349 LU LJ, 1988, FM14 LEH U DEP MECH LU LJ, 1991, IN PRESS EXPS FLUIDS LU LJ, 1985, THESIS LEHIGH U LUCHIK TS, 1987, J FLUID MECH, V174, P529 LUMLEY JL, 1967, ATMOSPHERIC TURBULEN, P166 METZLER SP, 1980, THESIS LEHIGH U MOSER RD, 1989, P ARIC INT SEMINAR W NYCHAS SG, 1973, J FLUID MECH, V61, P513 OFFEN GR, 1975, J FLUID MECH, V70, P209 OFFEN GR, 1974, J FLUID MECH, V62, P223 PERRY AE, 1982, J FLUID MECH, V119, P173 PURTELL LP, 1981, PHYS FLUIDS, V24, P802 ROBINSON SK, 1989, P ZARIC INT SEMINAR SCHLICHTING H, 1979, BOUNDARY LAYER THEOR SCHRAUB FA, 1965, T ASME D, V87, P429 SMITH CR, 1983, EXP FLUIDS, V1, P43 SMITH CR, 1989, P ZARIC INT SEMINAR SMITH CR, 1990, STRUCTURE TURBULENCE, P51 SPALART PR, 1988, J FLUID MECH, V187, P61 TIEDERMAN WG, 1989, P ZARIC INT SEMINAR WALKER JDA, 1990, P IUTAM S STRUCTURE, P101 WALLACE JM, 1977, J FLUID MECH, V83, P673 WHITE FM, 1974, VISCOUS FLUID FLOW TC 9 BP 303 EP 340 PG 38 JI J. Fluid Mech. PY 1991 PD NOV VL 232 GA GR864 PI NEW YORK RP LU LJ LEHIGH UNIV,DEPT MECH ENGN & MECH,BETHLEHEM,PA 18015 J9 J FLUID MECH PA 40 WEST 20TH STREET, NEW YORK, NY 10011-4211 UT ISI:A1991GR86400011 ER PT Journal AU NG, WY TI GENERALIZED COMPUTER-AIDED-DESIGN SYSTEM - A MULTIOBJECTIVE APPROACH SO COMPUTER-AIDED DESIGN LA English DT Article NR 15 SN 0010-4485 PU ELSEVIER SCI LTD C1 CHINESE UNIV HONG KONG,DEPT INFORMAT ENGN,SHA TIN,HONG KONG DE CAD PROCESS; DESIGN DATABASE; USER INTERFACE AB The generation of alternatives and tradeoff decision-making are the two main occupations of the design engineer. Despite the different practices and approaches of the many domains of engineering, viewed from a high-enough level, engineering design always proceeds as iterations of the two subtasks. The design process terminates when the 'convergence' of the two series of specifications and design alternatives are considered satisfactory. Subsequently, the final design is a product of much compromise, or matching, between wishes and possibilities. On the basis of this observation, a generalized computer-aided design system has been developed that models the design process as an interactive multiobjective programming process. The system features the maintenance of a design database that captures the series of specifications and design alternatives generated. Also, the provision of tools for data visualization and interaction supports the designer in the control and monitoring of the convergence, and the identification and making of tradeoff decisions in an informed manner along the way. CR CHORAFAS DN, 1988, ENG DATABASE CODD EF, 1970, COMMUN ACM, V13, P377 DATE CJ, 1980, INTRO DATABASE SYSTE, V1 FLETCHER R, 1987, PRACTICAL METHODS OP GOICOECHE AG, 1982, MULTIPLE OBJECTIVE D INSELBERG A, 1985, VISUAL COMPUT, V1, P69 KIWIEL KC, 1985, LECTURE NOTES MATH, V1133 NG WY, 1991, INFORM DECIS TECHNOL, V17, P133 NG WY, 1989, LECTURE NOTES SERIES, V132 NORMAN DA, 1986, USER CTR SYSTEM DESI ROSENTHAL RE, 1985, DECISION SCI, V16, P133 SAWARAGI Y, 1985, THEORY MULTIOBJECTIV SIMONS HA, 1957, MODELS MAN WIERZBICKI AP, 1982, MATH MODELLING, V3, P391 WIERZBICKI AP, 1979, QP79122 LLASA WORK P TC 2 BP 548 EP 553 PG 6 JI Comput.-Aided Des. 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PY 1991 PD DEC VL 16 IS 12 GA GN495 PI SAN MATEO RP RIVIER COLL,COMP SCI & MATH,RIVIER,NH J9 DR DOBBS J PA 411 BOREL AVE, SAN MATEO, CA 94402-3522 UT ISI:A1991GN49500015 ER PT Journal AU DOI, A AONO, M URANO, N SUGIMOTO, K TI DATA VISUALIZATION USING A GENERAL-PURPOSE RENDERER SO IBM JOURNAL OF RESEARCH AND DEVELOPMENT LA English DT Article NR 26 SN 0018-8646 PU IBM CORP C1 IBM JAPAN LTD,IBM YAMATO LAB,CIM SYST DEV,SHIMOTSURUMA 1623- 14,YAMATO,JAPAN IBM JAPAN LTD,TOKYO SCI CTR,TOKYO RES LAB,CHIYODA KU,TOKYO 102,JAPAN AB This paper describes a general approach to data visualization, based on the Rendering Subroutine Package (RSP). RSP is a general-purpose polygon-based renderer, and is IBM's first rendering application programming interface (API) for users who wish to develop their own applications. We present an overview of the system, details of the image synthesis tools, and several examples of the application of RSP to architectural CAD, molecular graphics, and computer tomography. 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Res. Dev. PY 1991 PD JAN-MAR VL 35 IS 1-2 GA GC204 PI ARMONK RP DOI A IBM JAPAN LTD,IBM YAMATO LAB,CIM SYST DEV,SHIMOTSURUMA 1623-14,YAMATO,JAPAN J9 IBM J RES DEVELOP PA OLD ORCHARD RD, ARMONK, NY 10504 UT ISI:A1991GC20400005 ER PT Journal AU STOLL, EP TI PICTURE-PROCESSING AND 3-DIMENSIONAL VISUALIZATION OF DATA FROM SCANNING TUNNELING AND ATOMIC FORCE MICROSCOPY SO IBM JOURNAL OF RESEARCH AND DEVELOPMENT LA English DT Review NR 48 SN 0018-8646 PU IBM CORP C1 IBM CORP,ZURICH RES LAB,DIV RES,CH-8803 RUSCHLIKON,SWITZERLAND ID LINEAR RESPONSE THEORY; ELECTRONIC-STRUCTURE; GRAPHITE SURFACE; REAL-SPACE; RESOLUTION; MODEL; IMAGE; RECONSTRUCTION; POTENTIOMETRY; STATES AB We present an overview of the current status of picture processing and three-dimensional visualization of data from scanning tunneling microscopy and related techniques. The topics we cover include the physical basis of the resolution limit and noise sources in scanning microscopes, the design of restoration filters, and methods of visualizing surface contours and other surface properties by use of shadowing, contour lines, and superimposed colors. Postprocessed images of gold, graphite, biological molecules, the active zone of a laser diode, and silicon illustrate the outstanding quality of these methods. 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Res. Dev. PY 1991 PD JAN-MAR VL 35 IS 1-2 GA GC204 PI ARMONK RP STOLL EP IBM CORP,ZURICH RES LAB,DIV RES,CH-8803 RUSCHLIKON,SWITZERLAND J9 IBM J RES DEVELOP PA OLD ORCHARD RD, ARMONK, NY 10504 UT ISI:A1991GC20400007 ER PT Journal AU TREINISH, LA GOETTSCHE, C TI CORRELATIVE VISUALIZATION TECHNIQUES FOR MULTIDIMENSIONAL DATA SO IBM JOURNAL OF RESEARCH AND DEVELOPMENT LA English DT Article NR 17 SN 0018-8646 PU IBM CORP C1 IBM CORP,THOMAS J WATSON RES CTR,DIV RES,YORKTOWN HTS,NY 10598 ID TOTAL OZONE AB Critical to the understanding of data is the ability to provide pictorial or visual representations of those data, particularly in support of correlative data analysis. Despite the many advances in visualization techniques for scientific data over the last several years, there are still significant problems in bringing today's hardware and software technology into the hands of the typical scientist. For example, there are computer science domains other than computer graphics, such as data management, that are required to make visualization effective. Well-defined, flexible mechanisms for data access and management must be combined with rendering algorithms, data transformations, etc. to form a generic visualization pipeline. A generalized approach to data visualization is critical for the correlative analysis of distinct, complex, multidimensional data sets in the space and earth sciences. Different classes of data representation techniques must be used within such a framework, which can range from simple, static two- and three- dimensional line plots to animation, surface rendering, and volumetric imaging. Static examples of actual data analyses will illustrate the importance of an effective pipeline in a data visualization system. 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PY 1991 PD JAN-MAR VL 35 IS 1-2 GA GC204 PI ARMONK RP TREINISH LA IBM CORP,THOMAS J WATSON RES CTR,DIV RES,YORKTOWN HTS,NY 10598 J9 IBM J RES DEVELOP PA OLD ORCHARD RD, ARMONK, NY 10504 UT ISI:A1991GC20400016 ER PT Journal AU PAPAMICHAEL, N BORNER, B HUSTEDT, H TI CONTINUOUS AQUEOUS PHASE EXTRACTION OF PROTEINS - AUTOMATED ONLINE MONITORING OF FUMARASE ACTIVITY AND PROTEIN- CONCENTRATION SO JOURNAL OF CHEMICAL TECHNOLOGY AND BIOTECHNOLOGY LA English DT Article NR 10 SN 0268-2575 PU JOHN WILEY & SONS LTD C1 GESELL BIOTECHNOL FORSCH GMBH,MASCHERODER WEG 1,W-3300 BRAUNSCHWEIG,GERMANY DE ENZYME (FUMARASE); PROTEIN; AUTOMATED ASSAY; BIURET; AQUEOUS PHASE EXTRACTION ID INJECTION ANALYSIS FIA; 2-PHASE SYSTEMS; ENZYMES; RECOVERY; SCALE AB Automated assay techniques are described for on-line measurements of fumarase activity and total protein concentration, including in-line sample dilution and sample multiplexing during continuous aqueous phase extraction. 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MOLECULAR GRAPHICS LA English DT Article NR 22 SN 0263-7855 PU BUTTERWORTH-HEINEMANN C1 SALK INST BIOL STUDIES,STRUCT BIOL LAB,10010 N TORREY PINES RD,LA JOLLA,CA 92037 DE AVS; CCP4; DATA-FLOW TOOLKITS; MACROMOLECULAR CRYSTALLOGRAPHY; GRAPHICAL USER INTERFACES; VISUALIZATION AB This article describes the integration of programs from the widely used CCP4 macromolecular crystallography package into a modern data flow visualization environment (application visualization system [AVS]), which provides a simple graphical user interface, a visual programming par adigm, and a variety of 1-, 2-, and 3-D data visualization tools for the display of graphical information and the results of crystallographic calculations, such as electron density and Patterson maps. The CCP4 suite comprises a number of separate Fortran 77 programs, which communicate via common file formats. Each program is encapsulated into an AVS macro module, and may be linked to others in a data flow network, reflecting the nature of many crystallographic calculations. Named pipes are used to pass input parameters from a graphical user interface to the program module, and also to intercept line printer output, which can be filtered to extract graphical information and significant numerical parameters. These may be passed to downstream modules, permitting calculations to be automated if no user interaction is required, or giving the user the opportunity to make selections in an interactive manner. 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PY 1995 PD OCT VL 13 IS 5 GA TH340 PI WOBURN RP WILD DL SALK INST BIOL STUDIES,STRUCT BIOL LAB,10010 N TORREY PINES RD,LA JOLLA,CA 92037 J9 J MOL GRAPHICS PA 225 WILDWOOD AVE #UNITB PO BOX 4500, WOBURN, MA 01801-2084 UT ISI:A1995TH34000005 ER PT Journal AU KRAAK, MJ MULLER, JC ORMELING, F TI GIS-CARTOGRAPHY - VISUAL DECISION-SUPPORT FOR SPATIOTEMPORAL DATA HANDLING SO INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SYSTEMS LA English DT Article NR 26 SN 0269-3798 PU TAYLOR & FRANCIS LTD LONDON C1 DELFT UNIV TECHNOL,FAC GEODESY,THYSSEWEG 11,2629 JA DELFT,NETHERLANDS RUHR UNIV BOCHUM,INST GEOG,D-44801 BOCHUM,GERMANY UNIV UTRECHT,FAC GEOG SCI,3508 TC UTRECHT,NETHERLANDS AB This paper describes a new joint Dutch research initiative 'GIS-cartography', combining the research efforts of the cartographers of Utrecht University, Delft University of Technology and the International Institute for Aerospace Survey and Earth Sciences (ITC) in Enschede. The research initiative focuses on the quantification and visualization of data quality, which will be placed in the context of providing automated visual decision support in specific map use strategies. As these map use strategies can only be performed if the relevant cartographic images can be created, studies of both physical access to the data, user interfaces and the provision of sufficient support to allow the user to understand and to derive sensible conclusions from the data are included in the project. Before modules automatically visualizing data quality can be implemented, data documentation, standardization and integration have to be effected, therefore these issues are also covered. 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These methods consist of four procedures, including (1) sampling of a surface within the cortex, (2) reconstruction of a three-dimensional model of that surface, (3) unfolding of the surface to generate a two-dimensional cortical map, and (4) visualization of data on the model and the map. These methods produce structurally accurate representations of the cortex and have practical advantages over previous manual and automated approaches for flattening the cortex. We illustrate the application of these methods to neuroanatomical data obtained from histological sections of cerebral cortex in the macaque monkey. The approach should be equally useful for structural and functional studies in other species, including humans. 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Cortex PY 1995 PD NOV-DEC VL 5 IS 6 GA TF333 PI CARY RP CARMAN GJ WASHINGTON UNIV,SCH MED,DEPT ANAT & NEUROBIOL,600 S EUCLID AVE,ST LOUIS,MO 63110 J9 CEREB CORTEX PA JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 UT ISI:A1995TF33300003 ER PT Journal AU HEATH, MT MALONY, AD ROVER, DT TI THE VISUAL-DISPLAY OF PARALLEL PERFORMANCE DATA SO COMPUTER LA English DT Article NR 12 SN 0018-9162 PU IEEE COMPUTER SOC C1 UNIV ILLINOIS,DEPT COMP SCI,DIGITAL COMP LAB 2304,1304 W SPRINGFIELD AVE,URBANA,IL 61801 UNIV ILLINOIS,NATL CTR SUPERCOMP APPLICAT,URBANA,IL 61801 UNIV OREGON,DEPT COMP & INFORMAT SCI,EUGENE,OR 97403 MICHIGAN STATE UNIV,DEPT ELECT ENGN,E LANSING,MI 48824 ID VISUALIZATION AB Data visualization can help users decipher scientific and engineering data and better comprehend large, complex data sets. The authors present a high-level abstract model for performance visualization that relates behavior abstractions to visual representations in a structured way. This model is based on two principles: Displays of performance information are linked directly to parallel performance models, and performance visualizations are designed and applied in an integrated environment. The authors explain some advantages of adhering to these principles. They begin by establishing a context for users to clearly understand performance information, defining terms such as perspective, semantic context, and subview mapping. Next, they describe the techniques used to scale graphical views as data sets become very large. Finally, they discuss concepts such as user perception and interaction, comparisons and cross-correlations between related views or representations, and information extraction. On the basis of this conceptual foundation, the authors present examples of practical applications for the model. These case studies address topics such as concurrency and communication in data- parallel computation, access patterns for data distributions, and critical paths in parallel computation. The authors conclude by discussing the relationship between performance visualization and general scientific visualization. CR COUCH AL, 1993, J PARALLEL DISTR COM, V18, P195 HACKSTADT S, 1993, CISTR9323 U OR DEP C HEATH M, 1995, IEEE PARALLEL DISTRI, V3 HEATH MT, 1991, IEEE SOFTWARE, V8, P29 KELLER P, 1993, VISUAL CUES PRACTICA LEBLANC TJ, 1990, J PARALLEL DISTR COM, V9, P203 MILLER BP, 1993, J PARALLEL DISTR COM, V18, P265 ROVER D, 1993, J PARALLEL DISTRIBUT, V18, P219 TUFTE E, 1990, ENVISIONING INFORMAT TUFTE ER, 1983, VISUAL DISPLAY QUANT WORLEY PH, 1992, CONCURRENCY-PRACT EX, V4, P269 YAN J, 1993, AUTOMATED INSTRUMENT TC 10 BP 21 EP & PG 0 JI Computer PY 1995 PD NOV VL 28 IS 11 GA TC583 PI LOS ALAMITOS RP HEATH MT UNIV ILLINOIS,DEPT COMP SCI,DIGITAL COMP LAB 2304,1304 W SPRINGFIELD AVE,URBANA,IL 61801 J9 COMPUTER PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1264 UT ISI:A1995TC58300013 ER PT Journal AU FRITZKE, B TI GROWING GRID - A SELF-ORGANIZING NETWORK WITH CONSTANT NEIGHBORHOOD RANGE AND ADAPTATION STRENGTH SO NEURAL PROCESSING LETTERS LA English DT Article NR 8 SN 1370-4621 PU D FACTO PUBLICATIONS C1 RUHR UNIV BOCHUM,INST NEUROINFORMAT,D-44780 BOCHUM,GERMANY AB We present a novel self-organizing network which is generated by a growth process. The application range of the model is the same as for Kohonen's feature map: generation of topology- preserving and dimensionality-reducing mappings, e.g., for the purpose of data visualization. The network structure is a rectangular grid which, however, increases its size during self-organization. By inserting complete rows or columns of units the grid may adapt its height/width ratio to the given pattern distribution. Both the neighborhood range used to co- adapt units in the vicinity of the winning unit and the adaptation strength are constant during the growth phase. This makes it possible to let the network grow until an application- specific performance criterion is fulfilled or until a desired network size is reached. A final approximation phase with decaying adaptation strength fine-tunes the-network. CR BAUER HU, 1995, TR95030 INT COMP SCI FRITZKE B, 1995, ADV NEURAL INFORMATI, P625 FRITZKE B, 1993, ADV NEURAL INFORMATI, V5, P123 FRITZKE B, 1994, NEURAL NETWORKS, V7, P1441 KANGAS JA, 1990, IEEE T NEURAL NETWOR, V1, P93 KOHONEN T, 1982, BIOL CYBERN, V44, P135 MARTINETZ T, 1991, ARTIFICIAL NEURAL NE, P397 RODRIGUES JS, 1990, P INNC 90 INT NEUR N, P813 TC 11 BP 9 EP 13 PG 5 JI Neural Process. Lett. PY 1995 PD SEP VL 2 IS 5 GA TB046 PI BRUSSELS RP RUHR UNIV BOCHUM,INST NEUROINFORMAT,D-44780 BOCHUM,GERMANY J9 NEURAL PROCESS LETT PA 45 RUE MASUI, B-1210 BRUSSELS, BELGIUM UT ISI:A1995TB04600003 ER PT Journal AU BAKES, CM GOLDBERG, FN TI APPLICATIONS OF FIBER OPTIC NETWORKS IN HIGH-TECHNOLOGY RESEARCH SO INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY LA English DT Article NR 25 SN 0952-8091 PU INDERSCIENCE ENTERPRISES LTD C1 KENT STATE UNIV,GRAD SCH MANAGEMENT,DEPT ADM SCI,KENT,OH 44242 NASA,LEWIS RES CTR,TELECOMMUN & NETWORKING BRANCH,CLEVELAND,OH 44135 DE FIBER DISTRIBUTED DATA INTERFACE (FDDI); FIBER OPTIC NETWORK; LOCAL AREA NETWORK (LAN); SYNCHRONOUS OPTICAL NETWORK (SONET); TELECOMMUNICATIONS ID FDDI AB The NASA Lewis Research Center has active programmes in fluid dynamics and solid dynamics. These require a communications network capable of transporting multimedia traffic, including data, voice, interactive and non-interactive video, real-time visualization, and data gathering from scientific experiments. The use of powerful desktop workstations, which operate as standalone devices, work cooperatively in local clusters, operate in client server mode, access central computers, and address remote sites, also impacts on network requirements. This paper provides an overview of FDDI and SONET networks and investigates their roles in supporting high-level technological research. FDDI's topology, reliability, traffic classes, data encoding, token ring operation, timers, network management issues, and candidate applications are discussed. The multiplexing hierarchy, optical signal format, OAM capability, survivability, and candidate applications of SONET networks are explored. Interoperability issues, with the SDH international standard, ATM packet switching, and BISDN networks, are also addressed. CR 1987, ANSI X31391987 AM NA 1988, ANSI X31481988 AM NA 1990, ANSI X31661990 AM NA BALCER R, 1990, IEEE COMMUN MAG, V28, P21 BALLET R, 1989, IEEE COMMUN MAG MAR, P8 BENINGER T, 1991, SONET BASICS BOEHM RJ, 1990, IEEE LCS MAGAZIN MAY, P8 BUX W, 1989, P IEEE, V77, P238 CHING YC, 1991, IEEE LTS NOV, P44 EAMES TR, 1991, IEEE LTS NOV, P24 FRANZEN M, 1991, COMPUT NETWORKS ISDN, V23, P51 HAC A, 1989, COMPUTER NOV, P26 HAQUE I, 1991, IEEE LTTS NOV, P30 JAIN R, 1991, IEEE LTS, V2, P16 JOHNSON MJ, 1986, APRR P SEM REAL TIM, P145 JOHNSON MJ, 1986, COMPUTER NETWORKS IS, V2, P121 MAZZAFERRO JF, 1990, J DATA COMPUTER COMM, V3, P15 MCCOOL JF, 1988, DATA COMMUNICATIONS, V17, P185 PULLEN RW, 1989, TELEPHONE ENG M 0215 ROSS FE, 1990, COMPUT COMMUN, V20, P16 ROSS FE, 1989, IEEE J SEL AREA COMM, V7, P1043 SANDESARA NB, 1990, IEEE COMMUN MAG, V28, P26 SEVCIK KC, 1987, IEEE T SOFTWARE ENG, V13, P376 TO M, 1991, IEEE LTS, P19 WU T, 1992, FIBER NETWORK SERVIC TC 0 BP 172 EP 189 PG 18 JI Int. J. Comput. Appl. Technol. PY 1995 VL 8 IS 3-4 GA TA187 PI GENEVA AEROPORT RP BAKES CM KENT STATE UNIV,GRAD SCH MANAGEMENT,DEPT ADM SCI,KENT,OH 44242 J9 INT J COMPUT APPL TECHNOL PA WORLD TRADE CENTER BLDG 110 AVE LOUIS CASAI CP 306, CH-1215 GENEVA AEROPORT, SWITZERLAND UT ISI:A1995TA18700005 ER PT Journal AU STHILAIRE, P TI SCALABLE OPTICAL ARCHITECTURE FOR ELECTRONIC HOLOGRAPHY SO OPTICAL ENGINEERING LA English DT Article NR 17 SN 0091-3286 PU SOC PHOTO-OPT INSTRUM ENG C1 INTERVAL RES CORP,1801 PAGE MILL RD,BLDG C,PALO ALTO,CA 94304 DE SYNTHETIC-APERTURE HOLOGRAPHY; BRAGG CELL; FOURIER TRANSFORM; SPATIAL FREQUENCY ANALYSIS ID SPATIAL LIGHT-MODULATOR AB Synthetic-aperture holography represents an attractive approach to the problem of three-dimensional data visualization, However, scaling the technology beyond a proof of concept requires a modification of the original architecture to allow for an increased hologram space-bandwidth product. It is demonstrated how the Fourier domain of a scanned aperture display can be segmented in small domains, each being processed by a different scanning element. The behavior of a display exhibiting such a segmentation is described, and the conditions under which images can be produced without incurring significant degradation are derived. A large-scale display based on the implementation of these concepts is demonstrated. CR ALLEN MG, 1990, SENSOR ACTUAT A-PHYS, V21, P211 BADEMIAN L, 1986, OPT ENG, V25, P303 BARDOS A, 1974, APPL OPTICS, V13, P832 BOVE VM, 1991, P SOC PHOTO-OPT INS, V1605, P886 COHEN MG, 1967, J APPL PHYS, V38, P3821 GOODMAN JW, 1968, INTRO FOURIER OPTICS, P101 JOHNSON RV, 1980, P SOC PHOTO-OPT INS, V222, P15 MOK F, 1986, OPT LETT, V11, P748 MYERS LM, 1936, TV SHORTWAVE WOR APR, P201 PAPE DR, 1992, OPT ENG, V31, P2148 PETERSEN KE, 1980, IBM J RES DEV, V24, P631 PSALTIS D, 1984, OPT ENG, V23, P698 RANDOLPH J, 1971, APPL OPTICS, V10, P1383 SIEGER J, 482665, GB STHILAIRE P, 1992, J OPT SOC AM A, V9, P1969 STHILAIRE P, 1990, P SOC PHOTOOPT INSTR, V1212, P174 VANDERLUGT A, 1982, APPL OPTICS, V21, P1092 TC 4 BP 2900 EP 2911 PG 12 JI Opt. Eng. PY 1995 PD OCT VL 34 IS 10 GA RZ708 PI BELLINGHAM RP STHILAIRE P INTERVAL RES CORP,1801 PAGE MILL RD,BLDG C,PALO ALTO,CA 94304 J9 OPT ENG PA PO BOX 10, BELLINGHAM, WA 98227-0010 UT ISI:A1995RZ70800011 ER PT Journal AU MORTON, CM KINCAID, DT TI A MODEL FOR CODING POLLEN SIZE IN REFERENCE TO PHYLOGENY USING EXAMPLES FROM THE EBENACEAE SO AMERICAN JOURNAL OF BOTANY LA English DT Article NR 10 SN 0002-9122 PU BOTANICAL SOC AMER INC C1 NEW YORK BOT GARDEN,BRONX,NY 10458 CUNY HERBERT H LEHMAN COLL,DEPT BIOL SCI,BRONX,NY 10468 ID CHARACTERS AB Most genetically based features should be available for use in cladistic analysis. Palynologists routinely measure polar (P) and equatorial (E) axes and place pollen into size classes defined by earlier pollen workers. Grouping of pollen into globally arbitrary classes may not correspond to statistically significant differences among the taxa of a study. We propose a model using conventional statistical procedures coupled with data visualization and Monte Carlo simulation. This approach is not a final solution to the general problem of coding continuous characters into discrete states; it is an attempt to address the problems of character state delimitation in pollen morphology. We suggest that the coding of continuous measurement variables (e.g., P, E) into character states should be done following a logical sequence of interactive visualization (2D and 3D) of bivariate frequency distributions including the inspection of prediction and confidence ellipses (e.g., 99%), and use of ANOVA. We illustrate our approach using realistic pollen data sets generated by a computer program (POLSIM) written to perform Monte Carlo sampling from normally distributed statistical populations of polar and equatorial axes. Our model is then applied to an original data set of 4,134 pollen grains from the Ebenaceae, resulting in the coding of the four genera into three character states for pollen size. CR ALMEIDA MT, 1984, TAXON, V33, P405 ARCHIE JW, 1985, SYST ZOOL, V34, P326 CHAPPILL JA, 1989, CLADISTICS, V5, P217 ERDTMAN G, 1971, POLLEN MORPHOLOGY PL ERDTMAN G, 1960, SVENSK BOTANISK TIDS, V54, P561 NILSSON S, 1992, ERDTMANS HDB PALYNOL NOREEN EW, 1989, COMPUTER INTENSIVE M SOKAL RR, 1981, BIOMETRY STEVENS PF, 1991, SYST BOT, V16, P553 WALKER JW, 1975, ANN MO BOT GARD, V62, P664 TC 2 BP 1173 EP 1178 PG 6 JI Am. J. Bot. PY 1995 PD SEP VL 82 IS 9 GA RV187 PI COLUMBUS RP NEW YORK BOT GARDEN,BRONX,NY 10458 J9 AMER J BOT PA OHIO STATE UNIV-DEPT BOTANY 1735 NEIL AVE, COLUMBUS, OH 43210 UT ISI:A1995RV18700013 ER PT Journal AU KIM, JY TI TECPLOT - INTERACTIVE DATA VISUALIZATION FOR SCIENTISTS AND ENGINEERS - VERSION-6.0 SO QUARTERLY REVIEW OF BIOLOGY LA English DT Software Review NR 0 SN 0033-5770 PU UNIV CHICAGO PRESS C1 YALE UNIV,NEW HAVEN,CT 06520 TC 0 BP 382 EP 382 PG 1 JI Q. Rev. Biol. PY 1995 PD SEP VL 70 IS 3 GA RV423 PI CHICAGO RP KIM JY YALE UNIV,NEW HAVEN,CT 06520 J9 QUART REV BIOL PA 5720 S WOODLAWN AVE, CHICAGO, IL 60637 UT ISI:A1995RV42300004 ER PT Journal AU ANANYEVA, ND BLAGODATSKAYA, YV ORLINSKIY, DB MYAKSHINA, TN BRYNSKIKH, MN TI ASSESSMENT OF ANTHROPOGENIC IMPACT ON SOILS WITH THE AID OF LARGE-SCALE SURVEYS SO EURASIAN SOIL SCIENCE LA English DT Article NR 18 SN 1064-2293 PU SCRIPTA TECHNICA PUBL C1 RUSSIAN ACAD SCI,INST SOIL SCI & PHOTOSYNTH,PUSHCHINO 142292,RUSSIA DE ASSESSMENT OF ANTHROPOGENIC IMPACT ON SOILS; LARGE-SCALE SURVEYS AB A system for computer analysis and visualization of data for survey with the purpose of using it for rating soil has been proposed: The impact of different ecological factors on soil microbial biomass has been evaluated (substrate-induced respiration method) in the course of soil studies in the Upper Oka River basin in Serpukhov region (scale 1:100,000). The rate of agricultural utilization of the area (71 percent) proved to be the most important agent changing the microbial biomass. CR 1987, SAS APPLICATIONS GUI AFIFI AA, 1982, STATISTICHESKII ANAL ANANYEVA ND, 1986, AGROKHIMIYA, P84 ANANYEVA ND, 1993, POCHVOVEDENIYE ANDERSSON BA, 1975, LIPIDS, V10, P215 DOMSCH KH, 1979, Z PFLANZ BODENKUNDE, V142, P520 GALIULIN RV, 1990, AGROKHIMIYA, P97 GRINCHENKO TA, 1991, AGROKHIMIYA, P52 GUZEV VS, 1991, POCHVOVEDENIE, P51 KISELYOV AN, 1985, PROGNOZNOYE BIOGEOGR KOZACHENKO TI, 1991, GEOGRAFIYA PRIRODNYY, P5 MINEYEV VG, 1992, POCHVOVEDENIE, P61 RUDENKO LG, 1992, GEOGRAFIYA PRIRODNYE, P13 SOKOLOV MS, 1979, METODY PROBLEMY EKOT, P20 TYURIN IV, 1959, POCHEVENNAYA YOMKA VASILYEVA GK, 1991, AGROKHIMIYA, P81 VASILYEVA GK, 1991, AGROKHIMIYA, P105 ZVYAGINTZEV DG, 1989, MIKROORGANIZMY OKHRA TC 0 BP 78 EP 87 PG 10 JI Eurasian Soil Sci. PY 1995 PD APR VL 27 IS 4 GA RR759 PI SILVER SPRING RP ANANYEVA ND RUSSIAN ACAD SCI,INST SOIL SCI & PHOTOSYNTH,PUSHCHINO 142292,RUSSIA J9 EURASIAN SOIL SCI PA 7961 EASTERN AVE, SILVER SPRING, MD 20910 UT ISI:A1995RR75900012 ER PT Journal AU CARROLL, RJ LI, KC TI BINARY REGRESSORS IN DIMENSION REDUCTION MODELS - A NEW LOOK AT TREATMENT COMPARISONS SO STATISTICA SINICA LA English DT Article NR 26 SN 1017-0405 PU STATISTICA SINICA C1 TEXAS A&M UNIV,DEPT STAT,COLLEGE STN,TX 77843 UNIV CALIF LOS ANGELES,DEPT MATH,LOS ANGELES,CA 90024 DE CONDITIONING; DIMENSION REDUCTION; LINEAR DESIGN CONDITION; NONPARAMETRIC CURVE FITTING; RANDOMIZATION; SIR; TREATMENT EFFECT ID SLICED INVERSE REGRESSION; RANK CORRELATION ESTIMATOR; PARTLY LINEAR-MODEL; DATA VISUALIZATION; CONVERGENCE-RATES; CURVES; LINK AB In this paper, new aspects of treatment comparison are brought out via the dimension reduction model of Li (1991) for general regression settings. Denoting the treatment indicator by Z and the covariate by X, the model Y = g(v' X + theta Z, epsilon) is discussed in detail. Estimates of upsilon and a are obtained without assuming a functional form for g. Our method is based on the use of SIR (sliced inverse regression) for reducing the dimensionality of the covariate, followed by a partial-inverse mean matching method for estimating the treatment effect theta. Asymptotic theory and a simulation study are presented. CR BRILLINGER DR, 1991, J AM STAT ASSOC, V86, P333 CARROLL RJ, 1992, J AM STAT ASSOC, V87, P1040 CHEN H, 1991, ANN STAT, V19, P142 CHEN H, 1988, ANN STAT, V16, P136 COOK RD, 1994, J AM STAT ASSOC, V89, P177 COOK RD, 1994, J AM STAT ASSOC, V89, P592 COOK RD, 1991, J AM STAT ASSOC, V86, P328 DUAN N, 1991, ANN STAT, V19, P505 ENGLE RF, 1986, J AM STAT ASSOC, V81, P310 HALL P, 1993, ANN STAT, V21, P867 HALL P, 1989, ANN STAT, V17, P573 HAN AK, 1987, J ECONOMETRICS, V35, P303 HARDLE W, 1993, ANN STAT, V21, P157 HARDLE W, 1990, ANN STAT, V18, P63 HARDLE W, 1989, J AM STAT ASSOC, V84, P986 HECKMAN NE, 1986, J ROY STAT SOC B MET, V48, P244 HSING TL, 1992, ANN STAT, V20, P1040 KNEIP A, 1992, ANN STAT, V20, P1266 LI KC, 1992, J AM STAT ASSOC, V87, P1025 LI KC, 1991, J AM STAT ASSOC, V86, P316 LI KC, 1992, PROBABILITY STAT, P138 RICE J, 1986, STAT PROBABIL LETT, V4, P203 SAMAROV AM, 1993, J AM STAT ASSOC, V88, P836 SHERMAN RP, 1993, ECONOMETRICA, V61, P123 SPECKMAN P, 1988, J ROY STAT SOC B MET, V50, P413 TIERNEY L, 1990, LISP STAT OBJECT ORI TC 4 BP 667 EP 688 PG 22 JI Stat. Sin. PY 1995 PD JUL VL 5 IS 2 GA RN080 PI WINNIPEG RP TEXAS A&M UNIV,DEPT STAT,COLLEGE STN,TX 77843 J9 STAT SINICA PA C/O DR S W CHENG,MANAG EDITOR DEPT STAT UNIV MANTOBA, WINNIPEG MB R3T 2N2, CANADA UT ISI:A1995RN08000018 ER PT Journal AU MOHAN, R ROTHENBERG, L REINSTIEN, L LING, CC TI IMAGING IN 3-DIMENSIONAL CONFORMAL RADIATION-THERAPY SO INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY LA English DT Article NR 40 SN 0899-9457 PU JOHN WILEY & SONS INC C1 MEM SLOAN KETTERING CANC CTR,1275 YORK AVE,NEW YORK,NY 10021 ID TREATMENT PLANNING SYSTEM; PORTAL IMAGES; LOCAL-CONTROL; EYE- VIEW; PATIENT; CANCER; VERIFICATION; PROSTATE; SURVIVAL; SECTIONS AB By and large, radiation therapy is a noninvasive method of the treatment of cancer requiring knowledge of the precise location and extent of the disease to be destroyed and the organs to be protected from radiation damage. Images have always played a central role in providing the requisite information for this mode of cancer treatment. Different types of images, such as computed tomography (CT); magnetic resonance imaging (MRI), positron emission tomographic (PET), simulator, etc., are used to varying degrees depending upon their relevance to radiation oncology as well as their accessibility. It is often necessary to merge data from various types of images. The availability of three-dimensional information from tomographic images has allowed the introduction of three-dimensional conformal radiation therapy (3DCRT) methods. Images are employed for diagnosing and establishing the extent of the disease, planning and delivery treatments, and evaluating the effectiveness of the treatment in controlling the disease and assessing the damage to normal tissues. Each image type has a unique informational content of importance to radiation oncology. To extract the maximum information from images, it is necessary to employ various image processing tools. These tools allow us to perform such functions as (1) image enhancement; (2) image correlation to register information from various images; (3) segmentation of images to extract the surface outlines of the tumor volume and normal anatomic structures; and (4) two- and three dimensional data visualization. One important aspect of planning radiation treatments is the computation of dose distribution in the patient for a proposed configuration of radiation beams. This step requires tracing rays in a three- dimensional CT image data set to compute radiologic path lengths through the patient's body. Although images are employed to a great advantage in radiation oncology, many problems still remain to be solved. Of the various 3DCRT tasks, the outlining of contours of the volume of intended treatment and normal anatomy on images is highly labor-intensive and fraught with uncertainty. In addition, the integration of data from various imaging modalities is difficult and error prone because of distortions inherent in imaging and also because of the motion, deformation, and displacement of patients and their internal anatomy. Investigations are in progress to find solutions to these problems. (C) 1995 John Wiley & Sons, Inc. CR 1993, DIGITAL IMAGING COMM 1993, GUIDE TELERADIOLOGY AUSTINSEYMOUR M, 1994, INT J RAD ONCOL B S1, V30, P117 BALDWIN BC, 1993, INT J RADIAT ONCOL, V27, P181 BEIER T, 1992, COMPUT GRAPHICS, V26, P35 BIGGS PJ, 1985, INT J RADIAT ONCOL, V11, P635 CANNY J, 1986, IEEE T PATTERN ANAL, V8, P679 CHEN GTY, 1989, COMPUT MED IMAG GRAP, V13, P235 CHEN QS, 1994, IEEE T PATTERN ANAL, V16, P1156 DAVIS LS, 1975, COMPUTER GRAPHICS IM, V4, P248 FUKS Z, 1991, INT J RADIAT ONCOL, V21, P537 GILHUIJS KGA, 1993, MED PHYS, V20, P667 GOITEIN M, 1983, INT J RADIAT ONCOL, V9, P777 GOITEIN M, 1983, INT J RADIAT ONCOL, V9, P789 HAZUKA MB, 1993, INT J RADIAT ONCOL, V27, P273 JONES SM, 1991, MED PHYS, V18, P1116 KESSLER ML, 1987, COMPUTER TECHNIQUES KESSLER ML, 1991, INT J RADIAT ONCOL, V21, P1653 LAWRENCE TS, 1992, INT J RADIAT ONCOL, V24, P781 LEIBEL SA, 1994, INT J RADIAT ONCOL, V20, P55 LEIBEL SA, 1991, INT J RADIAT ONCOL, V21, P805 LEIBEL SA, 1992, SEMIN RADIAT ONCOL, V2, P274 LICHTER AS, 1991, INT J RADIAT ONCOL, V21, P853 LICHTER AS, 1995, NEW ENGL J MED, V32, P371 LOVELOCK DM, 1994, MED PHYS, V21, P886 MACKIE TR, 1994, TOMOTHERAPY PROPOSAL, P176 MCSHAN DL, 1979, BRIT J RADIOL, V52, P478 MOHAN R, 1988, INT J RADIAT ONCOL, V15, P481 MOHAN R, 1987, INT J RADIAT ONCOL, V13, P1247 MUNRO P, 1995, SEMIN RADIAT ONCOL, V5, P115 PELIZZARI CA, 1987, REGISTRATION MULTIPL PHILLIPS MH, 1991, INT J RADIAT ONCOL, V20, P881 PIZER SM, 1987, COMPUT VISION GRAPH, V39, P355 SANDLER HM, 1992, RADIOTHER ONCOL, V23, P53 SHEROUSE GW, 1987, INT J RADIAT ONCOL, V13, P801 SHIU AS, 1987, INT J RADIAT ONCOL, V13, P1589 SUIT HD, 1982, CANCER, V50, P1227 SUIT HD, 1992, INT J RADIAT ONCOL, V23, P653 VANHERK M, 1994, MED PHYS, V21, P1163 WEISMEYER MD, 1994, 11TH P INT C US COMP, P222 TC 0 BP 14 EP 32 PG 19 JI Int. J. Imaging Syst. Technol. PY 1995 PD SPR VL 6 IS 1 GA RL772 PI NEW YORK RP MOHAN R MEM SLOAN KETTERING CANC CTR,1275 YORK AVE,NEW YORK,NY 10021 J9 INT J IMAGING SYST TECHNOL PA 605 THIRD AVE, NEW YORK, NY 10158-0012 UT ISI:A1995RL77200004 ER PT Journal AU JERN, M TI CUSTOM WIDGETS FOR INTERACTIVE VISUALIZATION USING X AND MOTIF SO COMPUTERS & GRAPHICS LA English DT Article NR 10 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 UNIRAS,AVS,15 BLOKKEN,DK-3460 BIRKEROD,DENMARK AB A suite of 30 custom widgets designed specifically for interactive data visualization is presented. Custom widgets are an extension of standard widget toolkits such as Motif. They are object-oriented components which can provide additional graphical user interface (GUI) functionality. Widgets are ideally suited standard programming tools for direct manipulation (point and click) operations in X Motif. CR 1994, ICS WIDGET DATABOOK 1994, WIDGET WRITERS GUIDE BERLAGE T, 1992, OSF MOTIF CONCEPTS P CULWIN F, 1993, X MOTIF PROGRAMMERS CUNNINGHAM S, 1992, COMPUTER GRAPHICS US JERN M, 1993, ANIMATION SCI VISUAL JERN M, 1992, X J, V2, P42 LEE G, 1993, OBJECT ORIENTED GUI MIKES, 1992, X J, V1, P28 YOUNG DA, 1992, OBJECT ORIENTED PROG TC 0 BP 189 EP 197 PG 9 JI Comput. Graph. PY 1995 PD MAR-APR VL 19 IS 2 GA RE241 PI OXFORD RP JERN M UNIRAS,AVS,15 BLOKKEN,DK-3460 BIRKEROD,DENMARK J9 COMPUT GRAPH PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1995RE24100003 ER PT Journal AU CAERTS, C LAUWEREINS, R PEPERSTRAETE, JA TI PDG - A PROCESS-LEVEL DEBUGGER FOR CONCURRENT PROGRAMS IN THE GRAPE PARALLEL PROGRAMMING ENVIRONMENT SO FUTURE GENERATION COMPUTER SYSTEMS LA English DT Article NR 20 SN 0167-739X PU ELSEVIER SCIENCE BV C1 KATHOLIEKE UNIV LEUVEN,ESAT LAB,KARD MERCIERLAAN 94,B-3001 HEVERLEE,BELGIUM DE PROCESS-LEVEL DEBUGGING; RECORD-REPLAY; PROGRAM ANIMATION; DATA VISUALIZATION AB In this paper, we describe the process-level debugger of GRAPE, our hierarchical graphical programming environment for concurrent programs. Its unique feature is that it clearly separates the identification of erroneous processes, which we call process-level debugging, from the exact localisation of the bug at the source-level. This divide-and-conquer approach is absolutely necessary for debugging complex parallel programs in a fast and systematic way. Our process-level debugging approach is based on an animation of the program's behaviour on its hierarchical graphical representations. Graphical views are used that reflect the programmer's mental picture of the actual application. Hierarchy allows us to employ a top-down debugging approach in which we successively refine the search-space by zooming in on suspect processes first-time-right. During animation a debugging kernel implementing a record-replay mechanism guarantees reproducible behaviour. CR 1992, TMS320C4XC SOURCE DE ADAMS E, 1986, SOFTWARE PRACTIC JUL, P653 BAIARDI F, 1986, IEEE T SOFTWARE ENG, V12, P547 BROWN MB, 1985, IEEE SOFTWARE JAN, P28 CAERTS C, 1991, LECTURE NOTES COMPUT, P54 CHARLTON CC, 1991, ACM ONR WORKSHOP PAR, P219 CHEUNG WH, 1990, IEEE SOFTWARE, P106 ENGELS M, 1991, IEEE DESIGN TEST JUN, P52 GAIT J, 1986, SOFTWARE PRACT EXPER, V16, P225 HALSTEAD RH, 1991, ACM ONR WORKSHOP PAR, P237 ISODA S, 1991, IEEE SOFTWARE MAY, P44 JONES SH, 1988, MAY P ACM SIGPLAN SI LAMPORT L, 1978, COMM ACM JUL, P558 LEBLANC TJ, 1987, IEEE T COMPUT, V36, P471 LEWIS TG, 1992, LECTURE NOTES COMPUT, P37 MOURLIN F, 1990, APPLICATIONS TRANSPU, V1, P252 PONAMGI MK, 1991, IEEE SOFTWARE MAY, P37 ROONEY J, 1992, MAR P EWPC 92 EUR WO SOCHA D, 1988, MAY P ACM SIGPLAN SI, P206 STEPNEY S, 1987, 7TH P OCC US GROUP I TC 0 BP 199 EP 210 PG 12 JI Futur. Gener. Comp. Syst. PY 1995 PD MAR VL 11 IS 2 GA QV937 PI AMSTERDAM RP CAERTS C KATHOLIEKE UNIV LEUVEN,ESAT LAB,KARD MERCIERLAAN 94,B-3001 HEVERLEE,BELGIUM J9 FUTURE GENER COMPUT SYST PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1995QV93700012 ER PT Journal AU ANSELIN, L TI LOCAL INDICATORS OF SPATIAL ASSOCIATION - LISA SO GEOGRAPHICAL ANALYSIS LA English DT Article NR 52 SN 0016-7363 PU OHIO STATE UNIV PRESS C1 W VIRGINIA UNIV,REG RES INST,MORGANTOWN,WV 26506 ID EXPANSION METHOD; REGRESSION; STATISTICS; MODELS; AUTOCORRELATION; NORMALITY; PEACE; ANOVA; WAR AB The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the ''spatial'' aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G(i)*; statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify ''outliers,'' as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations. CR ANSELIN L, 1992, ANN REGIONAL SCI, V26, P19 ANSELIN L, 1993, DEC GISDATA SPEC M G ANSELIN L, 1990, DYNAMICS CONFLICT RE, P325 ANSELIN L, 1990, J REGIONAL SCI, V30, P185 ANSELIN L, 1986, MICROQAP MICROCOMPUT ANSELIN L, 1992, NEW GEOPOLITICS, P39 ANSELIN L, 1993, NOV DOSES EUR WORKSH ANSELIN L, 1980, REGIONAL SCI DISSERT ANSELIN L, 1992, SPACESTAT PROGRAM AN ANSELIN L, 1988, SPATIAL ECONOMETRICS AZAR EE, 1980, J CONFLICT RESOLUT, V24, P143 BELSLEY DA, 1980, REGRESSION DIAGNOSTI CASETTI E, 1972, GEOGR ANAL, V4, P81 CASETTI E, 1986, IEEE T SYST MAN CYB, V16, P29 CLIFF AD, 1981, SPATIAL PROCESSES MO COOK RD, 1977, TECHNOMETRICS, V19, P15 COSTANZO CM, 1983, GEOGR ANAL, V15, P347 CRESSIE N, 1991, STATISTICS SPATIAL D DIEHL P, 1992, NEW GEOPOLITICS, P121 EASTMAN R, 1992, IDBISI VERSION 4 0 FOSTER SA, 1986, MANAGE SCI, V32, P878 GETIS A, 1991, ENVIRON PLANN A, V23, P1269 GETIS A, 1992, GEOGR ANAL, V24, P189 GORR WL, 1994, GEOGR ANAL, V26, P67 GRIFFITH DA, 1978, GEOGR ANAL, V10, P296 GRIFFITH DA, 1993, GEOGRAPHIC INFORMATI, P101 GRIFFITH DA, 1992, REG SCI URBAN ECON, V22, P347 HASLETT J, 1991, AM STAT, V45, P234 HOAGLIN DC, 1978, AM STAT, V32, P17 HUBERT LJ, 1987, ASSIGNMENT METHODS C HUBERT LJ, 1985, GEOGR ANAL, V17, P36 HUBERT LJ, 1981, GEOGR ANAL, V13, P224 HUBERT LJ, 1985, PSYCHOMETRIKA, V50, P449 JONES JP, 1992, APPLICATIONS EXPANSI KIEFER NM, 1983, ECON LETT, V11, P123 KIRBY AM, 1987, COMP POLIT STUD, V20, P293 MANTEL N, 1967, CANCER RES, V27, P209 MIELKE PW, 1979, COMMUN STAT THEORY, V8, P1541 ODEN NL, 1984, GEOGR ANAL, V16, P1 OLOUGHLIN J, 1994, ANN ASSOC AM GEOGR, V84, P351 OLOUGHLIN J, 1986, ANN ASSOC AM GEOGR, V76, P63 OLOUGHLIN J, 1991, INT INTERACT, V17, P29 OLOUGHLIN J, 1992, NEW GEOPOLITICS, P11 OPENSHAW S, 1991, EGIS 91, P788 OPENSHAW S, 1993, GEOGRAPHIC INFORMATI, P17 OPENSHAW S, 1990, INT J GEOGR INF SYST, V4, P297 ORD JK, 1994, DISTRIBUTIONAL ISSUE ROYALTEY H, 1975, GEOGR ANAL, V7, P369 SAVIN NE, 1980, REV ECON STUD, V67, P255 SIDAK Z, 1967, J AM STAT ASSOC, V62, P626 SOKAL RR, 1993, GEOGR ANAL, V25, P199 TIEFELSDORF M, 1994, IN PRESS ENV PLANN A TC 93 BP 93 EP 115 PG 23 JI Geogr. Anal. PY 1995 PD APR VL 27 IS 2 GA QU198 PI COLUMBUS RP ANSELIN L W VIRGINIA UNIV,REG RES INST,MORGANTOWN,WV 26506 J9 GEOGR ANAL PA 1050 CARMACK RD, COLUMBUS, OH 43210 UT ISI:A1995QU19800001 ER PT Journal AU MELOUN, M MILITKY, J TI COMPUTER-ASSISTED DATA TREATMENT IN ANALYTICAL CHEMOMETRICS .1. EXPLORATORY ANALYSIS OF UNIVARIATE DATA SO CHEMICAL PAPERS-CHEMICKE ZVESTI LA English DT Article NR 11 SN 0366-6352 PU SLOVAK ACADEMIC PRESS LTD C1 UNIV PARDUBICE,FAC CHEM TECHNOL,DEPT ANALYT CHEM,CR-53210 PARDUBICE,CZECH REPUBLIC CZECH TECH UNIV,DEPT TEXT MAT,CR-46117 LIBEREC,CZECH REPUBLIC AB The first step of univariate data analysis called an exploratory data analysis (EDA) isolates certain basic statistical features and patterns of data. EDA is based on the general assumptions as a continuity and differentiability of underlying density. For visualization of data the quantile plot, the dot and jittered dot diagrams, and the box-and- whisker plot are proposed. Peculiarities of sample distribution are investigated by the midsum plot, the symmetry plot, the curtosis plot, and the quantile-box plot. Construction of sample probability density function is carried out by the kernel estimation and the histogram. The quantile-quantile plot serves for comparison of sample distribution with selected theoretical ones. EDA is illustrated on a quantitative chemical analysis of phosphorus content in blood. CR CHAMBERS JM, 1983, GRAPHICAL METHODS DA HOAGLIN DC, 1985, EXPLORING DATA TABLE HOAGLIN DC, 1983, UNDERSTANDING ROBUST KAFADAR K, 1986, COMPUT STAT DATA AN, V4, P167 LEJENNE M, P C COMSTAT 82 TOULO, V3, P173 MELOUN M, 1991, CHEMOMETRICS ANAL CH, V1 MELOUN M, 1994, PC AIDED STATISTICAL, V2 PARZEN E, 1985, J AM STAT ASSOC, V74, P105 SCOTT DW, 1985, COMMUN STAT THEORY, V14, P1353 STOODLEY K, 1984, APPLIED COMPUTATIONA TUKEY JW, 1977, EXPLORATORY DATA ANA TC 1 BP 151 EP 157 PG 7 JI Chem. Pap.-Chem. Zvesti PY 1994 VL 48 IS 3 GA QF844 PI BRATISLAVA RP MELOUN M UNIV PARDUBICE,FAC CHEM TECHNOL,DEPT ANALYT CHEM,CR-53210 PARDUBICE,CZECH REPUBLIC J9 CHEM PAP-CHEM ZVESTI PA PO BOX 57, NAM SLOBODY 6, 810 05 BRATISLAVA, SLOVAKIA UT ISI:A1994QF84400004 ER PT Journal AU FACKLER, J KOHANE, I TI MONITOR-DRIVEN DATA VISUALIZATION - SMARTDISPLAY SO JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION LA English DT Article NR 11 SN 1067-5027 PU HANLEY & BELFUS INC C1 CHILDRENS HOSP MED CTR,BOSTON,MA 02115 HARVARD UNIV,SCH MED,BOSTON,MA 02115 ID DECISION-MAKING AB Exhaustive display of all available clinical data, particular in data-rich environments like the intensive care unit, can easily overwhelm the ability of clinicians to comprehend the clinical status and evolution of their patients and may reduce their ability to detect pathological trends in a reliable and timely manner. SmartDisplay is a system we have designed that restricts the data sets displayed to time-lines of those parameters that are relevant to the patient context and to the particular care provider. The relevance criteria are provided by monitoring programs which may range in complexity from simple threshold alarms to full-fledged diagnostic engines. SmartDisplay can specify which parameters to display and the time intervals during which they should be displayed. CR COLE WG, 1993, INT J CLIN MONIT COM, V10, P91 COUSINS SB, 1991, ARTIF INTELL MED, V3, P341 FACKLER J, 1994, SPR AAAI S INT CLIN, P37 FACKLER JC, 1993, 6TH ANN IEEE S COMP, P289 HAIMOWITZ IJ, 1993, NATIONAL C ARTIFICIA, P176 KOHANE IS, 1986, MEDINFO 86, P170 RUSS TA, 1990, COMPUT METH PROG BIO, V32, P81 SHAHAR Y, 1992, KNOWL ACQUIS, V1, P217 STEIMANN F, 1994, FUZZY SET SYST, V61, P37 TUFTE ER, 1990, ENVISIONING INFORMAT WENKEBACH U, 1992, 16TH P ANN S COMP AP, P18 TC 0 BP 939 EP 943 PG 5 JI J. Am. Med. Inf. Assoc. PY 1994 SU S GA QF216 PI PHILADELPHIA RP CHILDRENS HOSP MED CTR,BOSTON,MA 02115 J9 J AMER MED INFORM ASSOC PA 210 S 13TH ST, PHILADELPHIA, PA 19107 UT ISI:A1994QF21600165 ER PT Journal AU GOBEL, M KLIMENKO, SV TI SCIENTIFIC VISUALIZATION IN A VIRTUAL ENVIRONMENT SO PROGRAMMING AND COMPUTER SOFTWARE LA English DT Article NR 32 SN 0361-7688 PU MAIK NAUKA/INTERPERIODICA C1 FRAUNHOFER INST COMP GRAPH,FREIBURG,GERMANY PROTVINO HIGH ENERGY PHYS INST,PROTVINO,RUSSIA AB Scientific visualization is concerned with large amounts of numbers that are often difficult to interpret without preliminary research and active man-machine interaction. Virtual environments present a powerful new approach to man- machine interfaces. It is to be expected that new interaction techniques will lead to applications of virtually environments that have not even been thought of yet. The prospects for this new technology and its advantages for scientific visualization have been discussed at a number of conferences and seminars; this has led to interesting experiments on application of virtual environments to scientific visualization. This paper considers the strengths and weaknesses of virtual environments for scientific visualization. A number of examples of scientific visualization with virtual environments is given. They include a virtual windtunnel, a system for visualizing finite elements and bulk data, visualization of meteorological data for professional use, training for arthroscopic examination, and several others. The authors hope that the present paper will stimulate research on synergy between these two techniques. CR ASTHEIMER P, 1991, 2ND EUR WORKSH VIS S ASTHEIMER P, 1992, 3RD EUR WORKSH VIS S, P29 ASTHEIMER P, 1992, COMPUT IND, V19, P213 ASTHEIMER P, 1993, IEEE S VIRUTAL REALI ASTHEIMER P, 1993, P ESS 93 EUROPEAN SI BISHOP G, 1992, COMPUT GRAPHICS, V26, P153 BRYSON S, 1993, COMPUT GRAPH, V17, P679 BRYSON S, 1992, IEEE COMPUT GRAPH, V12, P25 BRYSON S, 1992, P IEEE SUPERCOMPUTIN BRYSON S, 1991, P IEEE VISUALIZATION BRYSON S, 1992, P VIS 92 BOSTON, P291 CRUZNEIRA C, P SIGGRAPH 9I CHICAG, P203 DELPINO A, 1993, SER VISUALIZATION CO ENCARNACAO JL, 1994, IEEE COMPUTER GRAPHI, V14 ENCARNACAO JL, 1993, P ICCG 9O BOMBAY FELGER W, 1992, P EUROGRAPHICS 92 CO FELGER W, 1992, SPIE IS T S ELECTRON FRUHAUF M, 1993, SCI VISUALIZATION AD, P101 FRUHAUF T, 1994, 5TH EUR WORKSH VIS S FRUHAUF T, 1994, FRONTIERS SCI VISUAL GOBEL M, 1993, DOMPUT GRAPHICS, V17, P627 GOBEL M, 1993, EUROGRAPHICS WORKSHO GOBEL M, 1992, MULTIMEDIA IMAGEPROZ GOBEL M, 1993, VIRTUAL REALITY COMP, V17 LOPES JMB, PROTOTYPE ONLINE VIS MCCORMICK H, 1987, ACM COMPUTER GRAPHIC, V21 SAKAS G, 1993, COMPUT GRAPH FORUM, V12, P329 SCHRODER F, 1994, COMPUTERS GRAPHI NOV SCHRODER F, 1993, IEEE COMPUTER GR SEP SHERMAN WR, 1993, 4TH EUR WORKSH VIS S SUTHERLAND IE, P IFIP 65, V2, P506 VARNER D, 1993, P ICAT VET HOUSTON TC 0 BP 157 EP 168 PG 12 JI Program. Comput. Softw. PY 1994 PD JUL-AUG VL 20 IS 4 GA QB332 PI NEW YORK RP FRAUNHOFER INST COMP GRAPH,FREIBURG,GERMANY J9 PROGRAM COMPUT SOFT-ENGL TR PA C/O PLENUM/CONSULTANTS BUREAU 233 SPRING ST, NEW YORK, NY 10013 UT ISI:A1994QB33200003 ER PT Journal AU KOCHIN, VN KULIKOV, VA TI SOFTWARE TOOLS FOR DATA VISUALIZATION WITH PERSONAL COMPUTERS SO PROGRAMMING AND COMPUTER SOFTWARE LA English DT Article NR 10 SN 0361-7688 PU MAIK NAUKA/INTERPERIODICA C1 PROTVINO HIGH ENERGY PHYS INST,PROTVINO,RUSSIA AB This article presents a group of software tools for visualizing multidimensional data with IBM compatible PC's. Three layers of software are considered: basic, instrumental, and interactive. CR BAZHANOVA LS, 1987, 4TH ALL UN C PROBL C, P20 KLIMENKO SV, 1987, ATOM 86 PACKAGE KLIMENKO SV, 1985, PIPELINE MODEL SOFTW KOCHIN VN, 1993, 3RD P INT C COMP GRA KOCHIN VN, 1987, ISO DIS8805 KOCHIN VN, 1988, ISO DP9636 KOCHIN VN, 1985, ISO IS7942 KOCHIN VN, 1987, ISO IS8632 KOCHIN VN, 1987, MICROSOFT WINDOWS SO KOCHIN VN, ZSOFT TECHNICAL REFE TC 0 BP 177 EP 180 PG 4 JI Program. Comput. Softw. PY 1994 PD JUL-AUG VL 20 IS 4 GA QB332 PI NEW YORK RP PROTVINO HIGH ENERGY PHYS INST,PROTVINO,RUSSIA J9 PROGRAM COMPUT SOFT-ENGL TR PA C/O PLENUM/CONSULTANTS BUREAU 233 SPRING ST, NEW YORK, NY 10013 UT ISI:A1994QB33200005 ER PT Journal AU KNIGHT, S CHIN, D TAYLOR, H PETERS, J TI THE SARNOFF ENGINE - A MASSIVELY-PARALLEL COMPUTER FOR HIGH- DEFINITION SYSTEM SIMULATION SO JOURNAL OF VLSI SIGNAL PROCESSING LA English DT Article NR 7 SN 0922-5773 PU KLUWER ACADEMIC PUBL C1 DAVID SARNOFF RES CTR,CN 5300,PRINCETON,NJ 08543 AB This paper describes the Sarnoff Engine, a 1.6 TeraFLOP, real- time, high definition, video and image processing computer. It is a second generation, scalable, linear array, multiuser, MIMD architecture focused on the applications of real-time high definition video and image processing (data compression encoders and decoders), 3D/4D data visualization, and neural network development. CR BINENBAUM N, 1991, IEEE SEP BRUNS R, UTILIZING 3 DIMENSIO CHIN D, 1988, IEEE T CONSUMER ELEC, V34 FREW D, HIGH DENSITY MEMORY ISNARDI M, 1988, IEEE T CONSUMER ELEC, V43 KABA J, 1992, OCT VIS 92 P SCHRODER P, 1992, OCT VIS 92 P TC 0 BP 183 EP 199 PG 17 JI J. VLSI Signal Process. PY 1994 PD OCT VL 8 IS 2 GA PZ676 PI DORDRECHT RP KNIGHT S DAVID SARNOFF RES CTR,CN 5300,PRINCETON,NJ 08543 J9 J VLSI SIGNAL PROCESS PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS UT ISI:A1994PZ67600007 ER PT Journal AU FRITZKE, B TI GROWING CELL STRUCTURES - A SELF-ORGANIZING NETWORK FOR UNSUPERVISED AND SUPERVISED LEARNING SO NEURAL NETWORKS LA English DT Article NR 32 SN 0893-6080 PU PERGAMON-ELSEVIER SCIENCE LTD C1 RUHR UNIV BOCHUM,INST NEUROINFORMAT,D-44780 BOCHUM,GERMANY DE SELF-ORGANIZATION; INCREMENTAL LEARNING; RADIAL BASIS FUNCTION; CLUSTERING; DATA VISUALIZATION; PATTERN CLASSIFICATION; 2- SPIRAL PROBLEM; FEATURE MAP ID MAPS AB We present a new self-organizing neural network model that has two variants. The first variant performs unsupervised learning and can be used for data visualization, clustering and vector quantization. The main advantage over existing approaches (e.g., the Kohonen feature map) is the ability of the model to automatically find a suitable network structure and size. This is achieved through a controlled growth process that also includes occasional removal of units. The second variant of the model is a supervised learning method that results from the combination of the above-mentioned self-organizing network with the radial basis function (RBF) approach. In this model it is possible-in contrast to earlier approaches-to perform the positioning of the RBF units and the supervised training of the weights in parallel. Therefore, the current classification error can be used to determine where to insert new RBF units. This leads to small networks that generalize very well. Results on the two-spirals benchmark and a vowel classification problem are presented that are better than any results previously published. CR BAUM EB, 1991, ADV NEURAL INFORMATI, V3, P904 BLACKMORE J, 1992, TR AI92192 U TEX BONNLANDER BV, 1993, ADV NEURAL INFORMATI, V5, P131 DETERDING DH, 1989, THESIS U CAMBRIDGE FAHLMAN SE, 1990, ADV NEURAL INFORMATI, V2, P524 FAHLMAN SE, 1993, CMU BENCHMARK COLLEC FAVATA F, 1991, BIOL CYBERN, V64, P463 FRITZKE B, 1993, ADV NEURAL INFORMATI, V5, P123 FRITZKE B, 1993, ICANN 93 INT C ART N, P580 HAKALA J, 1993, ICANN 93, P309 JOKUSCH S, 1990, PARALLEL PROCESSING, P169 KANGAS JA, 1990, IEEE T NEURAL NETWOR, V1, P93 KOHONEN T, 1984, 7TH P ICPR MONTR, P182 KOHONEN T, 1982, BIOL CYBERN, V43, P59 KOHONEN T, 1988, IEEE COMPUT, V21, P11 LANG KJ, 1989, 1988 P CONN MOD SUMM, P52 MARTINETZ T, 1991, ARTIFICIAL NEURAL NE, P397 MARTINETZ TM, 1993, INT C ART NEUR NETW, P427 MEHLHORN K, 1989, TR A0489 U SAARL FAC MOODY J, 1989, 1988 P CONN MOD SUMM, P133 PLATT J, 1991, NEURAL COMPUT, V3, P213 POGGIO T, 1990, SCIENCE, V247, P978 RABINER LR, 1990, DIGITAL PROCESSING S RITTER H, 1991, ARTIFICIAL NEURAL NE, P379 RITTER H, 1989, BIOL CYBERN, V61, P241 ROBINSON AJ, 1993, COMMUNICATION ROBINSON AJ, 1989, THESIS CAMBRIDGE U RODRIGUES JS, 1990, P INNC 90 INT NEUR N, P813 ROSENBLATT F, 1958, PSYCHOL REV, V65, P386 SCHWEIZER L, 1991, ARTIFICIAL NEURAL NE, P815 WILLSHAW DJ, 1976, P ROY SOC LOND B BIO, V194, P431 XU L, 1990, INT J NEURAL SYSTEMS, V1, P269 TC 88 BP 1441 EP 1460 PG 20 JI Neural Netw. PY 1994 VL 7 IS 9 GA PV678 PI OXFORD RP FRITZKE B RUHR UNIV BOCHUM,INST NEUROINFORMAT,D-44780 BOCHUM,GERMANY J9 NEURAL NETWORKS PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1994PV67800008 ER PT Journal AU FRONCIONI, AM PESKIN, RL TI QUALITATIVE FLOW DESCRIPTORS FOR UNSTRUCTURED TRIANGULAR GRIDS SO MATHEMATICS AND COMPUTERS IN SIMULATION LA English DT Article NR 12 SN 0378-4754 PU ELSEVIER SCIENCE BV C1 RUTGERS STATE UNIV,DEPT MECH & AEROSP ENGN,PISCATAWAY,NJ 08854 AB Flow visualization techniques are rapidly evolving to keep pace with improved flow solution methods. Critical point flow analysis has emerged as an important visualization and data compression method. In addition, flow topology can be described qualitatively using such methods. We propose a merger of this type of analysis with unstructured triangular- grid solution techniques. A software tool is developed to implement these methods and results are presented using a time- dependent wake flow problem. The role of symbolic computation in developing and extending such a system is discussed. CR BARTH TJ, 1991, 29TH AIAA P AER SCI DICKINSON RR, 1991, IBM J RES DEV, V35 HELMAN J, 1989, SPIE P, V1083, P23 HUGHES JR, 1987, FINITE ELEMENT METHO LAVIN I, 1993, APR I ADV RES PRINC LIGHTHILL MJ, 1963, LAMINAR BOUNDARY LAY, P48 PERRY AE, 1974, ADV GEOPHYS B, V18, P299 PERRY AE, 1982, J FLUID MECH, V116, P77 PEYRET R, 1983, SERIES COMPUTATIONAL ROACHE PJ, 1982, COMPUTATIONAL FLUID THOMPSON JMT, 1986, NONLINEAR DYNAMICS C TOBAK M, 1979, 12TH P AIAA FLUID PL TC 0 BP 467 EP 477 PG 11 JI Math. Comput. Simul. PY 1994 PD OCT VL 36 IS 4-6 GA PR898 PI AMSTERDAM RP FRONCIONI AM RUTGERS STATE UNIV,DEPT MECH & AEROSP ENGN,PISCATAWAY,NJ 08854 J9 MATH COMPUT SIMULAT PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1994PR89800019 ER PT Journal AU RIGHTER, R FORD, R TI AN OBJECT-ORIENTED CHARACTERIZATION OF SPATIAL ECOSYSTEM INFORMATION SO MATHEMATICAL AND COMPUTER MODELLING LA English DT Article NR 11 SN 0895-7177 PU PERGAMON-ELSEVIER SCIENCE LTD C1 UNIV MONTANA,DEPT COMP SCI,MISSOULA,MT 59812 DE OBJECT-ORIENTED MODELING; ECOSYSTEM INFORMATION SYSTEM; DISTRIBUTED INFORMATION SYSTEM; SPATIAL MODELING; ECOSYSTEM MODELING AB An information system that supports the work of ecosystem modelers should be easy for the modelers to use, should organize both datasets and computational processes in a convenient hierarchical fashion, should be initially populated with information of interest, and should be easily extensible through contributions from its users. We are currently using object-oriented techniques to implement a network-based ecosystem information system (EIS) to meet these objectives. The initial EIS implementation includes a database consisting of key ecosystem datasets and processes ''cast'' in the object- oriented framework, i.e., as a collection of classes, instances, and methods. In the long term, we rely on EIS users to extend this database by contributing their work to extend the database. Therefore, while a robust and user-friendly implementation of the EIS software is important, the design of the initial database is even more crucial to long term project success. That is, the initial database must present information of sufficient interest to attract the new user, it must illustrate the intuitive concepts and value of the object- oriented paradigm to the new user, and it must serve as a model to interest users in ''re-casting'' their own work into this paradigm for contribution to the database. The initial EIS database will include datasets and modeling tools from three important ecosystem applications: climate and regional/global scale carbon-exchange modeling, U.S. Fish and Wildlife's biodiversity analysis (a.k.a. ''GAP''), and general data visualization techniques for ecosystem modeling. After a brief overview of EIS functionality, the presentation here focuses on the design of this initial collection of ecosystem related classes, instances, and methods. CR BENNETT DA, 1993, 2ND P INT C GEOGR IN BOOCH G, 1992, OBJECT ORIENTED DESI FORD R, 1992, 6TH ANN INEL COMP S FORD R, 1994, SEP P DEC SUPP 2001 GOODCHILD MF, 1992, COMPUTAT GEOSCI, V18, P401 KLANIKA K, 1993, 2ND P INT C GEOGR IN RAPER J, 1993, 2ND P INT C GEOGR IN RIGHTER R, 1993, THESIS U MONTANA RUMBAUGH J, 1991, OBJECT ORIENTED MODE SEQUIERA RA, 1991, OBJECT ORIENTED SIMU SILVERT W, 1993, OBJECT ORIENTED ECOS TC 1 BP 17 EP 29 PG 13 JI Math. Comput. Model. PY 1994 PD OCT VL 20 IS 8 GA PT136 PI OXFORD RP RIGHTER R UNIV MONTANA,DEPT COMP SCI,MISSOULA,MT 59812 J9 MATH COMPUT MODELLING PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1994PT13600003 ER PT Book in series AU LAND, BR TI TEACHING COMPUTER-GRAPHICS AND SCIENTIFIC VISUALIZATION USING THE DATA-FLOW, BLOCK DIAGRAM LANGUAGE DATA EXPLORER SO UNIVERSITY EDUCATION USES OF VISUALIZATION IN SCIENTIFIC COMPUTING LA English DT Article NR 1 SN 0926-5473 PU ELSEVIER SCIENCE PUBL B V C1 CORNELL UNIV,CORNELL THEORY CTR,606 E&TC BLDG,ITHACA,NY 14853 DE VISUAL PROGRAMMING; COMPUTER GRAPHICS TECHNIQUES; COMPUTERS IN EDUCATION AB The scientific visualization language IBM Visualization Data Explorer (DX) was found to be useful in undergraduate education as a vehicle for teaching computer graphics at an introductory level. Although designed for scientific data visualization, DX can be used to construct student lab exercises in computer graphics. DX has been used for two years as an environment which emphasizes graphics manipulations (e.g. rotation, perspective) while not requiring the programming overhead of traditional computer languages. CR FOLEY JD, 1990, COMPUTERS GRAPHICS P TC 0 BP 33 EP 36 PG 4 SE IFIP TRANSACTIONS A-COMPUTER SCIENCE AND TECHNOLOGY PY 1994 VL 48 GA BB13E PI AMSTERDAM RP LAND BR CORNELL UNIV,CORNELL THEORY CTR,606 E&TC BLDG,ITHACA,NY 14853 J9 IFIP TRANS A PA SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1994BB13E00003 ER PT Journal AU CIGNONI, P MONTANI, C SCOPIGNO, R TI MAGICSPHERE - AN INSIGHT TOOL FOR 3D DATA VISUALIZATION SO COMPUTER GRAPHICS FORUM LA English DT Article NR 18 SN 0167-7055 PU BLACKWELL PUBL LTD C1 UNIV PISA,DIPARTIMENTO SCI INFORMAZ,C SO ITALIA 40,I-56100 PISA,ITALY CNR,IEI,I-56126 PISA,ITALY CNR,CNUCE,I-56126 PISA,ITALY AB How to render very complex datasets, and yet maintain interactive response times, is a hot topic in computer graphics. The MagicSphere idea originated as a solution to this problem, but its potential goes much further than this original scope. In fact, it has been designed as a very generical 3D widget: it defines a spherical volume of interest in the dataset modeling space. Then, several filters can be associated with the MagicSphere, which apply different visualization modalities to the data contained in the volume of interest. The visualization of multi-resolution datasets is selected here as a case study and an ad hoc filter has been designed, the MultiRes filter. Some results of a prototipal implementation are presented and discussed. CR AKMAN V, 1989, COMPUT AIDED DESIGN, V21, P410 BIER E, 1993, ACM COMP GRAPH P ACM, P73 CIGNONI P, 1991, 9401 I CNUCE CNR TEC CONNER DB, 1992, 1992 P S INT 3D GRAP, P183 DEFLORIANI L, 1989, IEEE COMPUT GRAP MAR, P67 DEFLORIANI L, 1992, LECT NOTES COMPUT SC, V639, P236 DEHAEMER MJ, 1991, COMPUT GRAPHICS, V15, P175 EDELSBRUNNER H, 1992, 1992 WORKSH VOL VIS, P75 FUNKHOUSER TA, 1993, ANN C SERIES, P247 HOPPE H, 1993, ACM COMPUTER GRAPHIC, P19 KALVIN AD, 1991, SPIE, V1445, P247 LORENSEN WE, 1987, COMPUT GRAPHICS, V21, P163 NEIDER J, 1993, OPEN GL PROGRAMMING PORTER T, 1984, COMPUT GRAPHICS, V18, P253 SARKAR M, 1992, 1992 P CHI 92, P83 SCHROEDER WJ, 1992, COMPUT GRAPHICS, V26, P65 TURK G, 1992, ACM COMPUTER GRAPHIC, V26, P55 ZELEZNIK R, 1993, ACM COMPUTER GRAPHIC, P81 TC 0 BP C317 EP & PG 0 JI Comput. Graph. Forum PY 1994 PD SEP 12 VL 13 IS 3 SI CI GA PH906 PI OXFORD RP CIGNONI P UNIV PISA,DIPARTIMENTO SCI INFORMAZ,C SO ITALIA 40,I-56100 PISA,ITALY J9 COMPUTER GRAPHICS FORUM PA 108 COWLEY RD, OXFORD, OXON, ENGLAND OX4 1JF UT ISI:A1994PH90600029 ER PT Journal AU RIBARSKY, W AYERS, E EBLE, J MUKHERJEA, S TI GLYPHMAKER - CREATING CUSTOMIZED VISUALIZATIONS OF COMPLEX DATA SO COMPUTER LA English DT Article NR 12 SN 0018-9162 PU IEEE COMPUTER SOC C1 GEORGIA INST TECHNOL,OIT,SCI VISUALISAT LAB,RM 229 HINMAN,ATLANTA,GA 30332 GEORGIA INST TECHNOL,CTR GRAPH VISUALISAT & USABIL,ATLANTA,GA 30332 AB Glyphmaker's general approach to data visualization/analysis lets users build customized representations of multivariate data and gives them the interactive tools to explore data patterns and relations. Using glyphs - graphical objects - bound to data, users with little programming knowledge can set up visualizations of complex data, refine them, add to the variables, and focus on regions of interest. Glyphmaker is built in a standard dataflow environment, Silicon Graphics' Iris Explorer, so that it can take advantage of existing features and flexibility. The main parts it adds are the Data Reader, the 3D Glyph Editor, the Glyph Binder, and the Conditional Box. To test its capabilities and effectiveness, Glyphmaker has been applied to results from a typical large- scale computer simulation of a materials system. It proved to be a general and quite effective approach. for visualizing and analyzing multivariate 3D data, especially data that evolves in time. The authors are now planning a series of tests and evaluations by scientists and engineers using real data. CR BUJA A, 1991, P VISUALIZATION 91, P156 CHENG HP, 1993, SCIENCE, V260, P1304 ELLSON R, 1988, SIMULATION, V51, P184 FOLEY JD, 1986, IEEE COMPUT GRAPH, V6, P16 FOLEY JD, 1990, INT J SUPERCOMPUT AP, V4, P154 HABER R, 1990, COMPUTING SYSTEMS EN, V1, P37 LEFKOWITZ H, 1991, P VISUALIZATION 91, P164 PICKETT RM, 1988, 1988 P IEEE C SYST M, P164 RIBARSKY W, 1994, FRONTIERS VISUALIZAT RIBARSKY W, 1993, GITGVU9326 GEORG I T TREINISH LA, 1993, BYTE, V18, P132 UPSON C, 1989, IEEE COMPUT GRAPH, V9, P30 TC 10 BP 57 EP 64 PG 8 JI Computer PY 1994 PD JUL VL 27 IS 7 GA NW997 PI LOS ALAMITOS RP RIBARSKY W GEORGIA INST TECHNOL,OIT,SCI VISUALISAT LAB,RM 229 HINMAN,ATLANTA,GA 30332 J9 COMPUTER PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1264 UT ISI:A1994NW99700016 ER PT Journal AU DZWINEL, W TI HOW TO MAKE SAMMON MAPPING USEFUL FOR MULTIDIMENSIONAL DATA- STRUCTURES ANALYSIS SO PATTERN RECOGNITION LA English DT Article NR 0 SN 0031-3203 PU PERGAMON-ELSEVIER SCIENCE LTD C1 AGH,INST COMP SCI,AL MICKIEWICZA 30,PL-30059 KRAKOW,POLAND DE MULTIDIMENSIONAL DATA VISUALIZATION; SAMMONS CRITERION; MAPPING OF PATTERNS; ARTIFACTS; SIMULATED ANNEALING; CLUSTERING; NUCLEAR REACTOR DIAGNOSTICS AB The ways of optimization of Sammon's mapping technique are suggested. Two sorts of the mapping artifacts produced by the local minimum traps and non-coherence of the source and target spaces (i.e. multi- and low-dimensional ones, respectively) are discussed. The methods of reduction of the artifacts' influence on the resulting two- and three-dimensional patterns are proposed. The nuclear reactor diagnostics system is taken as the source of real multidimensional data. Usefulness of the mapping for their analysis is shown. TC 5 BP 949 EP 959 PG 11 JI Pattern Recognit. PY 1994 PD JUL VL 27 IS 7 GA NU907 PI OXFORD RP DZWINEL W AGH,INST COMP SCI,AL MICKIEWICZA 30,PL-30059 KRAKOW,POLAND J9 PATT RECOG PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1994NU90700009 ER PT Journal AU SADOWSKY, J MASSOF, RW TI SENSORY ENGINEERING - THE SCIENCE OF SYNTHETIC ENVIRONMENTS SO JOHNS HOPKINS APL TECHNICAL DIGEST LA English DT Article NR 0 SN 0270-5214 PU JOHNS HOPKINS UNIV C1 JOHNS HOPKINS UNIV,APPL PHYS LAB,RES CTR,MATH & INFORMAT SCI GRP,LAUREL,MD 20723 AB Over the past several months, The Johns Hopkins University Applied Physics Laboratory and the Schools of Medicine, Engineering, and Arts and Sciences have been developing an Interdivisional Sensory Engineering Program. Sensory engineering, an exciting and emerging discipline, incorporates such technologies as virtual environments and virtual reality, data visualization, human sensory system modeling, human- machine interface, and perception, cognition, and performance characterization. In this article, we define sensory engineering and its diverse applied fields: virtual reality, robotic telepresence, teleoperations, visualization, environmental overlays, and sensory enhancement. These fields are illustrated with current projects at the Laboratory and in the Schools of Medicine, Engineering, and Arts and Sciences. The Interdivisional Sensory Engineering Program is introduced, and plans for developing and implementing the program are presented. TC 7 BP 99 EP 109 PG 11 JI Johns Hopkins APL Tech. Dig. PY 1994 PD APR-JUN VL 15 IS 2 GA NR651 PI LAUREL RP SADOWSKY J JOHNS HOPKINS UNIV,APPL PHYS LAB,RES CTR,MATH & INFORMAT SCI GRP,LAUREL,MD 20723 J9 JOHNS HOPKINS APL TECH DIG PA APPLIED PHYSICS LABORATORY ATTN: MANAGING EDITOR JOHN HOPKINS RD, BLDG 1-E254, LAUREL, MD 20723-6099 UT ISI:A1994NR65100002 ER PT Journal AU LU, XB STETTER, F TI SPECIFICATION SCHEME FOR THE VISUALIZATION OF DATA-STRUCTURES SO SOFTWARE ENGINEERING JOURNAL LA English DT Article NR 12 SN 0268-6961 PU IEE-INST ELEC ENG C1 UNIV MANNHEIM,LEHRSTUHL PRAKT INFORMAT 1,D-68131 MANNHEIM,GERMANY AB The paper describes a general principle for program visualisation, based on the concept of structure models. We propose a specification scheme that can be used to implement the general principle without requiring a change of the program code. Two aspects of the scheme are discussed in detail; the specification of critical entities and their graphical representations. Its use is illustrated by means of a specification for the visualisation of a program which calculates the convex hull of a finite set of points. CR AMBLER A, 1989, IEEE COMPUT, V22, P9 BROWN MH, 1988, ALGORITHM ANIMATION CHANG SK, 1990, PRINCIPLES VISUAL PR, P1 HELTTULA E, 1989, P 2 INT C SYST SCI, V2, P892 HSIA YT, 1987, P INT C COMPUTER LAN, P10 ICHIKAWA T, 1990, IEEE T, V16 LU XB, 1992, THESIS U MANNHEIM GE MOHER TG, 1988, IEEE T SOFTWARE ENG, V14, P849 MYERS BA, 1986, APR P ACM SIGCHI 86, P59 MYERS BA, 1984, COMPUT GRAPH, V17, P115 PREPARATA FP, 1985, COMPUTATIONAL GEOMET SHU NC, 1988, VISUAL PROGRAMMING TC 0 BP 127 EP 133 PG 7 JI Softw. Eng. J. PY 1994 PD MAY VL 9 IS 3 GA NR832 PI HERTFORD RP UNIV MANNHEIM,LEHRSTUHL PRAKT INFORMAT 1,D-68131 MANNHEIM,GERMANY J9 SOFTWARE ENG J PA MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD, ENGLAND SG1 2AY UT ISI:A1994NR83200004 ER PT Journal AU MARCHAK, FM TI AN OVERVIEW OF SCIENTIFIC VISUALIZATION TECHNIQUES APPLIED TO EXPERIMENTAL-PSYCHOLOGY SO BEHAVIOR RESEARCH METHODS INSTRUMENTS & COMPUTERS LA English DT Article NR 9 SN 0743-3808 PU PSYCHONOMIC SOC INC C1 TASC,55 WALKERS BROOK DR,READING,MA 01867 ID MULTIVARIATE DATA; EXPLORATORY ANALYSIS; DYNAMIC GRAPHICS AB This paper presents an overview of current data visualization techniques. The various types of graphics, such as contour plots, surface plots, scatterplot matrices, and dynamic spinning, are described. Then, using a data set typical of the field, the potential merits and pitfalls of each visualization technique are presented in terms of what aspects of the data they make explicit. The results illustrate the benefits that a psychological researcher can expect from data visualization capabilities. CR BECKER RA, 1991, PIXEL, V72, P36 BUTLER DL, 1993, BEHAV RES METH INSTR, V25, P81 CASTELLAN NJ, 1991, BEHAV RES METH INSTR, V23, P106 CLEVELAND WS, 1993, J COMPUTATIONAL GRAP, V2, P323 LOFTUS GR, 1993, BEHAV RES METH INSTR, V25, P250 MARCHAK FM, 1992, BEHAV RES METH INSTR, V24, P253 MARCHAK FM, 1991, BEHAV RES METH INSTR, V23, P296 MARCHAK FM, 1990, BEHAV RES METH INSTR, V22, P176 ROTHKOPF EZ, 1957, J EXP PSYCHOL, V53, P94 TC 2 BP 177 EP 180 PG 4 JI Behav. Res. Methods Instr. Comput. PY 1994 PD MAY VL 26 IS 2 GA NK980 PI AUSTIN RP MARCHAK FM TASC,55 WALKERS BROOK DR,READING,MA 01867 J9 BEHAV RES METHOD INSTRUM COMP PA 1710 FORTVIEW RD, AUSTIN, TX 78704 UT ISI:A1994NK98000017 ER PT Journal AU KAPLAN, G TI ENGINEERING SOFTWARE - MATH, VISUALIZATION, AND DATA- ACQUISITION SO IEEE SPECTRUM LA English DT Editorial Material NR 0 SN 0018-9235 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC TC 0 BP 40 EP 40 PG 1 JI IEEE Spectr. PY 1993 PD NOV VL 30 IS 11 GA NH246 PI NEW YORK J9 IEEE SPECTRUM PA 345 E 47TH ST, NEW YORK, NY 10017-2394 UT ISI:A1993NH24600011 ER PT Journal AU CARLSON, RE NATARAJAN, BK TI SPARSE APPROXIMATE MULTIQUADRIC INTERPOLATION SO COMPUTERS & MATHEMATICS WITH APPLICATIONS LA English DT Article NR 14 SN 0898-1221 PU PERGAMON-ELSEVIER SCIENCE LTD C1 HEWLETT PACKARD CORP,1501 PAGE MILL RD,3U,PALO ALTO,CA 94304 LAWRENCE LIVERMORE NATL LAB,LIVERMORE,CA 94550 AB Multiquadric interpolation is a technique for interpolating nonuniform samples of multivariate functions, in order to enable a variety of operations such as data visualization. We are interested in computing sparse but approximate interpolants, i.e., approximate interpolants with few coefficients. Such interpolants are useful since (1) the cost of evaluating the interpolant scales directly with the number of nonzero coefficients, and (2) the principle of Occam's Razor suggests that the interpolant with fewer coefficients better approximates the underlying function. Since the number of coefficients in a multiquadric interpolant is, as is to be expected, equal to the number of data points in the given set, the problem can be abstracted thus: given a set S of samples of a function f : R(k) --> R, and an error tolerance delta, find the smallest set of points T subset-or-equal-to S such that the multiquadric interpolant of T is within delta of f over S. Using some recent results on sparse solutions of linear systems, we show how T may be selected in a provably good fashion. CR BLUMER A, 1989, J ASSOC COMPUT MACH, V36, P929 CARLSON RE, 1991, COMPUT MATH APPL, V21, P29 FOLEY TA, 1986, COMPUT AIDED GEOM D, V3, P163 FRANKE R, 1979, NPS5379003 NAV POSTG GOLUB GH, 1983, MATRIX COMPUTATIONS HARDY RL, 1990, COMPUT MATH APPL, V19, P163 JOHNSON DS, 1974, J COMPUT SY, V9, P256 KANSA EJ, 1991, UCRLJC108658 L LIVER MICHELLI CA, 1986, CONSTR APPROX, V2, P11 NATARAJAN BK, 1993, 6TH P ACM C COMP LEA NATARAJAN BK, IN PRESS SIAM J COMP NATARAJAN BK, 1991, MACHINE LEARNING THE NATARAJAN BK, 1993, P IEEE DATA COMPRESS TARWATER AE, 1985, UCRL53670 L LIV LABS TC 4 BP 99 EP 108 PG 10 JI Comput. Math. Appl. PY 1994 PD MAR VL 27 IS 6 GA NA083 PI OXFORD RP HEWLETT PACKARD CORP,1501 PAGE MILL RD,3U,PALO ALTO,CA 94304 J9 COMPUT MATH APPL PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1994NA08300010 ER PT Journal AU LATEINER, JS TI INTERACTIVE STEREOSCOPIC VOLUME VISUALIZATION - ENHANCING DEPTH-PERCEPTION TO PROVIDE BETTER 3-DIMENSIONAL IMAGE DATA VISUALIZATION ENVIRONMENTS SO RADIOLOGY LA English DT Meeting Abstract NR 0 SN 0033-8419 PU RADIOLOGICAL SOC NORTH AMER TC 0 BP 622 EP 622 PG 1 JI Radiology PY 1994 PD FEB VL 190 IS 2 GA MW444 PI EASTON J9 RADIOLOGY PA 20TH AND NORTHAMPTON STS, EASTON, PA 18042 UT ISI:A1994MW44400083 ER PT Journal AU CETIN, H WARNER, TA LEVANDOWSKI, DW TI DATA CLASSIFICATION, VISUALIZATION, AND ENHANCEMENT USING N- DIMENSIONAL PROBABILITY DENSITY-FUNCTIONS (NPDF) - AVIRIS, TIMS, TM, AND GEOPHYSICAL APPLICATIONS SO PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING LA English DT Article NR 11 SN 0099-1112 PU AMER SOC PHOTOGRAMMETRY C1 PURDUE UNIV,DEPT EARTH & ATMOSPHER SCI,W LAFAYETTE,IN 47907 W VIRGINIA UNIV,DEPT GEOL & GEOG,MORGANTOWN,WV 26506 AB The n-Dimensional Probability Density Functions (nPDF) approach is a user-interactive image analysis technique which overcomes many of the inherent limitations of traditional classifiers. In this paper we illustrate the applications of nPDF analysis in three broad areas: data visualization, enhancement, and classification. For data visualization, nPDF provides a method for transforming multiple bands of data in a predictable and scene-independent way. These transformations may be designed so as to enhance a particular cover type, or to give the best visual representation of the multi-band image data. These approaches are illustrated with the enhancement of hydrothermally altered areas in Thematic Mapper (Tm) data, and the display of a false-color composite of six bands of Thermal Infrared Multispectral Scanner (TIMS) imagery. Spectral frequency plots of the nPDF components give a multispectral view of data distribution that can be used to investigate the number and distribution of spectral classes in a high dimensional data set. In addition, these plots are used in a non-parametric classification of the image for discrimination of discrete classes, as well as for classes that are mixtures at the sub-pixel scale. In a mixed deciduous and coniferous forest, an nPDF Deciduous Forest Index shows a high correlation with percent deciduous vegetation determined from field surveys. A classification of TIMS imagery of Death Valley results in excellent discrimination of 13 discrete rock types. Classification of TM data, as well as classification of combined geophysical data, is used to illustrate the power and variety of complex applications. The procedure is the opposite of a ''black box'' approach: nPDF transformations and plots show graphical representations of the spectral and informational class distributions, and the user decides on the exact location of the spectral boundaries of each class in the classification. In comparisons with standard statistical classifiers, nPDF is extremely accurate and fast, making it possible to analyze large data sets, such as full scenes of Advanced Visible/Infrared Imaging Spectrometer (AVIRIS) data, on a personal computer. CR 1990, ERDAS VERSION 7 USER CETIN H, 1990, 10TH P ANN INT GEOSC, V1, P353 CETIN H, 1992, GEOTECH 92 GEOCOMPUT, P397 CETIN H, 1991, PHOTOGRAMM ENG REM S, V57, P1579 GILLESPIE AR, 1986, REMOTE SENS ENVIRON, V20, P209 HUNT CB, 1966, 494A US GEOL SURV PR HUSCH B, 1982, FOREST MENSURATION PALLUCONI FD, 1985, JPL PUBLICATION, V8537 WARNER TA, 1993, 9TH P THEM C GEOL RE, V1, P345 WARNER TA, 1990, JPL PUBLICATION, V9055, P31 WARNER TA, 1991, PHOTOGRAMM ENG REM S, V57, P1179 TC 13 BP 1755 EP 1764 PG 10 JI Photogramm. Eng. Remote Sens. PY 1993 PD DEC VL 59 IS 12 GA MR778 PI BETHESDA RP PURDUE UNIV,DEPT EARTH & ATMOSPHER SCI,W LAFAYETTE,IN 47907 J9 PHOTOGRAMM ENG REMOTE SENSING PA 5410 GROSVENOR LANE SUITE 210, BETHESDA, MD 20814-2160 UT ISI:A1993MR77800005 ER PT Journal AU ASHWIN, P SWIFT, JW TI UNFOLDING THE TORUS - OSCILLATOR GEOMETRY FROM TIME DELAYS SO JOURNAL OF NONLINEAR SCIENCE LA English DT Article NR 9 SN 0938-8974 PU SPRINGER VERLAG C1 UNIV WARWICK,INST MATH,COVENTRY CV4 7AL,W MIDLANDS,ENGLAND NO ARIZONA UNIV,DEPT MATH,FLAGSTAFF,AZ 86011 DE COUPLED OSCILLATORS; DATA VISUALIZATION; TORUS MAPS ID IDENTICAL OSCILLATORS AB We present a simple method of plotting the trajectories of systems of weakly coupled oscillators. Our algorithm uses the time delays between the ''firings'' of the oscillators. For any system of n weakly coupled oscillators there is an attracting invariant n-dimensional torus, and the attractor is a subset of this invariant torus. The invariant torus intersects a suitable codimension-1 surface of section at an (n - 1)-dimensional torus. The dynamics of n coupled oscillators can thus be reduced, in principle, to the study of Poincare maps of the (n - 1)-dimensional torus. This paper gives a practical algorithm for measuring the n - 1 angles on the torus. Since visualization of 3 (or higher) dimensional data is difficult we concentrate on n = 3 oscillators. For three oscillators, a standard projection of the Poincare map onto the plane yields a projection of the 2-torus which is 4-to-1 over most of the torus, making it difficult to observe the structure of the attractor. Our algorithm allows a direct measurement of the 2 angles on the torus, so we can plot a 1-to-1 map from the invariant torus to the ''unfolded torus'' where opposite edges of a square are identified. In the cases where the attractor is a torus knot, the knot type of the attractor is obvious in our projection. CR ASHWIN P, 1991, 581991 U WARW MATH I ASHWIN P, 1992, J NONLINEAR SCI, V2, P69 ASHWIN P, 1990, NONLINEARITY, V3, P585 BAESENS C, 1991, PHYSICA D, V49, P387 CUMMING A, 1988, PHYS REV LETT, V60, P2719 KIM SH, 1986, PHYS REV A, V34, P3426 LINSAY PS, 1989, PHYSICA D, V40, P196 PACKARD NH, 1980, PHYS REV LETT, V45, P712 TAKENS F, 1981, LECT NOTES MATH, V898, P336 TC 6 BP 459 EP 475 PG 17 JI J. Nonlinear Sci. PY 1993 VL 3 IS 4 GA MJ698 PI NEW YORK RP ASHWIN P UNIV WARWICK,INST MATH,COVENTRY CV4 7AL,W MIDLANDS,ENGLAND J9 J NONLINEAR SCI PA 175 FIFTH AVE, NEW YORK, NY 10010 UT ISI:A1993MJ69800003 ER PT Journal AU HALL, P LI, KC TI ON ALMOST LINEARITY OF LOW-DIMENSIONAL PROJECTIONS FROM HIGH- DIMENSIONAL DATA SO ANNALS OF STATISTICS LA English DT Article NR 25 SN 0090-5364 PU INST MATHEMATICAL STATISTICS C1 AUSTRALIAN NATL UNIV,CTR MATH & APPLICAT,CANBERRA,ACT 2601,AUSTRALIA CSIRO,CANBERRA,ACT 2601,AUSTRALIA UNIV CALIF LOS ANGELES,DEPT MATH,LOS ANGELES,CA 90024 DE PROJECTIONS; PROJECTION PURSUIT; DATA VISUALIZATION; DIMENSION REDUCTION; SLICED INVERSE REGRESSION; REGRESSION ANALYSIS; LINK VIOLATION ID SLICED INVERSE REGRESSION; PURSUIT REGRESSION; DATA VISUALIZATION; REDUCTION; LINK AB This paper studies the shapes of low dimensional projections from high dimensional data. After standardization, let x be a p-dimensional random variable with mean zero and identity covariance. For a projection beta'x, \\beta\\ = 1, find another direction b so that the regression curve of b'x against beta'x is as nonlinear as possible. We show that when the dimension of x is large, for most directions beta even the most nonlinear regression is still nearly linear. Our method depends on the construction of a pair of p-dimensional random variables, w1, w2, called the rotational twin, and its density function with respect to the standard normal density. With this, we are able to obtain closed form expressions for measuring deviation from normality and deviation from linearity in a suitable sense of average. As an interesting by-product, from a given set of data we can find simple unbiased estimates of E(f(beta'x)(t)/phi1(t)-1)2 and E[(\\E(x\beta, beta'x = t)\\2- t2)f(beta'x)2(t)/phi1(2)t)], where phi1 is the standard normal density, f(beta'x) is the density for beta'x and the ''E'' is taken with respect to the uniformly distributed beta. This is achieved without any smoothing and without resorting to any laborious projection procedures such as grand tours. Our result is related to the work of Diaconis and Freedman. The impact of our result on several fronts of data analysis is discussed. For example, it helps establish the validity of regression analysis when the link function of the regression model may be grossly wrong. A further generalization, which replaces beta'x by B'x with B = (beta1,...,beta(k)) for k randomly selected orthonormal vectors (beta(i), i = 1,...,k), helps broaden the scope of application of sliced inverse regression (SIR). CR BRILLINGER DR, 1977, BIOMETRIKA, V64, P509 BRILLINGER DR, 1983, FESTSCHRIFT EL LEHMA, P97 CARROLL RJ, 1992, J AM STAT ASSOC, V87, P1040 CHEN H, 1991, ANN STAT, V19, P142 CLEVELAND WS, 1988, COLLECTED WORKS JW T, V5 CLEVELAND WS, 1988, DYNAMIC GRAPHICS STA COOK RD, 1991, J AM STAT ASSOC, V86, P328 DIACONIS P, 1984, ANN STAT, V12, P793 DONOHO DL, 1989, ANN STAT, V17, P58 DUAN N, 1991, ANN STAT, V19, P505 FRIEDMAN JH, 1987, J AM STAT ASSOC, V82, P249 FRIEDMAN JH, 1981, J AM STAT ASSOC, V76, P817 HAERDLE W, 1989, J AM STAT ASSOC, V84, P986 HALL P, 1989, ANN STAT, V17, P573 HALL P, 1989, ANN STAT, V17, P589 HSING TL, 1992, ANN STAT, V20, P1040 HUBER PJ, 1985, ANN STAT, V13, P435 LI KC, 1989, ANN STAT, V17, P1009 LI KC, 1992, J AM STAT ASSOC, V87, P1025 LI KC, 1991, J AM STAT ASSOC, V86, P316 LI KC, 1992, PROBABILITY STAT, P138 LI KC, 1990, UCLA STATISTICAL SER, V24 TIERNEY L, 1990, LISP STAT OBJECT ORI WEGMAN EJ, 1986, STATISTICAL IMAGE PR WHITTLE P, 1960, THEOR PROBAB APPL, V5, P302 TC 25 BP 867 EP 889 PG 23 JI Ann. Stat. PY 1993 PD JUN VL 21 IS 2 GA LV451 PI HAYWARD RP HALL P AUSTRALIAN NATL UNIV,CTR MATH & APPLICAT,CANBERRA,ACT 2601,AUSTRALIA J9 ANN STATIST PA IMS BUSINESS OFFICE-SUITE 6 3401 INVESTMENT BLVD, HAYWARD, CA 94545 UT ISI:A1993LV45100019 ER PT Journal AU CONSENS, MP HASAN, MZ TI SUPPORTING NETWORK MANAGEMENT THROUGH DECLARATIVELY SPECIFIED DATA VISUALIZATIONS SO IFIP TRANSACTIONS C-COMMUNICATION SYSTEMS LA English DT Article NR 25 SN 0926-549X PU ELSEVIER SCIENCE BV C1 UNIV TORONTO,COMP SYST RES INST,TORONTO M5S 1A1,ONTARIO,CANADA DE NETWORK MANAGEMENT; MANAGEMENT INFORMATION DATABASES; DATA VISUALIZATION; VISUAL DATABASE SYSTEMS; GRAPHLOG VISUAL QUERY LANGUAGE; HY+ SYSTEM AB The complexity of managing and controlling large heterogeneous networks requires the availability of management stations equipped with sophisticated tools. A fundamental feature of an advanced network management station is the capability to present to the human manager a complete picture of the relevant scenarios. The overwhelming volume and complexity of the information involved in network management scenarios poses a major challenge. In this paper we show how the Hy+ visual database system fulfills the information presentation requirements of advanced network management stations. A visual database system is capable of manipulating data visualizations through visually expressed queries. The Hy+ system provides a uniform framework for hygraph based data visualizations, queries, and their results. Visual queries, expressed in the GraphLog language, are interpreted as patterns that match existing visualizations and create new ones. A prime example of the functionality supported by the system consists of describing a query that filters a large visualization to retain the portion that is of interest for the network manager in the context of a particular task. Examples of data visualizations that are relevant for network management are the network topology at different levels of abstraction, the presentation of network configuration information, and the display of management information bases and their history traces. Employing a fault management scenario as motivation, we demonstrate how the Hy+ system deals with all of the above visualizations and several of their combinations, as well as entirely new ones generated through ad-hoc visual queries. 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PY 1993 VL 12 GA LJ990 PI AMSTERDAM RP CONSENS MP UNIV TORONTO,COMP SYST RES INST,TORONTO M5S 1A1,ONTARIO,CANADA J9 IFIP TRANS C PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1993LJ99000055 ER PT Journal AU BROWN, JF LOVELAND, TR MERCHANT, JW REED, BC OHLEN, DO TI USING MULTISOURCE DATA IN GLOBAL LAND-COVER CHARACTERIZATION - CONCEPTS, REQUIREMENTS, AND METHODS SO PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING LA English DT Article NR 38 SN 0099-1112 PU AMER SOC PHOTOGRAMMETRY C1 UNIV NEBRASKA,INST AGR & NAT RESOURCES,CTR ADV LAND MANAGEMENT INFORMAT TECHNOL,LINCOLN,NE 68588 US GEOL SURVEY,EROS DATA CTR,LAND SCI RES PROGRAM,SIOUX FALLS,SD 57198 HUGHES STX CORP,EROS DATA CTR,SIOUX FALLS,SD 57198 ID CONTERMINOUS UNITED-STATES; DIGITAL TERRAIN DATA; ANCILLARY DATA; CLASSIFICATION; IMAGERY AB Global land-cover data are needed as baseline information for global change research, Multisource data, both coarse- resolution satellite data and ancillary data, were used to produce a land-cover characteristics database for the conterqinous United States. Ancillary data, including elevation and ecological region data sets, were critical to the development, refinement, and information content of each class in the database. They contributed essential evidence for labeling and refining land-cover classes where differing types were represented by single spectral-temporal signatures. The characterization process can be expanded to a global effort depending on (1) the availability of global satellite coverage, (2) the quality and availability of ancillary data, and (3) the evolution of more sophisticated data visualization and analysis techniques. 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PY 1993 PD JUN VL 59 IS 6 GA LH904 PI BETHESDA RP BROWN JF UNIV NEBRASKA,INST AGR & NAT RESOURCES,CTR ADV LAND MANAGEMENT INFORMAT TECHNOL,LINCOLN,NE 68588 J9 PHOTOGRAMM ENG REMOTE SENSING PA 5410 GROSVENOR LANE SUITE 210, BETHESDA, MD 20814-2160 UT ISI:A1993LH90400009 ER PT Journal AU BENTLEY, R RODDEN, T SAWYER, P SOMMERVILLE, I STIEGLER, H DEWAN, P KWASNIK, B HARRISON, M TI A PROTOTYPING ENVIRONMENT FOR DYNAMIC DATA VISUALIZATION SO IFIP TRANSACTIONS A-COMPUTER SCIENCE AND TECHNOLOGY LA English DT Article NR 17 SN 0926-5473 PU ELSEVIER SCIENCE BV C1 UNIV LANCASTER,DEPT COMP,LANCASTER LA1 4YR,ENGLAND SYRACUSE UNIV,SYRACUSE,NY 13244 UNIV YORK,YORK YO1 5DD,N YORKSHIRE,ENGLAND SIEMENS NIXDORF,MUNICH,GERMANY PURDUE UNIV,W LAFAYETTE,IN 47907 DE INFORMATION INTERFACES AND PRESENTATIONS; USER INTERFACES; GROUP AND ORGANIZATION INTERFACES; MODELS AND PRINCIPLES; USER MACHINE SYSTEMS ID USER AB This paper describes the model underlying a user interface prototyping system which is designed to support the creation of multi-user, interactive database visualisations. The context of the work is the automation of an air traffic control system; we are concerned with the user interface to the in-flight database. The model is based on active display agents which interact in real-time with the database. The model separates the selection of entities for display, the entity representations and the way in which these representations are presented to specific controllers. The advantages of this model are that it allows multiple database views to be updated concurrently, it allows views to be shared by users at different workstations and it allows a high degree of end-user tailorability. CR BENTLEY R, 1992, IN PRESS NOV P CSCW BERTIN J, 1983, SEMIOLOGY GRAPHICS BORNING A, 1986, ACM T GRAPHIC, V5, P345 CASNER SM, 1991, ACM T GRAPHIC, V10, P111 FISCHER G, 1990, APR P CHI 90 SEATTL, P183 HAARSLEV V, 1990, OCT P ECOOP OOPSLA 9, P237 HARTSON R, 1989, IEEE SOFTWARE, V6, P62 HIX D, 1990, IEEE SOFTWARE, V7, P77 HUGHES JA, 1988, AUTOMATION AIR TRAFF LARSON JA, 1986, COMPUTER, V19, P62 MACKINLAY J, 1983, IEEE COMPUT GRAPH, V3, P11 MYERS B, 1991, ACM CHI 91, P243 MYERS BA, 1992, IN PRESS ADV HUMAN C, V4 NORMAN D, 1986, USER CTR SYSTEM DESI SOMMERVILLE I, 1992, IN PRESS SEP P HCI 9 WAITE KW, 1992, UNPUB ACM T OFFICE S WINDSOR P, 1990, INTERACT 90, P309 TC 0 BP 335 EP 348 PG 14 JI IFIP Trans. A-Comp. Sci. Technol. PY 1992 VL 18 GA LG013 PI AMSTERDAM RP BENTLEY R UNIV LANCASTER,DEPT COMP,LANCASTER LA1 4YR,ENGLAND J9 IFIP TRANS A PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1992LG01300021 ER PT Journal AU BARTELS, KA CRAWFORD, RH DAS, S GUDUR, S BOVIK, AC DILLER, KR AGGARWAL, SJ TI FABRICATION OF MACROSCOPIC SOLID MODELS OF 3-DIMENSIONAL MICROSCOPIC DATA BY SELECTIVE LASER SINTERING SO JOURNAL OF MICROSCOPY-OXFORD LA English DT Article NR 11 SN 0022-2720 PU BLACKWELL SCIENCE LTD C1 UNIV TEXAS,DEPT ELECT & COMP ENGN,AUSTIN,TX 78712 UNIV TEXAS,DEPT MECH ENGN,AUSTIN,TX 78712 DE CONFOCAL MICROSCOPY; SELECTIVE LASER SINTERING; FREE FORM FABRICATION; VISUALIZATION; 3-DIMENSIONAL IMAGE PROCESSING AB We discuss and give examples of the use of selective laser sintering to fabricate solid macroscopic models of microscopic specimens that have been imaged with a confocal microscope. The digital image processing necessary to create structurally sound models of both translucent and opaque specimens is presented. The fabricated models offer the ultimate in data visualization since they can be physically handled and manipulated to investigate the shape and features of the specimen. Such a powerful visualization tool is useful in both research and educational environments. CR ASHLEY S, 1991, MECH ENG, V113, P34 BOVIK AC, 1987, IEEE T PATTERN ANAL, V9, P181 BOVIK AC, 1991, IMAGE ANAL BIOL, P30 CIMA MJ, 1991, P SOL FREE FORM FABR, P187 DECKARD CR, 1987, 14 C PROD RES TECHN, P447 FOLEY JD, 1990, COMPUTER GRAPHICS HUANG TS, 1981, 2 DIMENSIONAL DIGI 2, V43, P191 ROGERS WE, 1991, P SOLID FREEFORM FAB, P158 ROSENFELD A, 1982, DIGITAL PICTURE PROC WEISS LE, 1991, P SOLID FREEFORM FAB, P178 WILSON T, 1990, CONFOCAL MICROSCOPY TC 1 BP 383 EP 389 PG 7 JI J. Microsc.-Oxf. PY 1993 PD MAR VL 169 PN 3 GA KX707 PI OXFORD RP BARTELS KA UNIV TEXAS,DEPT ELECT & COMP ENGN,AUSTIN,TX 78712 J9 J MICROSC-OXFORD PA OSNEY MEAD, OXFORD, OXON, ENGLAND OX2 0EL UT ISI:A1993KX70700007 ER PT Journal AU ZIMMERMAN, SS TI USING DATA VISUALIZATION SOFTWARE TO ANALYZE DATA ON AMINO- ACID-RESIDUES OF GLOBULAR-PROTEINS SO ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY LA English DT Meeting Abstract NR 0 SN 0065-7727 PU AMER CHEMICAL SOC C1 BRIGHAM YOUNG UNIV,GRAD SECT BIOCHEM,PROVO,UT 84602 BRIGHAM YOUNG UNIV,DEPT CHEM,PROVO,UT 84602 TC 0 BP 11 EP COMP PG 0 JI Abstr. Pap. Am. Chem. Soc. PY 1993 PD MAR 28 VL 205 PN 1 GA KQ981 PI WASHINGTON RP BRIGHAM YOUNG UNIV,GRAD SECT BIOCHEM,PROVO,UT 84602 J9 ABSTR PAP AMER CHEM SOC PA 1155 16TH ST, NW, WASHINGTON, DC 20036 UT ISI:A1993KQ98101663 ER PT Journal AU Martin, IM Marinescu, DC TI Concurrent computation and data visualization for spherical- virus structure determination SO IEEE COMPUTATIONAL SCIENCE & ENGINEERING LA English DT Article NR 19 SN 1070-9924 PU IEEE COMPUTER SOC C1 IBM Corp, Thomas J Watson Res Ctr, Box 218, Yorktown Heights, NY 10598 USA IBM Corp, Thomas J Watson Res Ctr, Yorktown Heights, NY 10598 USA Purdue Univ, Dept Comp Sci, W Lafayette, IN 47907 USA ID MICROSCOPY AB The authors present concurrent algorithms and programs for structure determination using structural information obtained through X-ray crystallography and electron microscopy. They introduce two interactive software systems, Emma and Tonitza, that support processing large data sets produced in structural biology experiments. CR BAKER TS, 1997, 97055 CSDTR PURD U D BAKER TS, 1996, J STRUCT BIOL, V116, P120 BOTTCHER B, 1997, NATURE, V386, P88 BRANDEN C, 1991, INTRO PROTEIN STRUCT CARTER CW, 1997, METHODS ENZYMOLOGY, V277 CHENG RH, 1995, CELL, V80, P621 CONNWAY JF, 1997, NATURE, V386, P91 CORNEAHASEGAN MA, 1995, ACTA CRYSTALLOGR D, V51, P749 CROWTHER RA, 1970, P ROY SOC LOND A MAT, V317, P319 HENN C, 1996, J STRUCT BIOL, V116, P86 LORENSEN WE, 1987, COMPUT GRAPHICS, V21, P163 LYNCH RE, 1997, CSDTR97042 PURD U DE MARINESCU DC, 1993, CONCURRENCY-PRACT EX, V5, P635 MARTIN IMB, 1997, J STRUCT BIOL, V120, P146 MUCKELBAUER JK, 1995, STRUCTURE, V3, P653 OLSON NH, 1989, ULTRAMICROSCOPY, V30, P281 ROSSMANN MG, 1962, ACTA CRYSTALLOGR, V15, P42 THUMANCOMMIKE PA, 1995, J MICROS SOC AM, V1, P191 VANHEEL M, 1982, ULTRAMICROSCOPY, V8, P331 TC 1 BP 40 EP 52 PG 13 JI IEEE Comput. Sci. Eng. PY 1998 PD OCT-DEC VL 5 IS 4 GA 148HU PI LOS ALAMITOS RP Martin IM IBM Corp, Thomas J Watson Res Ctr, Box 218, Yorktown Heights, NY 10598 USA J9 IEEE COMPUT SCI ENG PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000077499900005 ER PT Journal AU Pook, S Vaysseix, G Barillot, E TI Zomit: biological data visualization and browsing SO BIOINFORMATICS LA English DT Article NR 16 SN 1367-4803 PU OXFORD UNIV PRESS C1 GIS Infobiogen, 7 Rue Guy Moquet,BP 8, F-94801 Villejuif, France GIS Infobiogen, F-94801 Villejuif, France Ecole Natl Super Telecommun, Dept Informat & Reseaux, F-75634 Paris 13, France Genethon, F-91000 Evry, France ID HUMAN GENOME; MAP AB Motivation: The problems caused by the difficulty in visualizing and browsing biological databases have become crucial. Scientists can no longer interact directly with the huge amount of available data. However , future breakthroughs in biology depend on this interaction. We propose a new metaphor for biological data visualization and browsing that allows navigation in very large databases in an intuitive way. The concepts underlying our approach are based on navigation and visualization with zooming, semantic zooming and portals; and on data transformation via magic lenses. We think that these new visualization and navigation techniques should be applied globally to a federation of biological databases. Results: We have implemented a generic tool, called Zomit, that provides an application programming interface for developing servers for such navigation and visualization, and a generic architecture-independent client (Java(TM) applet) that queries such servers. As an illustration of the capabilities of our approach, we have developed ZoomMap, a prototype browser for the HuGeMap human genome map database. CR ACHARD F, 1997, PAC S BIOC 97, P39 BARILLOT E, 1998, NUCLEIC ACIDS RES, V26, P106 BEDERSON BB, 1996, J VISUAL LANG COMPUT, V7, P3 BELLANNECHANTEL.C, 1992, CELL, V70, P1059 BURNAS GW, 1995, CHI 95 HUM FACT COMP, P234 CHUMAKOV IM, 1995, NATURE, V377, P175 COHEN D, 1993, NATURE, V336, P698 DAVIDSON SB, 1995, J COMPUT BIOL, V2, P557 DIB C, 1996, NATURE, V380, P152 HUDSON TJ, 1995, SCIENCE, V270, P1945 LEUNG YK, 1994, ACM T COMPUTER HUMAN, V1, P126 ROBINSON AJ, 1997, ISMB 97, P241 RODRIGUEZTOME P, 1997, NUCLEIC ACIDS RES, V25, P81 SHEFFIELD VC, 1995, HUM MOL GENET, V4, P1837 STONE MC, 1994, CHI 94 HUM FACT COMP, P306 VANDAM A, 1997, COMMUN ACM, V40, P63 TC 3 BP 807 EP 814 PG 8 JI Bioinformatics PY 1998 VL 14 IS 9 GA 148DR PI OXFORD RP Pook S GIS Infobiogen, 7 Rue Guy Moquet,BP 8, F-94801 Villejuif, France J9 BIOINFORMATICS PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND UT ISI:000077489900008 ER PT Journal AU Pinciroli, F Portoni, L Combi, C Violante, FF TI WWW-based access to object-oriented clinical databases: the KHOSPAD project SO COMPUTERS IN BIOLOGY AND MEDICINE LA English DT Article NR 43 SN 0010-4825 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Politecn Milan, Dipartimento Bioingn, Pzza Leonardo Da Vinci 32, I-20133 Milan, Italy Politecn Milan, Dipartimento Bioingn, I-20133 Milan, Italy CNR, Ctr Ingn Biomed, Milan, Italy Politecn Milan, Dottorato Ricerca Bioingn, I-20133 Milan, Italy Univ Udine, Dipartimento Matemat & Informat, I-33100 Udine, Italy DE object-oriented clinical databases; temporal databases; WWW; internet; Java; software architecture; temporal data visualization ID WORLD-WIDE-WEB; INFORMATION; SYSTEM AB KHOSPAD is a project aiming at improving the quality of the process of patient care concerning general practitioner- patient-hospital relationships, using current information and networking technologies. The studied application field is a cardiology division, with hemodynamic laboratory and the population of PTCA patients. Data related to PTCA patients are managed by ARCADIA, an object-oriented database management system developed for the considered clinical setting. We defined a remotely accessible view of ARCADIA medical record, suitable for general practitioners (GPs) caring patients after PTCA, during the follow-up period. Using a PC, a modem and Internet, an authorized GP can consult remotely the medical records of his PTCA patients. Main features of the application are related to the management and display of complex data, specifically characterized by multimedia and temporal features, based on an object-oriented temporal data model. (C) 1998 Elsevier Science Ltd. All rights reserved. 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Biol. Med. PY 1998 PD SEP VL 28 IS 5 GA 146BM PI OXFORD RP Pinciroli F Politecn Milan, Dipartimento Bioingn, Pzza Leonardo Da Vinci 32, I-20133 Milan, Italy J9 COMPUT BIOL MED PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000077407500007 ER PT Journal AU Rogowitz, BE Treinish, LA TI Data visualization: the end of the rainbow SO IEEE SPECTRUM LA English DT Article NR 9 SN 0018-9235 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 IBM Corp, Thomas J Watson Res Ctr, Yorktown Heights, NY 10598 USA IBM Corp, Thomas J Watson Res Ctr, Yorktown Heights, NY 10598 USA CR 1994, SPIE P HUMAN VISION, V2179, P287 BERGMAN LD, 1995, P IEEE VIS 95 C, P118 FOLEY JD, 1991, COMPUTER GRAPHICS PR LEFKOWITZ H, 1992, IEEE COMPUT GRAPH, V12, P72 ROBERTSON PK, 1988, IEEE COMPUT GRAPH, V8, P50 STEVEN SS, 1955, HDB EXPT PSYCHOL STEVENS SS, 1966, PERCEPT PSYCHOPHYS, V1, P5 TUFTE E, 1990, VISUAL DISPLAY QUANT WANDELL BA, 1995, FDN VISION TC 3 BP 52 EP 59 PG 8 JI IEEE Spectr. PY 1998 PD DEC VL 35 IS 12 GA 143MB PI NEW YORK RP Rogowitz BE IBM Corp, Thomas J Watson Res Ctr, Yorktown Heights, NY 10598 USA J9 IEEE SPECTRUM PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000077258500019 ER PT Journal AU Schuhmann, D Seemann, M Schoepf, UJ Haubner, M Krapichler, C Gebicke, K Reiser, M Englmeier, KH TI Computerized diagnostic data analysis and 3-D visualization SO RADIOLOGE LA German DT Article NR 47 SN 0033-832X PU SPRINGER VERLAG C1 GSF Forschungszentrum Umwelt & Gesundheit, Inst Med Informat & Syst Forsch, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany GSF Forschungszentrum Umwelt & Gesundheit, Inst Med Informat & Syst Forsch, D-85764 Neuherberg, Germany Univ Munich, Inst Radiol Diagnost, Munich, Germany DE image analysis; visualization; virtual reality; virtual endoscopy ID SPIRAL CT-ANGIOGRAPHY; ACTIVE SHAPE MODELS; SEGMENTATION; DISPLAY; IMAGES; COLOGRAPHY; COLON AB Purpose:To survey methods for 3D data visualization and image analysis which can be used for computer based diagnostics. Material and methods: The methods available are explained in short terms and links to the literature are presented. Methods which allow basic manipulation of 3D data are windowing, rotation and clipping. More complex methods for visualization of 3D data are multiplanar reformation, volume projections (MIP,semi-transparent projections) and surface projections. Methods for image analysis comprise local data transformation (e.g. filtering) and definition and application of complex models (e.g. deformable models). Results: Volume projections produce an impression of the 3D data set without reducing the data amount. This supports the interpretation of the 3D data set and saves time in comparison to any investigation which requires examination of all slice images. More advanced techniques for visualization, e.g. surface projections and hybrid rendering visualize anatomical information to a very detailed extent, but both techniques require the segmentation of the structures of interest. Image analysis methods can be used to extract these structures(e.g. an organ)from the image data. Discussion:At the present time volume projections are robust and fast enough to be used routinely. Surface projections can be used to visualize complex and presegmented anatomical features. 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High-speed video sequences reveal interesting dynamics, and expose the limitations of the traditional emitter-detector analysis technique. Refinements to the traditional technique are suggested, based on the video data. A novel data visualization and reduction technique for use with high-speed video is described. (C) 1998 Elsevier Science B.V. CR AUSTIN J, 1991, PHYS LETT A, V155, P148 DAROCHA MSF, 1996, PHYS REV E, V54, P2378 DREYER K, 1991, AM J PHYS, V59 EGGERS J, 1997, REV MOD PHYS, V69, P865 GREBOGI C, 1983, PHYSICA D, V7, P181 MARTIEN P, 1985, PHYS LETT A, V110, P399 NEDA Z, 1996, CHAOS, V6, P59 OTT E, 1993, CHAOS DYNAMICAL SYST, P4 PARDO WB, 1993, 2 EXP CHAOS C BOC RA PINTO RD, IN PRESS PHYS REV E SHAW RS, 1984, CHAOS DYNAMICAL SYST, P4 WU XM, 1989, PHYSICA D, V40, P433 WU XM, 1989, REV SCI INSTRUM, V60, P3779 YEPEZ HNN, 1989, EUR J PHYS, V10, P99 TC 3 BP 353 EP 358 PG 6 JI Phys. Lett. A PY 1998 PD NOV 16 VL 248 IS 5-6 GA 138HU PI AMSTERDAM RP Buch TN Univ Miami, Dept Phys, Nonlinear Dynam Lab, Coral Gables, FL 33146 USA J9 PHYS LETT A PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000076967100011 ER PT Journal AU Hirahara, Y Yamamoto, A Okamoto, N Tsuda, K Takada, T TI Molecular simulations in a heterogeneous computer environment SO NEC RESEARCH & DEVELOPMENT LA English DT Article NR 5 SN 0547-051X PU NEC CORPORATION C1 NEC Corp Ltd, Informatec Syst, Tokyo, Japan NEC Corp Ltd, Informatec Syst, Tokyo, Japan NEC Corp Ltd, Fundamental Res Labs, Tokyo, Japan DE molecular simulation; ab initio Molecular Orbital (MO) theory; molecular dynamics; network computing; virtual microscope AB Molecular simulations became useful for molecular design and drug design, since recent supercomputers are able to handle these molecules within a reasonable computer time. In this article, future images of such molecular simulation systems are described, especially focusing on distributed processing on vector and MPP (Massively Parallel Processor) computers connected by network. Hartree-Fock calculation, which is the most popular method in the ab initio Molecular Orbital (MO) scheme, is chosen as a benchmark to investigate efficiencies of vector and parallel computations. Since molecular simulations consist of several steps such as Hartree-Fock calculations, molecular dynamics, visualization and data handling, one question is how these tasks can be distributed to proper computers in a heterogeneous web computing environment. Some considerations on this issue are also made in this paper. CR SCALAPACK UNPUB INT J SUPERCOM *GRAPE, GRAPE PROJ HEHRE WJ, 1986, AB INITIO MOL ORBITA HUIJINAGA S, 1984, GAUSSIAN BASIS SETS TC 0 BP 439 EP 444 PG 6 JI NEC Res. Dev. PY 1998 PD OCT VL 39 IS 4 GA 137GH PI TOKYO RP Hirahara Y NEC Corp Ltd, Informatec Syst, Tokyo, Japan J9 NEC RES DEVELOP PA 7-1 SHIBA 5-CHOME, MINATO-KU, TOKYO, 108-01, JAPAN UT ISI:000076906000019 ER PT Journal AU Girolami, M Cichocki, A Amari, S TI A common neural-network model for unsupervised exploratory data analysis and independent component analysis SO IEEE TRANSACTIONS ON NEURAL NETWORKS LA English DT Article NR 19 SN 1045-9227 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 RIKEN, Inst Phys & Chem Res, Brain Sci Inst, Lab Open Informat Syst, Wako, Saitama 35101, Japan RIKEN, Inst Phys & Chem Res, Brain Sci Inst, Lab Open Informat Syst, Wako, Saitama 35101, Japan Lab Informat Synth, Wako, Saitama 35101, Japan DE blind source separation; data clustering; data visualization; independent component analysis; unsupervised learning ID PROJECTION PURSUIT; BLIND SEPARATION; ALGORITHM AB This paper presents the derivation of an unsupervised learning algorithm, which enables the identification and visualization of latent structure within ensembles of high-dimensional data. This provides a linear projection of the data onto a lower dimensional subspace to identify the characteristic structure of the observations independent latent causes. The algorithm is shown to be a very promising tool for unsupervised exploratory data analysis and data visualization. Experimental results confirm the attractiveness of this technique for exploratory data analysis and an empirical comparison is made with the recently proposed generative topographic mapping (GTM) and standard principal component analysis (PCA), Based on standard probability density models a generic nonlinearity is developed which allows both 1) identification and visualization of dichotomised clusters inherent in the observed data and 2) separation of sources with arbitrary distributions from mixtures, whose dimensionality may be greater than that of number of sources. The resulting algorithm is therefore also a generalized neural approach to independent component analysis (ICA) and it is considered to be a promising method for analysis of real-world data that will consist of sub- and super-Gaussian components such as biomedical signals. CR AMARI S, 1995, NEURAL INFORMATION P, V8, P757 AMARI S, 1997, NEURAL NETWORKS, V10, P1345 BELL AJ, 1995, NEURAL COMPUT, V7, P1129 BISHOP C, 1997, NCRG96028 AST U BISHOP C, NEURAL COMPUT, V10, P215 CARDOSO JF, 1997, IEEE T SIGNAL PROCES, V43, P3017 CICHOCKI A, 1994, ELECTRON LETT, V30, P1386 CICHOCKI A, IEEE T CIRCUITS SYST, V43, P894 COVER T, 1991, ELEMENTS INFORMATION DOUGLAS SC, 1997, P IEEE WORKSH NEUR N, P436 EVERITT BS, 1993, CLUSTER ANAL EVERITT BS, 1984, INTRO LATENT VARIABL FRIEDMAN JH, 1987, J AM STAT ASSOC, V82, P249 GIROLAMI M, 1998, NEURAL COMPUT, V10, P2103 GIROLAMI M, 1997, P I ELECT ENG VIS IM, V14, P299 HYVARINEN A, 1997, NEURAL COMPUT, V9, P1483 JONES MC, 1987, J ROY STAT SOC A GEN, V150, P1 KARHUNEN J, 1997, IEEE T NEURAL NETWOR, V8, P487 PEARSON K, 1894, PHILOS T ROY SOC A, V185, P71 TC 5 BP 1495 EP 1501 PG 7 JI IEEE Trans. Neural Netw. PY 1998 PD NOV VL 9 IS 6 GA 136QT PI NEW YORK RP Girolami M RIKEN, Inst Phys & Chem Res, Brain Sci Inst, Lab Open Informat Syst, Wako, Saitama 35101, Japan J9 IEEE TRANS NEURAL NETWORKS PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000076871200035 ER PT Journal AU Jerding, DF Stasko, JT TI The information mural: A technique for displaying and navigating large information spaces SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS LA English DT Article NR 37 SN 1077-2626 PU IEEE COMPUTER SOC C1 Georgia Inst Technol, Coll Comp, Graph Visualizat & Usabil Ctr, Atlanta, GA 30332 USA Georgia Inst Technol, Coll Comp, Graph Visualizat & Usabil Ctr, Atlanta, GA 30332 USA DE information visualization; software visualization; data visualization; focus+context; navigation; browsers ID PARALLEL PROGRAMS; VISUALIZATION AB Information visualizations must allow users to browse information spaces and focus quickly on items of interest. Being able to see some representation of the entire information space provides an initial gestalt overview and gives context to support browsing and search tasks. However, the limited number of pixels on the Screen constrain the information bandwidth and make it difficult to completely display large information spaces. The information Mural is a two-dimensional, reduced representation of an entire information space that fits entirely within a display window or screen. The Mural creates a miniature version of the information space using visual attributes, such as gray-scale shading, intensity, color. and pixel size, along with antialiased compression techniques. Information Murals can be used as stand-alone visualizations or in global navigational views; We have built several prototypes to demonstrate the use of Information Murals in Visualization applications; subject matter for these views includes computer software, scientific data, text documents, and geographic information. CR BARROS J, 1979, P 1979 SIGGRAPH C, V13, P260 BEARD DV, 1990, BEHAV INFORM TECHNOL, V9, P451 BECKER RA, 1987, TECHNOMETRICS, V29, P127 BERGMAN LD, 1995, P IEEE VIS 95 C, P118 CHIMERA R, 1992, P ACM SIGCHI 92 C HU, P293 CITRIN W, 1995, SOFTWARE PRACT EXPER, V25, P1367 CLEVELAND WS, 1985, ELEMENTS GRAPHING DA CLEVELAND WS, 1993, VISUALIZING DATA EICK SG, 1992, IEEE T SOFTWARE ENG, V18, P957 FOLEY JD, 1990, COMPUTER GRAPHICS PR FURNAS GW, 1986, P SIGCHI86, P16 HAEBERLI P, 1990, P SIGGRAPH 90 ACM SI, P309 HEALEY CG, 1996, P IEEE VIS 96 C SAN, P263 HEARST M, 1995, P CHI 95, P59 HEATH MT, 1991, IEEE SOFTWARE, V8, P29 HILL WC, 1992, P ACM C HUM FACT COM, P3 JERDING DF, 1995, P 1995 S US INT SOFT, P73 JERDING DF, 1997, P 4 WORK C REV ENG O, P56 JERDING DF, 1995, P IEEE VIS 95 S INF, P43 JERDING DF, 1997, PROC INT CONF SOFTW, P360 JOHNSON B, 1991, P IEEE VISUALIZATION, P284 KEIM DA, 1995, P VISUALIZATION 95, P279 KIMELMAN D, 1994, P IEEE VISUALIZATION, P172 LAFFRA C, 1994, P USENIX 6 CPLUSPL T MARTIN AR, 1995, P IEEE C VIS ATL GA, P271 MONMONIER M, 1996, LIE MAPS PLAISANT C, 1995, IEEE SOFTWARE, V12, P21 PRICE BA, 1993, J VISUAL LANGUAGES C, V4, P211 RAO R, 1992, P ACM SIGCHI 94 C HU, P318 ROBERTSON GG, 1993, COMMUN ACM, V36, P57 SARKAR M, 1992, P CHI 92, P83 STASKO J, 1998, SOFTWARE VISUALIZAIT STASKO JT, 1993, J PARALLEL DISTR COM, V18, P258 STASKO JT, 1996, P 1996 IEEE S VIS LA, P166 STONE MC, 1992, P ACM SIGCHI 94 C HU, P306 TUFTE ER, 1990, ENVISIONING INFORMAT TUFTE ER, 1983, VISUAL DISPLAY QUANT TC 2 BP 257 EP 271 PG 15 JI IEEE Trans. Vis. Comput. Graph. PY 1998 PD JUL-SEP VL 4 IS 3 GA 124JC PI LOS ALAMITOS RP Jerding DF Georgia Inst Technol, Coll Comp, Graph Visualizat & Usabil Ctr, Atlanta, GA 30332 USA J9 IEEE TRANS VISUAL COMPUT GR PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000076177800006 ER PT Journal AU Blackwell, M Morgan, F DiGioia, AM TI Augmented reality and its future in orthopaedics SO CLINICAL ORTHOPAEDICS AND RELATED RESEARCH LA English DT Article NR 16 SN 0009-921X PU LIPPINCOTT WILLIAMS & WILKINS C1 Carnegie Mellon Univ, Inst Robot, 5000 Forbes Ave, Pittsburgh, PA 15213 USA Carnegie Mellon Univ, Inst Robot, Pittsburgh, PA 15213 USA Mitsubishi Elect Informat Technol Ctr Amer, Cambridge, MA USA Univ Pittsburgh, Shadyside Hosp, Med Ctr, Pittsburgh, PA USA ID VISUALIZATION; REGISTRATION; SYSTEM AB Augmented reality is a display technique that combines supplemental information with the real world environment. Augmented reality systems are on the verge of being used everyday in medical training, preoperative planning, preoperative and intraoperative data visualization, and intraoperative tool guidance. The basic technologies of augmented reality are discussed, augmented reality systems currently being used in the medical domain are examined, and some future uses of these systems in orthopaedic applications are explored. CR BUCHOLZ RD, 1997, LECT NOTES COMPUT SC, V1205, P459 EDWARDS PJ, 1995, P 2 INT S MED ROB CO, P8 ETTINGER GJ, 1997, LECT NOTES COMPUT SC, V1205, P477 FRIETS EM, 1995, IEEE T BIO-MED ENG, V42, P867 GRIMSON WEL, 1996, IEEE T MED IMAGING, V15, P129 KAUFMAN S, 1997, ST HEAL T, V39, P131 LAVALLEE S, 1995, COMPUTER INTEGRATED, P77 MASUTANI J, 1995, J COMP AIDED SURG S, V1, P16 MAURER CR, 1993, INTERACTIVE IMAGE GU, P17 OLANO M, 1995, P 1995 S INT 3D GRAP, P218 OTOOLE RV, 1995, INTERACTIVE TECHNOLO, P271 ROLLAND JP, 1997, ST HEAL T, V39, P337 SCHMANDT C, 1983, COMPUT GRAPHICS, V17, P253 SIMON DA, 1997, LECT NOTES COMPUT SC, V1205, P583 SOLTAN P, 1995, INTERACTIVE TECHNOLO, P349 WEBB S, 1988, PHYSICS MED IMAGING TC 0 BP 111 EP 122 PG 12 JI Clin. Orthop. Rel. Res. PY 1998 PD SEP IS 354 GA 119LP PI PHILADELPHIA RP Blackwell M Carnegie Mellon Univ, Inst Robot, 5000 Forbes Ave, Pittsburgh, PA 15213 USA J9 CLIN ORTHOP RELATED RES PA 227 EAST WASHINGTON SQ, PHILADELPHIA, PA 19106 USA UT ISI:000075898000014 ER PT Journal AU Wolkenstein, MG Hutter, H Grasserbauer, M TI New software tools for visualization of analytical data SO FRESENIUS JOURNAL OF ANALYTICAL CHEMISTRY LA English DT Article NR 6 SN 0937-0633 PU SPRINGER VERLAG C1 Vienna Univ Technol, Inst Analyt Chem, Getreidemarkt 9-151, A- 1060 Vienna, Austria Vienna Univ Technol, Inst Analyt Chem, A-1060 Vienna, Austria AB Efforts towards using advanced computer graphics for improved visualization of three-dimensional (3-D) secondary ion mass spectrometry (SIMS) data are described. The application of the Visualization Toolkit (vtk), a freely available C++ class library for 3-D graphics and visualization for both PC and Unix systems, is demonstrated. Various available algorithms are used to analyze and visualize features otherwise hidden within data. A selection of examples is presented to demonstrate the capabilities of data visualization. CR BRODLIE KW, 1992, SCI VISUALIZATION TE GALLAGHER RS, 1995, COMPUTER VISUALIZATI HUTTER H, 1994, CHEMOMETR INTELL LAB, V24, P99 HUTTER H, 1992, MIKROCHIM ACTA, V107, P137 SCHROEDER W, 1996, VISUALIZATION TOOLKI UPSON C, 1989, IEEE COMPUT GRAPH, V9, P30 TC 2 BP 722 EP 724 PG 3 JI Fresenius J. Anal. Chem. PY 1998 PD JUL-AUG VL 361 IS 6-7 GA 114JU PI NEW YORK RP Wolkenstein MG Vienna Univ Technol, Inst Analyt Chem, Getreidemarkt 9-151, A-1060 Vienna, Austria J9 FRESENIUS J ANAL CHEM PA 175 FIFTH AVE, NEW YORK, NY 10010 USA UT ISI:000075606100068 ER PT Journal AU Mitchell, C Gekelman, W TI Real-time physics data-visualization system using Performer SO COMPUTERS IN PHYSICS LA English DT Article NR 15 SN 0894-1866 PU AMER INST PHYSICS C1 Univ Calif Los Angeles, Dept Phys, Los Angeles, CA 90095 USA Univ Calif Los Angeles, Dept Phys, Los Angeles, CA 90095 USA AB A data visualization system geared toward large (gigabyte or greater-than-gigabyte) time-varying scientific data sets has been written. The system is a distributed multiprocessed application that employs techniques used both in the video-game industry and military flight simulators to achieve real-time frame rates. On the computational side, the system calculates vector fields, streamlines, isosurfaces, and cutplanes in real time. The graphics component employs strategic visual database decomposition and levels of detail to maintain a fast frame rate. This allows one to navigate through the data sets as they evolve temporally. (C) 1998 American Institute of Physics. CR CORMEN T, 1990, ALGORITHMS FOLEY J, 1996, COMPUT GRAPH, P550 GEKELMAN W, 1997, J GEOPHYS RES, V102, PA4 GEKELMAN W, 1991, REV SCI INSTRUM, V62, P12 HAIMES R, 1994, P AIAA 32 AER SCI M JEPSON W, 1997, I ITSEC 97 19 INT IN KEIM DA, 1995, VISUALIZATION 95 LIPPMAN S, 1996, INSIDE C PLUS PLUS O, P78 LORENSEN WE, 1987, COMPUT GRAPHICS, V21, P163 MANDRAKE L, 1997, COMPUT PHYS, V11, P5 MORALES GJ, 1994, PHYS PLASMAS, V1, P3765 ROHOLF J, 1994, P ANN C SER, P381 SANTOS JR, 1997, UCLA CSD TECHNICAL R SCHROEDER W, 1996, VISUALIZATION TOOLKI SMITH S, 1991, VISUALIZATION 91, P248 TC 0 BP 371 EP 379 PG 9 JI Comput. Phys. PY 1998 PD JUL-AUG VL 12 IS 4 GA 114MX PI WOODBURY RP Mitchell C Univ Calif Los Angeles, Dept Phys, Los Angeles, CA 90095 USA J9 COMPUT PHYS PA CIRCULATION FULFILLMENT DIV, 500 SUNNYSIDE BLVD, WOODBURY, NY 11797-2999 USA UT ISI:000075614200019 ER PT Journal AU Ireland, J Walsh, RW Galsgaard, K TI Visualization of three-dimensional datasets SO SOLAR PHYSICS LA English DT Article NR 3 SN 0038-0938 PU KLUWER ACADEMIC PUBL C1 Univ St Andrews, Dept Math & Computat Sci, St Andrews KY16 9SS, Fife, Scotland Univ St Andrews, Dept Math & Computat Sci, St Andrews KY16 9SS, Fife, Scotland AB The effective visualization of three-dimensional (3d) datasets, both observationally and computationally derived, is an increasing problem in solar physics. We present here plots of computational data derived from the 3d reconstruction of the magnetic field of a loop system, rendered as anaglyphs. By combining images of the same 3d object from two slightly different angles a realistic and useful 3d effect is obtained, aiding data visualization. The application of the same technique to real solar data (such as from the Coronal Diagnostic Spectrometer (CDS) on board the Solar and Heliospheric Observatory (SOHO)) is discussed. CR PERKINS WJ, 1993, MED BIOL ENG COMPUT, V31, P557 TURNNIDGE R, 1997, IMAGING SCI J, V45, P43 WALSH RW, 1997, IN PRESS ASTRON SOC TC 1 BP 87 EP 90 PG 4 JI Sol. Phys. PY 1998 PD JUL VL 181 IS 1 GA 113KL PI DORDRECHT RP Ireland J Univ St Andrews, Dept Math & Computat Sci, St Andrews KY16 9SS, Fife, Scotland J9 SOL PHYS PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS UT ISI:000075550100006 ER PT Journal AU Corsi, S Pozzi, M Tagliabue, G TI A new real-time digital simulator of the turbine-alternator- grid system (STAR) for control apparatus closed-loop tests SO IEEE TRANSACTIONS ON ENERGY CONVERSION LA English DT Article NR 3 SN 0885-8969 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 ENEL SpA, R&D, Ctr Automat Res, Milan, Italy ENEL SpA, R&D, Ctr Automat Res, Milan, Italy AB A new generation of real-time digital simulator of the turbine- alternator-grid system (STAR) has been developed by ENEL-R&D- CRA in order to meet the demands of design review, commissioning test, preliminary assessment and final fine- tuning of excitation systems and voltage regulators, as well as of turbine actuators and speed governors. In particular, the electromechanical behavior of the overall turbine-alternator system, with respect to the electrical interconnected grid, may be theoretically studied, by using dynamic simulation models perfected with suitable details. The Control systems performances can also be easily checked and fine-tuned, in presence of small and large perturbations, both in normal and in emergency operating conditions. The simulator STAR, which has been successfully used also for the specialists training, can be effectively considered as a real turbine-alternator system. In fact its inputs may be directly connected to the interfaces of the excitation system and turbine governor; moreover, the simulator provides in output the process variables with the suitable format requested by the different control apparatuses. The paper describes the main design topics of the STAR simulator, as mainly regards its hardware and software architecture and the model framework of the turbine (steam, hydro or gas), alternator (with static and rotating exciter) and grid interconnection. The structure of the excitation control system and turbine speed governor, as well as the parameters configuration, runtime simulation, and data visualization procedures are shortly described. Finally, the STAR performances are shown through significant transient responses. CR ARCIDIACONO V, 1989, AEI ANN M BARATELLA P, 1995, IEE KTH STOCKH POW T MARCOCCI L, 1983, P IMACS NANT TC 0 BP 282 EP 291 PG 10 JI IEEE Trans. Energy Convers. PY 1998 PD SEP VL 13 IS 3 GA 113XQ PI NEW YORK RP Corsi S ENEL SpA, R&D, Ctr Automat Res, Milan, Italy J9 IEEE TRANS ENERGY CONVERS PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000075579500021 ER PT Journal AU Aldrich, C TI Visualization of transformed multivariate data sets with autoassociative neural networks SO PATTERN RECOGNITION LETTERS LA English DT Article NR 13 SN 0167-8655 PU ELSEVIER SCIENCE BV C1 Univ Stellenbosch, Dept Chem Engn, Private Bag X1, ZA-7602 Stellenbosch, South Africa Univ Stellenbosch, Dept Chem Engn, ZA-7602 Stellenbosch, South Africa DE neural networks; data projection; exploratory data analysis; data visualization ID PROJECTION AB Artificial neural networks have recently gained prominence as powerful tools for the projection of high-dimensional data, where fast interactive mapping of multi-dimensional data onto 2D or 3D maps with as little distortion as possible is required. These methods typically generate static maps of the data, based on some optimization criterion. A new strategy based on the transformation of the data prior to use of autoassociative neural networks is therefore proposed and it is shown that this strategy allows more flexible visualization of the data than is possible with either Kohonen or hidden target backpropagation (Sammon) neural networks, in that various perspectives of the multi-dimensional space can be explored by dynamically mapping the data with respect to user-defined vantage points in the multi-dimensional space. (C) 1998 Published by Elsevier Science B.V. All rights reserved. CR ANDERSON E, 1939, B AM IRIS SOC, V59, P2 BISWAS G, 1981, IEEE T PATTERN ANAL, V3, P701 HINTON GE, 1989, ARTIF INTELL, V40, P195 JAIN AK, 1992, JUN P IEEE INT JOINT, V3, P335 KOHONEN T, 1990, P IEEE, V78, P1464 KRAAIJVELD MA, 1995, IEEE T NEURAL NETWOR, V6, P548 KRAMER MA, 1992, COMPUT CHEM ENG, V16, P313 KRZANOWSKI WJ, 1994, MULTIVARIATE ANAL 1 LEWINSON L, 1994, DATABASE PROGRAMMING, V7, P50 MAO JC, 1995, IEEE T NEURAL NETWOR, V6, P296 MAREN AJ, 1990, HDB NEURAL COMPUTING SAMMON JW, 1969, IEEE T COMPUT, V18, P401 TATTERSALL GD, 1994, BT TECHNOL J, V12, P23 TC 2 BP 749 EP 764 PG 16 JI Pattern Recognit. Lett. PY 1998 PD JUN VL 19 IS 8 GA 107EJ PI AMSTERDAM RP Aldrich C Univ Stellenbosch, Dept Chem Engn, Private Bag X1, ZA-7602 Stellenbosch, South Africa J9 PATTERN RECOGNITION LETT PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000075193800012 ER PT Journal AU Scholl, DJ TI Translation-invariant data visualization with orthogonal discrete wavelets SO IEEE TRANSACTIONS ON SIGNAL PROCESSING LA English DT Letter NR 10 SN 1053-587X PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 Ford Motor Co, Dearborn, MI 48121 USA Ford Motor Co, Dearborn, MI 48121 USA AB Orthogonal discrete wavelet transforms, can be made translation-invariant by adding redundant wavelet coefficients through repeated shifting operations. Othogonality is lost, but isometry and compact time support can be preserved. The practical application to data visualization of scalograms based on such transforms is discussed and illustrated with measured transient signals. CR BEYLKIN G, 1992, SIAM J NUMER ANAL, V6, P1716 COHEN A, 1993, APPL COMPUT HARMON A, V1, P54 COHEN L, 1989, P IEEE, V77, P941 COHEN L, 1995, TIME FREQUENCY ANAL DAUBECHIES I, 1992, 10 LECT WAVELETS DAUBECHIES I, 1988, COMMUN PURE APPL MAT, V41, P909 FLANDRIN P, 1994, RECENT ADV WAVELET A, P309 LANG M, 1996, IEEE SIGNAL PROC LET, V3, P10 LIANG J, 1996, IEEE T SIGNAL PROCES, V44, P235 RIOUL O, 1991, IEEE SIGNAL PROC OCT, P14 TC 0 BP 2031 EP 2034 PG 4 JI IEEE Trans. Signal Process. PY 1998 PD JUL VL 46 IS 7 GA ZV830 PI NEW YORK RP Scholl DJ Ford Motor Co, Dearborn, MI 48121 USA J9 IEEE TRANS SIGNAL PROCESS PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000074345600024 ER PT Journal AU Mikkelson, D Worlton, T TI TOF-VIS, software for interactive exploration of time-of-flight data SO PHYSICA B LA English DT Article NR 1 SN 0921-4526 PU ELSEVIER SCIENCE BV C1 Univ Wisconsin, Dept Math Stat & Comp Sci, Menomonie, WI 54751 USA Univ Wisconsin, Dept Math Stat & Comp Sci, Menomonie, WI 54751 USA Argonne Natl Lab, IPNS, Argonne, IL 60439 USA DE data visualization; instrumentation; neutron scattering AB TOF-VIS is a fast, highly interactive program for examining time-of-flight neutron-scattering data. All spectra from an experiment are displayed simultaneously as an image. The data can be displayed in terms of time-of-flight, energy, wave vector, or lattice spacing. TOF-VIS has been used for examining data from IPNS and ISIS, and has been useful for diagnosing problems with instruments and detectors as well as for making a quick evaluation of the quality of the data. Hard copy output to a variety of devices using routines built on PGPLOT is now available. TOF-VIS is portable to VMS and UNIX, and is currently implemented primarily using object-based methods in C, MOTIF and X-Windows. (C) 1998 Elsevier Science B.V. All rights reserved. CR MIKKELSON DJ, 1995, NUCL INSTRUM METH A, V354, P112 TC 0 BP 142 EP 144 PG 3 JI Physica B PY 1997 PD DEC VL 241 GA ZT224 PI AMSTERDAM RP Mikkelson D Univ Wisconsin, Dept Math Stat & Comp Sci, Menomonie, WI 54751 USA J9 PHYSICA B PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000074062600040 ER PT Journal AU Klosowski, P Koennecke, M Tischler, JZ Osborn, R TI NeXus: A common format for the exchange of neutron and synchrotron data SO PHYSICA B LA English DT Article NR 2 SN 0921-4526 PU ELSEVIER SCIENCE BV C1 Natl Inst Stand & Technol, Reactor Res Div, Gaithersburg, MD 20899 USA Natl Inst Stand & Technol, Reactor Res Div, Gaithersburg, MD 20899 USA Paul Scherrer Inst, CH-5232 Villigen, Switzerland Argonne Natl Lab, Argonne, IL 60439 USA DE data format; data analysis; data visualization; computing AB NeXus is a data format for the exchange of neutron and synchrotron scattering data between facilities and user institutions. It has been developed by an international team of scientists and computer programmers from neutron and X-ray facilities around the world. The NeXus format uses the hierarchical data format (HDF) which is portable, binary, extensible and self-describing. The NeXus format defines the structure and contents of these HDF tiles in order to facilitate the visualization and analysis of neutron and X-ray data. In addition? an application program interface (API) is being produced in order to simplify the reading and writing of NeXus files. The details of the format are available at [http://www.neutron.anl.gov/NeXus/]. (C) 1998 Published by Elsevier Science B.V. All rights reserved. CR WORKSH SOFTW DEV NEU KLOSOWSKI P, 1996, NEXUS PORTABLE EXTEN TC 0 BP 151 EP 153 PG 3 JI Physica B PY 1997 PD DEC VL 241 GA ZT224 PI AMSTERDAM RP Klosowski P Natl Inst Stand & Technol, Reactor Res Div, Gaithersburg, MD 20899 USA J9 PHYSICA B PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000074062600043 ER PT Journal AU Stankiewicz, BA van Bergen, PF Smith, MB Carter, JF Briggs, DEG Evershed, RP TI Comparison of the analytical performance of filament and Curie- point pyrolysis devices SO JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS LA English DT Article NR 28 SN 0165-2370 PU ELSEVIER SCIENCE BV C1 Univ Utrecht, Fac Earth Sci, Organ Geochem Grp, POB 80021, NL- 3508 TA Utrecht, Netherlands Univ Bristol, Dept Geol, Biogeochem Res Ctr, Bristol BS8 1RJ, Avon, England Univ Bristol, Sch Chem, Organ Geochem Unit, Bristol BS8 1TS, Avon, England Univ Bristol, Comp Serv, Bristol BS8 1UD, Avon, England DE Curie-point; filament; comparison; biopolymers; geopolymers; pyrolysis ID CHROMATOGRAPHY-MASS-SPECTROMETRY; GAS CHROMATOGRAPHY; ORGANIC- MATTER; INTERFACE; FRACTIONS; FOSSIL; RESINS; CHITIN; COALS; GC AB A wide range (16) of synthetic polymers, biological and organic geochemical samples was chosen to compare the performance of filament and Curie-point pyrolysis devices in combination with gas chromatography/mass spectrometry (py-GC/MS). The pyrolysis results were compared qualitatively, quantitatively and using statistical data visualization methods. Multivariate visualization methods showed a good reproducibility between consecutive runs of the same sample. Statistical analyses of processed py-GC/MS data and qualitative comparison of total ion and mass chromatograms revealed a high degree of comparability between the data obtained from the two pyrolysis devices. This investigation constitutes the first systematic comparison of the two most widely used pyrolysis devices and demonstrates that their results can be confidently cross-referenced providing that all other analytical variables, e.g. sample size, GC column stationary phase, carrier gas, etc. are strictly controlled. (C) 1998 Elsevier Science B.V. All rights reserved. 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Anal. Appl. Pyrolysis PY 1998 PD MAY VL 45 IS 2 GA ZT914 PI AMSTERDAM RP van Bergen PF Univ Utrecht, Fac Earth Sci, Organ Geochem Grp, POB 80021, NL-3508 TA Utrecht, Netherlands J9 J ANAL APPL PYROL PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000074140700004 ER PT Book in series AU Naim, O Hey, AJG TI Visualization of do-loop performance SO HIGH-PERFORMANCE COMPUTING AND NETWORKING LA English DT Article NR 23 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Univ Wisconsin, Dept Comp Sci, 1210 W Dayton St, Madison, WI 53706 USA Univ Wisconsin, Dept Comp Sci, Madison, WI 53706 USA Univ Southampton, Dept Elect & Comp Sci, Southampton SO17 1BJ, Hants, England ID PARALLEL AB Performance visualization is the use of graphical display techniques for the analysis of performance data in order to improve understanding of complex performance phenomena. Performance visualization systems for parallel programs have been helpful in the past and they are commonly used in order to improve parallel program performance. However, despite the advances that have been made in visualizing scientific data, techniques for visualizing performance of parallel programs remain ad hoc and performance visualization becomes more difficult as the parallel system becomes more complex. The use of scientific visualization tools (e.g. AVS, Application Visualization System) to display performance data is becoming a very powerful alternative to support performance analysis of parallel programs, One advantage of this approach is that no tool development is required and that every feature of the data visualization tool can be used for further data analysis. In this paper the Do-Loop-Surface (DLS) display, an abstract view of the performance of a particular do-loop in a program implemented using AVS, is presented as an example on how a data visualization tool can be used to define new abstract representations of performance, helping the user to analyze complex data potentially generated by a large number of processors. CR *ADV VIS SYST INC, 1992, AVS US GUIDE REL 4 *PAR CORP, 1990, PAR EXPR US GUID BANGALORE P, 1995, PRIVATE ELECT MAIL C BRODLIE K, 1995, NUCL INSTRUM METH A, V354, P104 COUCH AL, 1993, J PARALLEL DISTR COM, V18, P195 CWIK T, 1994, J OPT SOC AM A, V11 DONGARRA J, 1992, 43 LAPACK GROPP W, 1995, USERS GUIDE ANL IBM GROPP W, 1994, USING MPI PORTABLE P HEATH M, 1992, WORKSHOP PERFORMANCE HEATH MT, 1991, IEEE SOFTWARE SEP, P29 LEBLANC TJ, 1990, J PARALLEL DISTR COM, V9, P203 MALONY A, 1992, WORKSHOP PERFORMANCE MILLER BP, 1995, COMPUTER, V28, P37 NAIM O, 1996, CONCURRENCY-PRACT EX, V8, P205 NAIM O, 1994, LECT NOTES COMPUTER, V797, P367 REED D, 1992, PABLO PERFORMANCE AN ROVER D, 1993, P 3 ACM ONR WORKSH P, P53 ROVER DT, 1993, J PARALLEL DISTR COM, V18, P129 SARUKKAI S, 1992, SCAL HIGH PERF COMP, P424 SIMMONS M, 1990, PERFORMANCE INSTRUME WAHEED A, 1994, INT WORKSHOP MODELIN WAHEED A, 1993, VISUALIZATION 93 SAN TC 0 BP 878 EP 887 PG 10 SE LECTURE NOTES IN COMPUTER SCIENCE PY 1997 VL 1225 GA BK98Z PI BERLIN 33 RP Naim O Univ Wisconsin, Dept Comp Sci, 1210 W Dayton St, Madison, WI 53706 USA J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, W-1000 BERLIN 33, GERMANY UT ISI:000074015300086 ER PT Journal AU Yin, M Wade, TD Lawler-Heavner, J Ruttenber, AJ TI Computer-generated time lines for visualizing and editing epidemiological and biomedical data SO COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE LA English DT Article NR 13 SN 0169-2607 PU ELSEVIER SCI IRELAND LTD C1 Univ Colorado, Hlth Sci Ctr, Sch Med, Dept Prevent Med & Biometr, 4200 E 9th Ave,POB C-245, Denver, CO 80262 USA Univ Colorado, Hlth Sci Ctr, Sch Med, Dept Prevent Med & Biometr, Denver, CO 80262 USA DE time line; data visualization; data editing ID RELIABILITY AB Most biomedical data have a temporal dimension. Time-line displays spatialize this dimension and help the viewer comprehend large sets of complex data. If we add ways for users to selectively expand the details of data visible on a time line, even more information can be organized and accessed. Design issues for this kind of display include: how to display time scales that are often wider than the physical display space; how to display events with brief duration; how to display data for two or more events that overlap in time; how to manage the display of data details. how to allow database editing from a time line; and how to facilitate time-based analytical techniques. We describe a time-line display system that addresses each of these issues, and show how it can be used to organize data for an epidemiological study of parental radiation exposure and childhood leukemia. We also suggest further refinements of the time line technique for other biomedical applications. (C) 1998 Elsevier Science Ireland Ltd. All rights reserved. CR BOND GG, 1988, AM J EPIDEMIOL, V128, P343 BRADBURN NM, 1987, SCIENCE, V236, P157 COUSINS SB, 1991, ARTIF INTELL MED, V3, P341 FIDLER AT, 1987, BRIT J IND MED, V44, P133 FRIEDENREICH CM, 1995, EPIDEMIOLOGY, V5, P1 JOBE JB, 1989, AM J PUBLIC HEALTH, V79, P1053 KAHN MG, 1991, MED DECIS MAKING, V11, P249 KELLER PR, 1993, VISUAL CUES PRACTICA ROBERTSON GG, 1993, COMMUN ACM, V36, P57 SKINNER HA, 1982, J STUD ALCOHOL, V43, P1157 SOBELL LC, 1979, BEHAV RES THER, V17, P157 STANTON MD, 1992, J MARITAL FAM THER, V18, P331 WHITBOURNE SK, 1985, INT J AGING HUM DEV, V22, P147 TC 1 BP 23 EP 29 PG 7 JI Comput. Meth. Programs Biomed. PY 1998 PD APR VL 56 IS 1 GA ZN591 PI CLARE RP Ruttenber AJ Univ Colorado, Hlth Sci Ctr, Sch Med, Dept Prevent Med & Biometr, 4200 E 9th Ave,POB C-245, Denver, CO 80262 USA J9 COMPUT METHOD PROGRAM BIOMED PA CUSTOMER RELATIONS MANAGER, BAY 15, SHANNON INDUSTRIAL ESTATE CO, CLARE, IRELAND UT ISI:000073661200003 ER PT Journal AU Swayne, DF Buja, A TI Missing data in interactive high-dimensional data visualization SO COMPUTATIONAL STATISTICS LA English DT Article NR 7 SN 0943-4062 PU PHYSICA VERLAG GMBH C1 BELLCORE, Room 1A-316B,445 S St, Morristown, NJ 07960 USA BELLCORE, Morristown, NJ 07960 USA AT&T Bell Labs, Murray Hill, NJ 07974 USA DE missing values; imputation of missing values; data visualization; statistical graphics; interactive graphics; dynamic graphics; linked views; brushing; data rotations; grand tours AB We describe techniques for the interactive exploratory analysis of multivariate data with missing values. The approach is to 1) provide trivial imputations such as fixed values, 2) accept multiple imputations computed elsewhere, and 3) provide a means for keeping track of the location of missing values in the data. The techniques have two major uses: First, they support the exploration of missing values, their correlations across variables and their associations with the variables of interest. Second, the techniques support the investigation and comparison of precomputed imputation schemes; in particular, they can be used to informally diagnose the adequacy of imputations. The techniques are illustrated with an implementation in the XGobi software. CR BUJA A, 1996, J COMPUTATIONAL GRAP, V5, P78 CARR DB, 1984, P 5 ANN C EXP COMP G, V2, P743 CESTNIK G, 1987, PROGR MACHINE LEARNI, P31 DIACONIS P, 1983, SCI AM, V248 SWAYNE DF, 1990, P 22 S INT SWAYNE DF, 1996, UNPUB XGOBI INTERACT UNWIN AR, 1996, J COMPUTATIONAL GRAP, V5, P113 TC 2 BP 15 EP 26 PG 12 JI Comput. Stat. PY 1998 VL 13 IS 1 GA ZP118 PI HEIDELBERG RP Swayne DF BELLCORE, Room 1A-316B,445 S St, Morristown, NJ 07960 USA J9 COMPUTATION STAT PA TIERGARTENSTRASSE 17, 69121 HEIDELBERG, GERMANY UT ISI:000073718200003 ER PT Journal AU Chen, CH Li, KC TI Can SIR be as popular as multiple linear regression? SO STATISTICA SINICA LA English DT Article NR 41 SN 1017-0405 PU STATISTICA SINICA C1 Acad Sinica, Inst Stat Sci, Taipei 115, Taiwan Acad Sinica, Inst Stat Sci, Taipei 115, Taiwan Univ Calif Los Angeles, Dept Math, Los Angeles, CA 90024 USA DE dimension reduction; dynamic graphics; inverse regression; projection pursuit; transformation ID SLICED INVERSE REGRESSION; PROJECTION PURSUIT REGRESSION; DIMENSION REDUCTION; DATA VISUALIZATION; ASYMPTOTICS; LINK; TRANSFORMATIONS; SQUARES AB Despite its limitation in exploring nonlinear structures, multiple linear regression (MLR) still retains its popularity among the practitioners. This is mainly because of the several seemingly irreplaceable features of MLR that users are accustomed to, including : (i) it is easy to implement; (ii) it has a solid theoretical foundation; (iii) diagnostic tools are available for model checking; (iv) standard errors are available for significance assessment; (v) output is easy to interpret. Whether such advantages can be maintained or not is an important issue in developing new nonlinear methods for high dimension regression. This issue is studied for one of the recently proposed methods, sliced inverse regression (SIR). We show how to enhance the SIR analysis so that these features can be maintained. CR BICKEL PJ, 1981, J AM STAT ASSOC, V76, P296 BOX GEP, 1964, J ROY STAT SOC B MET, V26, P211 BREIMAN L, 1984, CLASSIFICATION REGRE BREIMAN L, 1985, J AM STAT ASSOC, V80, P580 BRILLINGER DR, 1977, BIOMETRIKA, V64, P509 BRILLINGER DR, 1983, FESTSCHRIFT EL LEHMA, P97 BRILLINGER DR, 1991, J AM STAT ASSOC, V86, P333 CARROLL RJ, 1992, J AM STAT ASSOC, V87, P1040 CARROLL RJ, 1988, J AM STAT ASSOC, V83, P1045 CARROLL RJ, 1988, TRANSFORMATION WEIGH CHAUDHURI P, 1994, STAT SINICA, V4, P143 CHEN H, 1991, ANN STAT, V19, P142 COOK RD, 1994, J AM STAT ASSOC, V89, P177 COOK RD, 1994, J AM STAT ASSOC, V89, P592 COOK RD, 1992, J AM STAT ASSOC, V87, P419 COOK RD, 1991, J AM STAT ASSOC, V86, P328 COOK RD, 1994, TEST, V2, P33 DELEEUW J, 1981, DATA ANAL INFORMATIC, V3, P415 DIACONIS P, 1984, ANN STAT, V12, P793 DUAN N, 1991, ANN STAT, V19, P505 FRIEDMAN JH, 1990, ANN STAT, V19, P1 FRIEDMAN JH, 1981, J AM STAT ASSOC, V76, P817 GIFI A, 1990, NONLINEAR MULTIVARIA HALL P, 1993, ANN STAT, V21, P867 HALL P, 1989, ANN STAT, V17, P573 HARRISON D, 1978, J ENVIRON ECON MANAG, V5, P81 HINKLEY DV, 1984, J AM STAT ASSOC, V79, P302 HSING TL, 1992, ANN STAT, V20, P1040 HUBER PJ, 1985, ANN STAT, V13, P435 KNICKERBOCKER RK, 1992, UNPUB DIMENSION REDU KOYAK RA, 1987, ANN STAT, V15, P1215 LI KC, 1997, ANN STAT, V57, P577 LI KC, 1989, ANN STAT, V17, P1009 LI KC, 1992, J AM STAT ASSOC, V87, P1025 LI KC, 1991, J AM STAT ASSOC, V86, P316 LI KC, 1992, PROBABILITY STAT, P138 LI KC, 1990, UCLA STAT SER, V24 SCHOTT JR, 1994, J AM STAT ASSOC, V89, P141 TIERNEY L, 1990, LISP STAT OBJECT ORI ZHU LX, 1996, ANN STAT, V24, P1053 ZHU LX, 1995, STAT SINICA, V5, P727 TC 6 BP 289 EP 316 PG 28 JI Stat. Sin. PY 1998 PD APR VL 8 IS 2 GA ZK690 PI TAIPEI RP Chen CH Acad Sinica, Inst Stat Sci, Taipei 115, Taiwan J9 STAT SINICA PA C/O DR H C HO, INST STATISTICAL SCIENCE, ACADEMIA SINICA, TAIPEI 115, TAIWAN UT ISI:000073351400002 ER PT Journal AU Swayne, DF Cook, D Buja, A TI XGobi: Interactive dynamic data visualization in the X Window System SO JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS LA English DT Article NR 18 SN 1061-8600 PU AMER STATISTICAL ASSOC C1 Iowa State Univ Sci & Technol, Dept Stat, Ames, IA 50011 USA AT&T Bell Labs, Res, Florham Park, NJ 07932 USA DE brushing; data rotations; data visualization; dynamic graphics; grand tours; interactive graphics; linked views; statistical graphics; parallel coordinate displays; projection pursuit AB XGobi is a data visualization system with state-of-the-art interactive and dynamic methods for the manipulation of views of data. It implements 2-D displays of projections of points and lines in high-dimensional spaces, as well as parallel coordinate displays and textual views thereof. Projection tools include dotplots of single variables, plots of pairs of variables, 3-D data rotations, various grand tours, and interactive projection pursuit. Views of the data can be reshaped. Points can be labeled and brushed with glyphs and colors. Lines can be edited and colored. Several XGobi processes can be run simultaneously and linked for labeling, brushing, and sharing of projections. Missing data are accommodated and their patterns can be examined; multiple imputations can be given to XGobi for rapid visual diagnostics. XGobi includes an extensive online help facility. XGobi can be integrated in other software systems, as has been done for the data analysis language S, the geographic information system (GIS) ArcView(TM), and the interactive multidimensional scaling program XGvis. 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PY 1998 PD MAR VL 7 IS 1 GA ZH295 PI ALEXANDRIA RP Iowa State Univ Sci & Technol, Dept Stat, Ames, IA 50011 USA J9 J COMPUT GRAPH STAT PA 1429 DUKE ST, ALEXANDRIA, VA 22314 USA UT ISI:000073093000007 ER PT Journal AU Bishop, CM Tipping, ME TI A hierarchical latent variable model for data visualization SO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE LA English DT Article NR 18 SN 0162-8828 PU IEEE COMPUTER SOC C1 Microsoft Res, St George House,1 Guildhall St, Cambridge CB2 3NH, England Microsoft Res, Cambridge CB2 3NH, England Aston Univ, Neural Comp Res Grp, Birmingham B4 7ET, W Midlands, England DE latent variables; data visualization; EM algorithm; hierarchical mixture model; density estimation; principal component analysis; factor analysis; maximum likelihood; clustering; statistics ID EM ALGORITHM AB Visualization has proven to be a powerful and widely-applicable tool for the analysis and interpretation of multivariate data. Most visualization algorithms aim to find a projection from the data space down to a two-dimensional visualization space. However, for complex data sets living in a high-dimensional space, it is unlikely that a single two-dimensional projection can reveal all of the interesting structure. We therefore introduce a hierarchical visualization algorithm which allows the complete data set to be visualized at the top level, with clusters and subclusters of data points visualized at deeper levels. The algorithm is based on a hierarchical mixture of latent variable models, whose parameters are estimated using the expectation-maximization algorithm. We demonstrate the principle of the approach on a toy data set, and we then apply the algorithm to the visualization of a synthetic data set in 12 dimensions obtained from a simulation of multiphase flows in oil pipelines, and to data in 36 dimensions derived from satellite images. A Matlab software implementation of the algorithm is publicly available from the World Wide Web. CR BISHOP CM, 1998, NEURAL COMPUT, V10, P215 BISHOP CM, 1995, NEURAL NETWORKS PATT BISHOP CM, 1993, NUCL INSTRUM METH A, P580 BUJA A, 1996, J COMPUTATIONAL GRAP, V5, P78 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 EVERITT BS, 1984, INTRO LATENT VARIABL FRIEDMAN JH, 1974, IEEE T COMPUT, V23, P881 JORDAN MI, 1994, NEURAL COMPUT, V6, P181 KOHONEN T, 1995, SELF ORG MAPS KRZANOWSKI WJ, 1994, MULTIVARIATE ANAL 2 MALTSON RL, 1965, IBM J, V9, P294 MCCULLAGH P, 1989, GEN LINEAR MODELS MICHIE D, 1994, MACHINE LEARNING NEU MIIKULAINEN R, 1990, CONNECTION SCI, V2, P80 RUBIN DB, 1982, PSYCHOMETRIKA, V47, P69 TIPPING ME, 1997, NCRG97003 AST U NEUR TIPPING ME, 1997, P IEE 5 INT C ART NE, P13 VERSINO C, 1996, LECT NOTES COMPUTER, V1112, P221 TC 17 BP 281 EP 293 PG 13 JI IEEE Trans. Pattern Anal. Mach. Intell. PY 1998 PD MAR VL 20 IS 3 GA ZH156 PI LOS ALAMITOS RP Bishop CM Microsoft Res, St George House,1 Guildhall St, Cambridge CB2 3NH, England J9 IEEE TRANS PATT ANAL MACH INT PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000073078400005 ER PT Journal AU Helt, GA Lewis, S Loraine, AE Rubin, GM TI BioViews: Java-based tools for genomic data visualization SO GENOME RESEARCH LA English DT Article NR 24 SN 1054-9803 PU COLD SPRING HARBOR LAB PRESS C1 Univ Calif Berkeley, Dept Cell & Mol Biol, BDGP, Berkeley, CA 94720 USA Univ Calif Berkeley, Dept Cell & Mol Biol, BDGP, Berkeley, CA 94720 USA ID DROSOPHILA; MAP AB Visualization tools for bioinformatics ideally should provide universal access to the most current data in an interactive and intuitive graphical user interface. Since the introduction of lava, a language designed for distributed programming over the Web, the technology now exists to build a genomic data visualization tool that meets these requirements. Using lava we have developed a prototype genome browser applet (BioViews) that incorporates a three-level graphical view of genomic data: a physical map, an annotated sequence map, and a DNA sequence display. Annotated biological features are displayed on the physical and sequence-based maps, and the different views are interconnected. The applet is linked to several databases and can retrieve features and display hyperlinked textual data on selected features. In addition to browsing genomic data, different types of analyses can be performed interactively and the results of these analyses visualized alongside prior annotations. Our genome browser is built on top of extensible, reusable graphic components specifically designed for bioinformatics. Other groups can (and do) reuse this work in various ways. Genome centers can reuse large parts of the genome browser with minor modifications, bioinformatics groups working on sequence analysis can reuse components to build Front ends for analysis programs, and biology laboratories can reuse components to publish results as dynamic Web documents. 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PY 1998 PD MAR VL 8 IS 3 GA ZE853 PI PLAINVIEW RP Rubin GM Univ Calif Berkeley, Dept Cell & Mol Biol, BDGP, Berkeley, CA 94720 USA J9 GENOME RES PA 1 BUNGTOWN RD, PLAINVIEW, NY 11724 USA UT ISI:000072838200017 ER PT Journal AU Wolkenstein, MG Hutter, H Grasserbauer, M TI Visualization of n-dimensional analytical data on personal computers SO TRAC-TRENDS IN ANALYTICAL CHEMISTRY LA English DT Article NR 7 SN 0165-9936 PU ELSEVIER SCIENCE BV C1 Vienna Univ Technol, Inst Analyt Chem, Getreidemarkt 9-151, A- 1060 Vienna, Austria Vienna Univ Technol, Inst Analyt Chem, A-1060 Vienna, Austria DE scientific visualization; n-dimensional data; analytical chemistry; visualization toolkit; computer graphics AB Visualization is the task of creating images that allow important features in the data to be discerned much more readily than from the raw data. In this article we describe our efforts towards using three-dimensional graphics for improved visualization caf different data from analytical chemistry. Various available algorithms are used to analyze and visualize data features that are otherwise difficult to grasp, PB selection of case studies is presented to demonstrate the capabilities of data visualization. It is shown that, despite some limitations in computing power, it is perfectly feasible to use standard personal computers for advanced data visualization tasks. (C) 1998 Elsevier Science B.V. CR BRODLIE KW, 1992, SCI VISUALIZATION TE FOLEY JD, 1990, COMPUTER GRAPHICS PR HUTTER H, 1992, MIKROCHIM ACTA, V107, P137 KASPER A, 1994, ANAL CHIM ACTA, V291, P297 LENDL B, IN PRESS ANAL CHEM SCHROEDER W, 1996, VISUALIZATION TOOLKI UPSON C, 1989, IEEE COMPUT GRAPH, V9, P30 TC 1 BP 120 EP 128 PG 9 JI Trac-Trends Anal. Chem. PY 1998 PD MAR VL 17 IS 3 GA ZB632 PI AMSTERDAM RP Wolkenstein MG Vienna Univ Technol, Inst Analyt Chem, Getreidemarkt 9-151, A-1060 Vienna, Austria J9 TRAC-TREND ANAL CHEM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000072492000010 ER PT Journal AU Poulton, MM Birken, RA TI Estimating one-dimensional models from frequency-domain electromagnetic data using modular neural networks SO IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING LA English DT Article NR 34 SN 0196-2892 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 Univ Arizona, Dept Min & Geol Engn, Lab Adv Subsurface Imaging, Tucson, AZ 85721 USA Univ Arizona, Dept Min & Geol Engn, Lab Adv Subsurface Imaging, Tucson, AZ 85721 USA DE conductivity; electromagnetic induction; geophysics; neural networks ID PATTERN-RECOGNITION; PICKING AB An artificial neural network interpretation system is being used to interpret data from a frequency-domain electromagnetic (EM) geophysical system in near real time, The interpretation system integrates 45 separate networks in a data visualization shell, The networks produce interpretations at three different transmitter-receiver (Tx-Rx) separations for half-space and layered-earth interpretations. Modular neural networks (MNN's) were found to be the only paradigm that could successfully perform the layered-earth interpretations. An MNN with 16 inputs, five local experts, each with seven hidden processing elements, and three outputs was trained on 4795 patterns for 200 epochs, For two-layer models with a resistivity contrast greater than 2:1, resistivity estimates had greater than 96% accuracy for the first-layer resistivity, greater than 98% for the second-layer resistivity, and greater than 96% for the thickness of the first layer, If the contrast is less than 2:1, the resistivity accuracies are unaffected but thickness estimates for layers less than 2 m are unreliable, A Tx-Rx separation of 16 m with maximum depth of penetration of 8 m was assumed for the example cited. CR ASHLEY D, 1994, THESIS U ARIZONA TUS BIRKEN RA, P SAGEEP 95, P349 BRIDLE JS, 1990, ADV NEURAL INFORMATI, V2, P211 BROWN M, P SAGEEP 95, P689 CARTABIA G, 1994, SOC EXPLORATION GEOP, P432 CISAR D, P SAGEEP 93, V2, P599 DENNIS JE, 1981, ACM T MATH SOFTWARE, V7, P348 FEI D, 1994, SOC EXPLORATION GEOP, P636 FOSSATI M, 1992, SOC EXPLORATION GEOP, P6 HAYKIN S, 1994, NEURAL NETWORKS COMP HIDALGO H, 1994, IEEE WORLD C NEURAL JACOBS RA, 1991, ADV NEURAL INFORMATI, V3, P767 JACOBS RA, 1991, COGNITIVE SCI, V15, P219 JACOBS RA, 1991, NEURAL COMPUT, V3, P79 MCCORMACK MD, 1993, GEOPHYSICS, V58, P67 MCCORMACK MD, 1991, LEADING EDGE, V10, P11 MCLACHLAN GJ, 1988, MIXTURE MODELS INFER MINIOR DV, P SAGEEP 93, V2, P449 MURAT ME, 1992, GEOPHYS PROSPECT, V40, P587 NOWLAN SJ, 1991, ADV NEURAL INFORMATI, P774 PEARSON WC, 1990, SOC EXPLORATION GEOP, P587 POULTON MM, 1992, GEOPHYSICS, V57, P1534 POULTON MM, 1992, J APPL GEOPHYS, V29, P21 POULTON MM, 1991, SOC EXPLORATION GEOP, P507 RAICHE A, 1991, GEOPHYS J INT, V105, P629 ROTH G, 1994, THESIS U PARIS PARIS RUECKL JG, 1989, J COGNITIVE NEUROSCI, V2, P171 SCHNEIDERBAUER K, 1994, 56 M EUR ASS EXPL GE STERNBERG BK, P SAGEEP 94, V2, P847 SWINIARSKI RW, 1993, P SPIE GROUND SENSIN, V1941, P151 TAYLOR CL, 1990, SOC EXPLORATION GEOP, P591 THOMAS S, 1996, THESIS U ARIZONA TUC WIENER JM, 1981, SOC EXPLORATION GEOP, P285 WINKLER E, 1994, 56 M EUR ASS EXPL GE TC 2 BP 547 EP 555 PG 9 JI IEEE Trans. Geosci. Remote Sensing PY 1998 PD MAR VL 36 IS 2 GA ZC333 PI NEW YORK RP Poulton MM Univ Arizona, Dept Min & Geol Engn, Lab Adv Subsurface Imaging, Tucson, AZ 85721 USA J9 IEEE TRANS GEOSCI REMOT SEN PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000072566800016 ER PT Journal AU Small, H TI A general framework for creating large-scale maps of science in two or three dimensions: The SciViz system SO SCIENTOMETRICS LA English DT Article NR 14 SN 0138-9130 PU ELSEVIER SCIENCE BV C1 Inst Informat Sci, 3501 Market St, Philadelphia, PA 19104 USA Inst Informat Sci, Philadelphia, PA 19104 USA AB Data visualization techniques have opened up new possibilities for science mapping. To exploit this opportunity new methods are needed to position tens of thousands of documents in a single coordinate space. A general framework is described for achieving this goal involving hierarchical clustering, ordination of clusters, and the merging of ordinations into a common coordinate space. The SciViz system is presented as one particular implementation of this framework. CR CALLON M, 1986, MAPPING DYNAMICS SCI, P103 GERSHON N, 1996, P IEEE S INF VIS INF HIGGS P, 1995, SANDIA LAB NEWS JAIN A, 1988, ALGORITHMS CLUSTERIN JOG N, 1995, IFIP 2 6 VISUAL DATA, P1 JOHNSON B, 1991, P IEEE VISUALIZATION, P284 LIN X, 1997, J AM SOC INFORM SCI, V48, P40 SMALL H, 1985, J INFORM SCI, V11, P147 SMALL H, 1995, P ASIS ANN, V32, P118 SMALL H, 1974, SCI STUD, V4, P17 SMALL H, 1997, SCIENTOMETRICS, V38, P275 SMALL H, 1994, SCIENTOMETRICS, V30, P229 SNEATH PH, 1973, NUMERICAL TAXONOMY WISE JA, 1995, P IEEE S INF VIS 95, P51 TC 6 BP 125 EP 133 PG 9 JI Scientometrics PY 1998 PD JAN-FEB VL 41 IS 1-2 GA YU922 PI AMSTERDAM RP Small H Inst Informat Sci, 3501 Market St, Philadelphia, PA 19104 USA J9 SCIENTOMETRICS PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000071770100012 ER PT Journal AU Pack, T TI Visualizing information - Visualization systems data management SO DATABASE LA English DT Article NR 0 SN 0162-4105 PU ONLINE INC TC 2 BP 47 EP 49 PG 3 JI Database PY 1998 PD FEB-MAR VL 21 IS 1 GA YT990 PI WILTON J9 DATABASE PA 462 DANBURY RD, WILTON, CT 06897-2126 USA UT ISI:000071668400010 ER PT Journal AU Jurisica, I Mylopoulos, J Glasgow, J Shapiro, H Casper, RF TI Case-based reasoning in IVF: prediction and knowledge mining SO ARTIFICIAL INTELLIGENCE IN MEDICINE LA English DT Article NR 37 SN 0933-3657 PU ELSEVIER SCIENCE BV C1 Univ Toronto, Dept Comp Sci, Toronto, ON M5S 1A4, Canada Univ Toronto, Dept Comp Sci, Toronto, ON M5S 1A4, Canada Queens Univ, Dept Comp & Informat Sci, Kingston, ON K7L 3N6, Canada Toronto Gen Hosp, Div Reprod Sci, Toronto, ON M5G 2C4, Canada DE case-based reasoning; in vitro fertilization; relevance; similarity; context; prediction; knowledge mining ID INFORMATION-SYSTEMS; FERTILIZATION; RETRIEVAL; PREGNANCY; DATABASE AB In vitro fertilization (IVF) is a medically-assisted reproduction technique, enabling infertile couples to achieve successful pregnancy. Given the unpredictability of the task, we propose to use a case-based reasoning system that exploits past experiences to suggest possible modifications to an NF treatment plan in order to improve overall success rates. Once the system's knowledge base is populated with a sufficient number of past cases, it can be used to explore and discover interesting relationships among data, thereby achieving a form of knowledge mining. The article describes the TA3(IVF) system- a case-based reasoning system which relies on context-based relevance assessment to assist in knowledge visualization, interactive data exploration and discovery in this domain. The system can be used as an advisor to the physician during clinical work and during research to help determine what knowledge sources are relevant for a treatment plan. (C) 1998 Elsevier Science B.V. CR AGRAWAL R, 1993, IEEE T KNOWL DATA EN, V5, P914 BAEKGAARD L, 1995, IEEE T KNOWL DATA EN, V7, P583 BANCILHON F, 1986, KNOWLEDGE BASE MANAG, P165 BAREISS ER, 1988, THESIS U TEXAS BATEMAN BG, 1995, PREDICTION MANAGEMEN BLACKER CM, 1995, PREDICTIVE VALUE SER BUSTILLO M, 1993, FERTIL STERIL, V59, P668 DAVIS OK, 1995, REPRODUCTIVE ENDOCRI, V2, P2319 DEVANBU P, 1991, AUTOMATIC SOFTWARE D FAYYAD U, 1996, ADV KNOWLEDGE DISCOV FREY P, 1991, MACH LEARN, V6 GAASTERLAND T, 1993, P INT C INT COOP INF, P359 GREINER R, 1994, AAAI FALL S SERIES R HADDAD M, 1995, THESIS U VIENNA VIEN HARMAN H, 1976, MODERN FACTOR ANAL HOOVER L, 1995, GYNECOL OBSTET INVES, V40, P151 JURISCA I, 1996, 8 IEEE INT C TOOLS A JURISICA I, 1994, 3 WORKSH AI SOFTW EN JURISICA I, 1996, 5 INT C DAT KNOWL SY JURISICA I, 1995, 51 C AM SOC REPR MED JURISICA I, 1996, AAAI FAIL S FLEX COM JURISICA I, 1996, CAN AL C WORKSH WHAT KOKENY T, 1995, INT J ARTIF INTELL T, V4, P55 LEAKE D, 1996, CASE BASED REASONING MACURA RT, 1995, P ICCBR 95 SES PORT MYLOPOULOS J, 1990, ACM T INFORM SYST, V8, P325 NANGLE B, 1994, ARTIF INTELL MED, V6, P207 ORTEGA J, 1995, J ARTIFICIAL INTELLI, V2, P361 PORTER BW, 1990, ARTIF INTELL, V45, P229 SCHUURMANS D, 1995, COMPUTATIONAL LEARNI SEIFERT CM, 1994, MACH LEARN, V16, P37 SHAHSAVAR N, 1995, ARTIF INTELL MED, V7, P37 SHAVLIK JW, 1991, MACH LEARN, V6, P111 SOO VW, 1994, ARTIF INTELL MED, V6, P249 STEPP RE, 1986, MACHNE LEARNING ARTI, P471 THAGARD P, 1990, ARTIF INTELL, V46, P259 WETTSCHERECK D, 1995, MACH LEARN, V19, P5 TC 7 BP 1 EP 24 PG 24 JI Artif. Intell. Med. PY 1998 PD JAN VL 12 IS 1 GA YU093 PI AMSTERDAM RP Jurisica I Univ Toronto, Dept Comp Sci, Toronto, ON M5S 1A4, Canada J9 ARTIF INTELL MED PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000071681000001 ER PT Journal AU Cuenca, RH Stangel, DE Kelly, SF TI Soil water balance in a boreal forest SO JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES LA English DT Article NR 29 SN 0747-7309 PU AMER GEOPHYSICAL UNION C1 Oregon State Univ, Dept Bioresource Engn, Gilmore Hall 116, Corvallis, OR 97331 USA Oregon State Univ, Dept Bioresource Engn, Corvallis, OR 97331 USA ID HYDRAULIC CONDUCTIVITY; TENSION INFILTROMETERS; UNSATURATED SOILS; HAPEX-MOBILHY; ATMOSPHERE AB Measurements of root zone soil water content and soil hydraulic properties at the flux tower sites were conducted during the course of the Boreal Ecosystem-Atmosphere Study (BOREAS) experiment. Instrumentation included neutron probe, time domain reflectometry (TDR), and tension infiltrometer. Several methods of data visualization were employed to demonstrate fluctuations in soil water content with depth and time during the intensive field campaigns (IFCs). These methods started with two- dimensional plots of soil water content as a function of time and depth and evolved into the construction of three- dimensional soil water prisms. The prisms were constructed by using cubic-spline (vertical z-direction) and linear (horizontal x-direction) interpolations for soil water content and linear interpolations along the time (y direction) axis. The prisms allow for animation along any plane or combination of planes to demonstrate the evolution of soil water conditions due to evapotranspiration, drainage, and precipitation. Soil hydraulic properties were determined at the flux tower sites based on analysis of in situ tension infiltrometer tests and soil water retention data from laboratory analysis of soil cores. Results from this analysis are saturated hydraulic conductivity and fitting parameters for the van Genuchten soil water retention function and Mualem hydraulic conductivity function at each flux tower site. These functions are required for physically based simulation models of soil water dynamics, soil water balance, and the interaction of the soil profile with the atmospheric boundary layer. Examples of cumulative evapotranspiration and drainage calculated from the soil water balance are presented and compared with flux tower measurements. CR ANDRE JC, 1988, ANN GEOPHYS, V6, P477 ANKENY MD, 1988, SOIL SCI SOC AM J, V52, P893 BROOKS RH, 1964, 3 COL STAT U CARSEL RF, 1988, WATER RESOUR RES, V24, P755 CUENCA RH, 1995, EOS T AGU S, V76, PS127 CUENCA RH, 1989, IRRIGATION SYSTEM DE CUENCA RH, 1996, J GEOPHYS RES-ATMOS, V101, P7269 CUENCA RH, 1997, J HYDROL, V188, P224 CUENCA RH, 1988, J IRRIG DRAIN ENG, V114, P644 CUENCA RH, 1991, LAND SURFACE EVAPORA, P287 FUENTES C, 1992, J HYDROL, V134, P117 GARDNER WR, 1958, SOIL SCI, V85, P228 GOUTORBE JP, 1994, ANN GEOPHYS, V12, P53 GOUTORBE JP, 1989, ANN GEOPHYS, V7, P415 HUSSEN AA, 1993, WATER RESOUR RES, V29, P4103 JARVIS NJ, 1995, SOIL SCI SOC AM J, V59, P27 LEIJ FJ, 1996, EPA600R96095 OFF RES MUALEM Y, 1976, WATER RESOUR RES, V12, P513 PARKES ME, 1979, J AGR ENG RES, V24, P87 PERROUX KM, 1988, SOIL SCI SOC AM J, V52, P1205 RAWLS WJ, 1982, T ASAE, V25, P1316 REYNOLDS WD, 1991, SOIL SCI SOC AM J, V55, P633 SELLERS P, 1995, B AM METEOROL SOC, V76, P1549 SELLERS PJ, 1992, J GEOPHYS RES-ATMOSP, V97, P18345 VANGENUCHTEN MT, 1985, ANN GEOPHYS, V3, P615 VANGENUCHTEN MT, 1991, IAGDW12933934 EPA VANGENUCHTEN MT, 1980, SOIL SCI SOC AM J, V44, P892 WARRICK AW, 1992, WATER RESOUR RES, V28, P1319 WOODING RA, 1992, WATER RESOUR RES, V4, P1259 TC 12 BP 29355 EP 29365 PG 11 JI J. Geophys. Res.-Atmos. PY 1997 PD DEC 26 VL 102 IS D24 GA YQ418 PI WASHINGTON RP Kelly SF Oregon State Univ, Dept Bioresource Engn, Gilmore Hall 116, Corvallis, OR 97331 USA J9 J GEOPHYS RES-ATMOS PA 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA UT ISI:000071384900053 ER PT Journal AU Cignoni, P Montani, C Puppo, E Scopigno, R TI Multiresolution representation and visualization of volume data SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS LA English DT Article NR 47 SN 1077-2626 PU IEEE COMPUTER SOC C1 CNR, Ist Elaboraz Informaz, Via S Maria 46, I-56100 Pisa, Italy CNR, Ist Elaboraz Informaz, I-56100 Pisa, Italy CNR, Ist Matemat Appl, I-16132 Genoa, Italy CNR, Ist CNUCE, I-56100 Pisa, Italy DE volume data visualization; multiresolution representation; tetrahedral meshes ID WAVELET AB A system to represent and visualize scalar volume data at multiple resolution is presented. The system is built on a multiresolution model based on tetrahedral meshes with scattered vortices that can be obtained from any initial dataset. The model is built off-line through data simplification techniques, and stored in a compact data structure that supports fast on-line access. The system supports interactive visualization of a representation at an arbitrary level of resolution through isosurface and projective methods. The user can interactively adapt the quality of visualization to requirements ct a specific application task and to the performance of a specific hardware platform. Representations at different resolutions can be used together to further enhance interaction and performance through progressive and multiresolution rendering. CR AGARWAL PK, 1994, P 5 ACM SIAM S DISCR, P24 AGARWAL PK, 1977, P 8 ACM SIMA S DISCR BERTOLOTTO M, 1995, JP 4 INT S SOL MOD S, P153 CIAMPALINI A, 1997, VISUAL COMPUT, V13, P228 CIGNONI P, 1994, COMPUT GRAPH FORUM, V13, P317 CIGNONI P, 1997, IEEE T VIS COMPUT GR, V3, P158 CIGNONI P, 1994, P 1994 S VOL VIS, P19 CIGNONI P, 1997, VISUAL COMPUT, V13, P199 CIGNONI P, 1995, VISUALIZATION SCI CO, P58 COHEN J, 1996, COMP GRAPH P ANN C S, P119 DANSKIN J, 1992, P 1992 WORKSH VOL VI, P91 DEERING M, 1995, SIGGRAPH 95 C P ACM, P13 EDELSBRUNNER H, 1990, COMBINATORICA, V10, P251 FOWLER RJ, 1979, COMPUT GRAPH, V13, P199 GROSSO R, 1997, P IEEE VIS 97 PHOEN GUO BN, 1995, IEEE T VIS COMPUT GR, V1, P291 HAMANN B, 1994, COMPUT AIDED GEOM D, V11, P197 HAMANN B, 1994, COMPUT AIDED GEOM D, V11, P477 HOPPE H, 1996, ANN C SERIES, P99 JOE B, 1991, COMPUT AIDED GEOM D, V8, P123 KALVIN AD, 1996, IEEE COMPUT GRAPH, V16, P64 KAO T, 1991, P AUTOCARTO, V10, P219 LAUR D, 1991, COMPUT GRAPHICS, V25, P285 LEE J, 1991, INT J GEOGR INF SYST, V5, P267 LORENSEN WE, 1987, COMPUT GRAPHICS, V21, P163 MALLAT SG, 1989, IEEE T PATTERN ANAL, V11, P674 MONTANI C, 1994, VISUALIZATION 94 P, P281 MURAKI S, 1995, IEEE T VIS COMPUT GR, V1, P109 NEUBAUER R, 1997, P VIS SCI COMP 97 PANG A, 1994, IEEE COMPUT GRAPH, V14, P57 PREPARATA FP, 1985, COMPUTATIONAL GEOMET PUPPO E, 1997, C9712 CNUCE CNR PUPPO E, 1996, P 8 CAN C COMP GEOM, P202 RENZE KJ, 1996, IEEE COMPUT GRAPH, V16, P24 RUPPERT J, 1989, P 5 ANN ACM S COMP G, P380 SAMET H, 1990, DESIGN ANAL SPATIAL SCHROEDER WJ, 1992, COMPUT GRAPHICS, V26, P65 SHIRLEY P, 1990, COMPUT GRAPHICS, V24, P63 WESTERMANN R, 1994, P 1994 S VOL VIS OCT, P51 WILHELMS J, 1992, ACM T GRAPHIC, V11, P201 WILHELMS J, 1994, P 1994 S VOL VIS, P27 WILHELMS J, 1993, VISUAL COMPUT, V9, P450 WILLIAMS PL, 1992, ACM T GRAPHIC, V11, P103 WILLIAMS PL, 1992, VIS 92 P, P37 YAGEL R, 1996, P 1996 S VOL VIS OCT, P55 ZHAO TC, 1995, 95 UTR U DEP COMP SC ZHOU Y, 1997, IN PRESS IEEE VIS 97 TC 7 BP 352 EP 369 PG 18 JI IEEE Trans. Vis. Comput. Graph. PY 1997 PD OCT-DEC VL 3 IS 4 GA YP676 PI LOS ALAMITOS RP Cignoni P CNR, Ist Elaboraz Informaz, Via S Maria 46, I-56100 Pisa, Italy J9 IEEE TRANS VISUAL COMPUT GR PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000071302800007 ER PT Journal AU Beshers, C Feiner, S TI Generating efficient virtual worlds for visualization using partial evaluation and dynamic compilation SO ACM SIGPLAN NOTICES LA English DT Article NR 21 SN 0362-1340 PU ASSOC COMPUTING MACHINERY C1 Columbia Univ, Dept Comp Sci, 500 W 120th St,450 CS Bldg, New York, NY 10027 USA Columbia Univ, Dept Comp Sci, New York, NY 10027 USA DE virtual worlds; multivariate data visualization; dataflow; partial evaluation; program transformation ID DESIGN; PRESENTATIONS AB We argue that runtime program transformation, partial evaluation, and dynamic compilation are essential tools for automated generation of flexible, highly interactive graphical interfaces. in particular, these techniques help bridge the gap between a high-level, functional description and an efficient implementation. To support our claim, we describe our application of these techniques to a functional implementation of n-Vision, a real-time visualization system that represents multivariate relations as nested 3D interactors, and to Auto Visual, a rule-based system that designs n-Vision visualizations from high-level task specifications, n-Vision visualizations are specified using a simple functional language. These programs are transformed into a cached dataflow graph. A partial evaluator is used on particular computation- intensive function applications, and the results are compiled to native code. The functional representation simplifies generation of correct code, and the program transformations ensure good performance. We demonstrate why these transformations improve performance and why they cannot be done at compile time. CR ANDRE E, 1993, INTELLIGENT MULTIMED, P75 BERLIN A, 1990, CSLTR90422 MIT ART I BESHERS C, 1993, IEEE COMPUT GRAPH, V13, P41 BESHERS C, 1989, P ACM SIGGRAPH US IN, P76 CASNER SM, 1991, ACM T GRAPHIC, V10, P111 FEINER SK, 1993, INTELLIGENT MULTIMED, P117 FOLEY J, 1996, COMPUTER GRAPHICS PR GALLESIO E, 1996, LNCS, V1049, P137 GLUCK R, 1995, LECT NOTES COMPUT SC, V982, P259 JONES ND, 1996, ACM COMPUT SURV, V28, P480 JONES ND, 1993, PARTIAL EVALUATION A LEONE M, 1994, P WORKSH PART EV SEM, P97 MACKINLAY J, 1986, ACM T GRAPHIC, V5, P110 MASSALIN H, 1992, THESIS COLUMBIA U OUSTERHOUT JK, 1994, TCL TK TOOLKIT ROTH SF, 1990, P CHI 90, P193 SELIGMANN D, 1991, COMPUT GRAPH, V25, P123 SHNEIDERMAN B, 1983, IEEE COMPUT, V16, P57 THIEMANN PJ, 1996, P ACM SIGPLAN INT C, P180 TYSON HR, 1988, P ACM SIGGRAPH S US, P167 ZHOU M, 1996, P INFOVIS 96 IEEE S, P13 TC 0 BP 107 EP 115 PG 9 JI ACM Sigplan Not. PY 1997 PD DEC VL 32 IS 12 GA YM085 PI NEW YORK RP Beshers C Columbia Univ, Dept Comp Sci, 500 W 120th St,450 CS Bldg, New York, NY 10027 USA J9 ACM SIGPLAN NOTICES PA 1515 BROADWAY, NEW YORK, NY 10036 USA UT ISI:000071026500010 ER PT Journal AU Cook, D Buja, A TI Manual controls for high-dimensional data projections SO JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS LA English DT Article NR 23 SN 1061-8600 PU AMER STATISTICAL ASSOC C1 IOWA STATE UNIV SCI & TECHNOL,DEPT STAT,AMES,IA 50011 DE data visualization; dynamic graphics; grand tour; multivariate analysis; projection pursuit ID PURSUIT AB Projections of high-dimensional data onto low-dimensional subspaces provide insightful views for understanding multivariate relationships. This article discusses how to manually control the variable contributions to the projection. The user has control of the way a particular variable contributes to the viewed projection and can interactively adjust the variable's contribution. These manual controls complement the automatic views provided by a grand tour, or a guided tour, and give greatly improved flexibility to data analysts. CR ANDREWS DF, 1985, DATA COLLECTION PROB ASIMOV D, 1985, SIAM J SCI STAT COMP, V6, P128 BRYANT PG, 1995, PRACTICAL DATA ANAL BUJA A, 1988, DYNAMIC GRAPHICS STA, P277 BUJA A, IN PRESS J COMPUTATI BUJA A, 1996, J COMPUTATIONAL GRAP, V5, P78 CARR BD, 1996, 129 G MAS U CTR COMP COOK D, 1995, J COMPUTATIONAL GRAP, V4, P155 DIACONIS P, 1984, ANN STAT, V12, P793 DUFFIN KL, 1994, P VISUALIZATION 94, P205 FISHEKELLER M, 1974, SLACPUB1408 FOLEY JD, 1990, COMPUTER GRAPHICS PR FRIEDMAN JH, 1987, J AM STAT ASSOC, V82, P249 HARDLE W, 1995, XPLORE INTERACTIVE S HUBER PJ, 1985, ANN STAT, V13, P435 HURLEY C, 1990, SIAM J SCI STAT COMP, V11, P1193 JONES MC, 1987, J ROY STAT SOC A GEN, V150, P1 MORTON SC, 1989, 106 STANF U LAB COMP SCOTT DW, 1995, ASA P SECT STAT GRAP, P28 SWAYNE DF, IN PRESS J COMPUTATI TIERNEY L, 1991, LISPSTAT OBJECT ORIE UNWIN AR, 1988, ASA P SECTION STAT G, P263 WEGMAN EJ, 1991, 68 G MAS U CTR COMP TC 4 BP 464 EP 480 PG 17 JI J. Comput. Graph. Stat. PY 1997 PD DEC VL 6 IS 4 GA YK837 PI ALEXANDRIA RP Cook D IOWA STATE UNIV SCI & TECHNOL,DEPT STAT,AMES,IA 50011 J9 J COMPUT GRAPH STAT PA 1429 DUKE ST, ALEXANDRIA, VA 22314 UT ISI:A1997YK83700007 ER PT Journal AU Park, S Lee, K TI High-dimensional trivariate NURBS representation for analyzing and visualizing fluid flow data SO COMPUTERS & GRAPHICS LA English DT Article NR 17 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 SEOUL NATL UNIV,DEPT MECH DESIGN & PROD ENGN,KWANAK GU,SAN 56-1 SHINLIM DONG,SEOUL 151742,SOUTH KOREA SAMSUNG SDS CO LTD,INFORMAT TECHNOL R&D CTR,PROD DEV TEAM,SEOUL 135080,SOUTH KOREA AB Scientific data visualization addresses the representation, manipulation, and rendering of volumetric data sets, providing mechanisms for looking closely into structures and understanding their complexity and dynamics. In the past several years, a tremendous amount of research and development has been directed toward algorithms and data modeling methods for a scientific data visualization. But there has been very little work on developing a mathematical representation of a volumetric data that feeds this visualization. Especially, in flow visualization, the Volumetric model has long been demanded as a foundation for the display of very large amounts of data resulting from numerical simulations. In this paper, we focus on the mathematical representation of volumetric data sets and the feature segmentation of meaningful information from the derived volumetric model. For this purpose, a well-known B- spline volume is extended to a high-dimensional trivariate model in this paper. Several three-dimensional examples are presented to demonstrate the capabilities of this suggested model. (C) 1997 Elsevier Science Ltd. CR BUNING PG, 1985, AIAA 7 CFC C CINC OH, P162 CASALE MS, 1985, IEEE COMPUT GRAPH, V5, P45 DELEEUW WC, 1993, P VISUALIZATION 93, P39 DELMARCELLE T, 1994, ENG VISUALIZATION DELMARCELLE T, 1993, IEEE COMPUT GRAPH, V13, P25 FARIN G, 1990, CURVES SURFACES COMP, P150 GLOBUS A, 1991, P VISUALIZATION 91, P33 HARTLEY PJ, 1980, COMPUT AIDED DESIGN, V12, P235 HELMAN JL, 1991, IEEE COMPUT GRAPH, V11, P36 HESSELINK L, 1994, FRONTIERS SCI VISUAL HULTQUIST JPM, 1992, P VISUALIZATION 92, P171 KENWRIGHT D, 1992, P VISUALIZATION 92 B, P62 KERLICK GD, 1990, P VISUALIZATION 90, P124 LASSER D, 1985, COMPUT AIDED GEOM D, V2, P145 LASSER D, 1993, FUNDAMENTALS COMPUTE SILVER D, 1993, J VISUAL COMMUNICATI, V4, P46 TILLER W, 1983, IEEE COMPUT GRAPH, V3, P61 TC 0 BP 473 EP 482 PG 10 JI Comput. Graph. PY 1997 PD JUL-AUG VL 21 IS 4 GA YJ538 PI OXFORD RP Park S SEOUL NATL UNIV,DEPT MECH DESIGN & PROD ENGN,KWANAK GU,SAN 56-1 SHINLIM DONG,SEOUL 151742,SOUTH KOREA J9 COMPUT GRAPH PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1997YJ53800011 ER PT Journal AU Ki, B Klasky, S TI Collaborative scientific data visualization SO CONCURRENCY-PRACTICE AND EXPERIENCE LA English DT Article NR 8 SN 1040-3108 PU JOHN WILEY & SONS LTD C1 SYRACUSE UNIV,NE PARALLEL ARCHITECTURES CTR,SYRACUSE,NY 13244 AB We have designed a collaborative scientific visualization package that will aid researchers from distant, diverse locations to work together in developing scientific codes, providing them with a system to analyze their scientific data, We have utilized Java to develop this infrastructure. Two important areas which we have concentrated on developing ape (i) a collaborative framework from which the scientific data are interpreted and utilized, and (ii) a framework which is customizable to suit the needs of a particular task and/or scientific group, (C) 1997 John Wiley & Sons, Ltd. CR *NCSA, HAB DEV GUID *SUN MICR, JAV 3D API WHIT PAP *SUN MICR, JAV LANG WHIT PAP *SUN MICR, OBJ SER SPEC BECA L, TANGO COLLABORATIVE CHANDY KM, CALTECH INFOSPHERES KVANDE BJ, 1996, JAVA COLLABORATOR TO ORFALI R, 1997, CLIENT SERVER PROGRA TC 0 BP 1249 EP 1259 PG 11 JI Concurrency-Pract. Exp. PY 1997 PD NOV VL 9 IS 11 GA YJ185 PI W SUSSEX RP SYRACUSE UNIV,NE PARALLEL ARCHITECTURES CTR,SYRACUSE,NY 13244 J9 CONCURRENCY-PRACT EXPER PA BAFFINS LANE CHICHESTER, W SUSSEX, ENGLAND PO19 1UD UT ISI:A1997YJ18500022 ER PT Journal AU Andrienko, GL Andrienko, NV TI Intelligent cartographic visualization for supporting data exploration in the IRIS system SO PROGRAMMING AND COMPUTER SOFTWARE LA English DT Article NR 36 SN 0361-7688 PU MAIK NAUKA/INTERPERIODICA C1 RUSSIAN ACAD SCI,INST MATH PROBLEMS BIOL,PUSHCHINO 142292,MOSCOW OBLAST,RUSSIA DE visualization; geographical information systems (GIS); artificial intelligence systems; data exploration; Internet; WWW; Java programming ID INFORMATION; DESIGN; PRESENTATIONS AB Systems of intelligent data visualization (IDV) that are currently available are able to construct high-quality graphical presentations that adequately take into account the data characteristics. However, these systems do not take account of the objectives of the presentation. Explorative and communicative goals should be distinguished, since the principles of graphics construction are different for these groups of tasks. In systems designed for visualization of territorially related data, peculiarities and restrictions of the cartographic presentation must be taken into account. The IRIS system is designed to support spatial data exploration by means of intelligent cartographic visualization. It provides an opportunity to interactively manipulate graphical presentations and data. The system is implemented under MS Windows and for use on the Internet. CR AHLBERG C, 1992, P ACM CHI INT C HUM, P619 ANDRIENKO GL, 1995, IZV ROSS AKAD NAUK T, P160 ANDRIENKO GL, 1994, LECT NOTES ARTIFICIA, V867, P244 ANDRIENKO GL, 1996, PROGRAMMIROVANIE, P17 BERNSTEIN B, 1992, CUCS59692 U COL BERTIN J, 1983, SEMIOLOGY GRAPHICS D CASNER SM, 1991, ACM T GRAPHIC, V10, P111 CLEVELAND WS, 1986, INT J MAN MACH STUD, V25, P491 CLEVELAND WS, 1985, J AM STA ASS, V797, P531 DORLING D, 1994, VISUALIZATION GEOGRA, P85 FAYYAD UM, 1996, IEEE INTELL SYST APP, V11, P20 GROSSLEY D, 1996, WORLD WIDE WEB J, P723 HEARNSHAW HM, 1994, VISUALIZATION GEOGRA JUNG V, 1995, P 3 ACM INT WORKSH A, P101 KEIM DA, 1994, IEEE COMPUT GRAPH, V14, P40 KOSSLYN SM, 1989, APPL COGNITIVE PSYCH, V3, P185 KOSSLYN SM, 1994, ELEMENTS GRAPH DESIG KOSSLYN SM, 1985, J AM STAT ASSOC, V80, P499 KRAAK MJ, 1996, IEEE MULTIMEDIA, V3, P59 LARKIN JH, 1987, COGNITIVE SCI, V11, P65 LEE HY, 1996, IEEE EXPERT, V11, P69 MACKINLAY J, 1986, ACM T GRAPHIC, V5, P110 MITTAL VO, 1995, P INT JOINT C ART IN, V2, P1276 MONMONIER MS, 1991, HOW LIE MAPS MURRAY BS, 1994, KNOWL ENG REV, V9, P269 ROBINSON AH, 1995, ELEMENTS CARTOGRAPHY ROTH SF, 1990, P CHI 90, P193 SCHMID CE, 1983, STAT GRAPHICS DESIGN SENAY H, 1994, IEEE COMPUT GRAPH, V14, P36 SMITH TR, 1996, IEEE COMPUT, V29, P54 TUFTE ER, 1990, ENVISIONING INFORMAT TUFTE ER, 1983, VISUAL DISPLAY QUANT TUKEY JW, 1977, EXPLORATORY DATA ANA WROBEL S, 1996, P KDD 96 2 INT C KNO, P214 ZENKIN AA, 1992, KOGNITIVNAYA COMPYUT ZHAN FB, 1995, INT J GEOGR INF SYST, V9, P293 TC 2 BP 268 EP 281 PG 14 JI Program. Comput. Softw. PY 1997 PD SEP-OCT VL 23 IS 5 GA YA994 PI NEW YORK RP RUSSIAN ACAD SCI,INST MATH PROBLEMS BIOL,PUSHCHINO 142292,MOSCOW OBLAST,RUSSIA J9 PROGRAM COMPUT SOFT-ENGL TR PA C/O PLENUM/CONSULTANTS BUREAU 233 SPRING ST, NEW YORK, NY 10013 UT ISI:A1997YA99400005 ER PT Journal AU Jesse, LA Kalita, JK TI Situation assessment and prediction in intelligence domains SO KNOWLEDGE-BASED SYSTEMS LA English DT Article NR 61 SN 0950-7051 PU ELSEVIER SCIENCE BV C1 UNIV COLORADO,DEPT COMP SCI,COLORADO SPRINGS,CO 80933 DE situation assessment; situation prediction; intelligence analysis ID INTERACTIVE SYSTEMS; ALGORITHM; GRAMMARS; PLANS; TIME AB We discuss a user-friendly system containing an operational suite of tools that support intelligence data processing, data visualization, historical analysis, situation assessment and predictive analysis. The tools facilitate the study of events as a function of time to determine situational patterns. To support this analysis, the system has various data displays (e.g., timelines, maps charts, and tables) a historical event database, query capabilities, and expert system tools. The expert system tools analyze temporal information, predict future events, and explain decisions visually and textually. The tools are currently installed in several military commands and intelligence agencies supporting analysis ranging from strategic C-3 to counter drug. (C) 1997 Elsevier Science B.V. 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PY 1997 PD AUG VL 10 IS 2 GA YA173 PI AMSTERDAM RP UNIV COLORADO,DEPT COMP SCI,COLORADO SPRINGS,CO 80933 J9 KNOWL-BASED SYST PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1997YA17300003 ER PT Journal AU Ojasoo, T Dore, JC Fiet, J Raynaud, JP TI Visualization of data on age-related endocrine and metabolic changes in men SO ENDOCRINOLOGY AND METABOLISM LA English DT Article NR 38 SN 1074-939X PU BAILLIERE TINDALL C1 UNIV PARIS 06,F-75252 PARIS 05,FRANCE MUSEUM NATL HIST NAT,F-75231 PARIS 05,FRANCE CNRS,URA 401,F-75231 PARIS 05,FRANCE HOP ST LOUIS,HORMONE BIOCHEM DEPT,F-75475 PARIS 10,FRANCE ID BENIGN PROSTATIC HYPERPLASIA; SERUM DEHYDROEPIANDROSTERONE- SULFATE; SEX-HORMONES; PLASMA; CANCER; HYPOANDROGENISM; TESTOSTERONE; ANDROGENS; WOMEN AB The thesis put forward is that endocrine and metabolic changes belong to complex dynamic systems that need to be viewed from a wider perspective than is often customary. As an illustration, we analysed the relationships among 10 plasma hormones and metabolites in 60 patients (47-91 years) with stage D2 prostate cancer by Correspondence Analysis (CA). CA, which is based on chi(2)-metrics, analyses bath discrete and categorical variables, linear and non-linear relationships, and depicts table rows and columns on common plots that visualize the latent multidimensional structure of the system under study. The results obtained were coherent with much published data. Half the variance was explained by covariations between LH and FSH, dehydroepiandrosterone (DHEA) and its sulfate (DHEA-S), testosterone and estradiol, androst-4-enedione and cortisol. The changes in DHEA-S were correlated with those in androst-4- enedione but not testosterone. Dihydrotestosterone (DHT) was a highly discriminatory variable especially in the 65-75 age group. Variations in DHEA and DHEA-S were in part negatively correlated with those in LH and FSH and this correlation was largely accounted for by age. In addition, the CA plots suggested that there may be two distinct pituitary driving mechanisms, besides the pituitary-testicular axis, influencing adrenal androgens, one governed by prolactin, the other tay unmeasured factors [e.g. ACTH and/or hormones of the somatotropic axis (GH-insulin-like growth factors)]. We conclude that CA will be a useful tool for visualizing the interrelationships among a much larger number of variables involved in endocrine and metabolic homeostasis. 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Metab. PY 1997 PD SEP VL 4 IS 5 GA YA140 PI LONDON RP UNIV PARIS 06,F-75252 PARIS 05,FRANCE J9 ENDOCRINOL METAB PA 24-28 OVAL RD, LONDON, ENGLAND NW1 7DX UT ISI:A1997YA14000006 ER PT Journal AU Elbinger, SZ Routier, R TI 3D visualization applied to electromagnetic engineering SO NAVAL ENGINEERS JOURNAL LA English DT Article NR 6 SN 0028-1425 PU AMER SOC NAVAL ENG INC C1 USN,SEA SYST COMMAND,COMBAT SYST BRANCH,WASHINGTON,DC 20350 AB Over the past few years, the Naval Sea Systems Command (NavSea) Topside Design Group has conducted extensive development of the electromagnetics code for naval ship topside design. Analysis of computational electromagnetic data has long been a challenging task for surface ship designers. There are a number of very good computational models for analyzing a surface ship's electromagnetic characteristics. Unfortunately, the output of these models consist of huge matrices of real numbers that are very difficult to analyze. This paper sill show how NavSea, in conjunction with Naval Command, Control and Ocean Surveillance Center RDT&E Division (NCCOSC/NRaD) and Rockwell International, is using computer graphics to help surface ship topside designers optimize the electromagnetic characteristics of a ship's design. NavSea has developed 2D and 3D scientific data visualization tools that provide an intuitive ''feel'' for the output of the numeric computational electromagnetic models. This paper will also describe how industrial software standards and fast, inexpensive graphics engines will provide electromagnetic design engineers with much more capable, robust, flexible, portable, and economical scientific visualization tools. CR 1995, RAY TRACING CASTING *GEORG TECH RES I, 1995, SPHER AP AN SHIPB DI *NRAD, 1995, JTUAV SV TOPS EMC AN BARON NT, 1991, 28 ANN TECHN S NAV S LOGAN JC, 1988, ROLE MODEL ELECTROMA WINSTON JA, EM ENG SYSTEM ARCHIT, P2 TC 0 BP 31 EP 45 PG 15 JI Nav. Eng. J. PY 1997 PD SEP VL 109 IS 5 GA XZ256 PI ALEXANDRIA RP Elbinger SZ USN,SEA SYST COMMAND,COMBAT SYST BRANCH,WASHINGTON,DC 20350 J9 NAV ENG J PA 1452 DUKE STREET, ALEXANDRIA, VA 22314-3458 UT ISI:A1997XZ25600007 ER PT Journal AU Xia, TH TI Interactive multidimensional data visualization. SO ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY LA English DT Meeting Abstract NR 0 SN 0065-7727 PU AMER CHEMICAL SOC C1 GLAXO WELLCOME INC,RES TRIANGLE PK,NC 27709 TC 0 BP 199 EP COMP PG 1 JI Abstr. Pap. Am. Chem. Soc. PY 1997 PD SEP 7 VL 214 PN 1 GA XQ857 PI WASHINGTON RP GLAXO WELLCOME INC,RES TRIANGLE PK,NC 27709 J9 ABSTR PAP AMER CHEM SOC PA 1155 16TH ST, NW, WASHINGTON, DC 20036 UT ISI:A1997XQ85701398 ER PT Journal AU Ihlenfeldt, WD TI Virtual reality in chemistry SO JOURNAL OF MOLECULAR MODELING LA English DT Review NR 65 SN 0948-5023 PU SPRINGER VERLAG C1 UNIV ERLANGEN NURNBERG,COM CHEM CTR,NAGELSBACHSTR 25,D-91052 ERLANGEN,GERMANY DE virtual reality; visualization; computer graphics; molecular modeling; databases; data mining; document navigation AB With the advent of ever more powerful computer graphics hardware and visualization packages, new graphical methods of scientific visualization and data exploration are beginning to be explored. This includes fully immersive environments where the chemist is surrounded by data objects in 3D space. New models of animation and interactive manipulation of graphical entities are developed to help the chemist in gaining insight from or navigating in large amounts of data. This review discusses some representative approaches and systems which demonstrate where chemistry-related visualization and data management is headed. 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Mol. Model. PY 1997 VL 3 IS 9 GA XU767 PI NEW YORK RP Ihlenfeldt WD UNIV ERLANGEN NURNBERG,COM CHEM CTR,NAGELSBACHSTR 25,D-91052 ERLANGEN,GERMANY J9 J MOL MODEL PA 175 FIFTH AVE, NEW YORK, NY 10010 UT ISI:A1997XU76700002 ER PT Journal AU Hansen, MH James, DA TI A computing environment for spatial data analysis in the microelectronics industry SO BELL LABS TECHNICAL JOURNAL LA English DT Article NR 13 SN 1089-7089 PU LUCENT TECHNOLOGIES C1 AT&T BELL LABS,STAT & INFORMAT ANAL DEPT,MURRAY HILL,NJ 07974 AB This paper describes a computing environment called S-wafers that is tailored for the analysis of spatial data collected during semiconductor manufacturing processes. At the core of S- wafers lies a new statistical methodology that systematically exploits the basic spatial nature of these data. The S-wafers environment builds on the experience of Lucent Technologies' Microelectronics engineers and Bell Labs researchers who have noticed that patterns in mapped wafer data can provide ''signatures,'' which can be used to help identify and correct process problems. The S-wafers environment provides the means to formalize these observations into implementable strategies by furnishing extensive fools for data visualization; identification of groups of similarly patterned wafers; spatial analysis of designed experiments; assessment of the impact of particle contamination; and much more. In addition to supporting statistical research into problems of process improvement in this area, the S-wafers environment has been used for more than two years by various groups in Microelectronics. Extensions to both the software environment and the underlying statistical methodology continue at a rapid pace. CR *MATHS INC STATSC, 1995, S PLUS SOFTW DOC VER *MICR CORP, 1994, VIS BAS LANG VERS 3 BECKER RA, 1988, NEW S LANGUAGE CHAMBERS JM, 1996, COMPUTING SCI STAT, V28 CHAMBERS JM, 1992, STAT MODELS S DENBY L, 1995, COMPUTING SCI STAT, V27, P212 EVERITT BS, 1994, HDB STAT ANAL USING FRIEDMAN D, 1997, UNPUB IEEE T SEMICON HANSEN MH, 1995, COMPUTING SCI STAT, V27, P3 HANSEN MH, 1997, IN PRESS TECHNOMETRI NAKATANI LH, 1991, FIT PROGRAMMING LANG SPECTOR P, 1994, INTRO S S PLUS VENABLES WN, 1994, MODERN APPL STAT S P TC 2 BP 114 EP 129 PG 16 JI Bell Labs Tech. J. PY 1997 PD WIN VL 2 IS 1 GA XP574 PI MURRAY HILL RP Hansen MH AT&T BELL LABS,STAT & INFORMAT ANAL DEPT,MURRAY HILL,NJ 07974 J9 BELL LABS TECH J PA 600-700 MOUNTAIN AVE RM 3C-412 BELL LABS TECHNICAL JOURNAL, P O BOX 636, MURRAY HILL, NJ 07974-0636 UT ISI:A1997XP57400010 ER PT Journal AU Ehlschlaeger, CR Shortridge, AM Goodchild, MF TI Visualizing spatial data uncertainty using animation SO COMPUTERS & GEOSCIENCES LA English DT Article NR 25 SN 0098-3004 PU PERGAMON-ELSEVIER SCIENCE LTD C1 CUNY HUNTER COLL,DEPT GEOG,NEW YORK,NY 10021 UNIV CALIF SANTA BARBARA,NCGIA,SANTA BARBARA,CA 93106 DE animation; uncertainty; spatial data; digital elevation model; optimal route; random fields ID MODELS; SPACE; TIME AB This paper examines methodologies for dynamically displaying information about uncertainty. Modeling uncertainty in elevation data results in the generation of dozens or hundreds of realizations of the elevation surface. Producing animations of these surfaces is an approach to exploratory data visualization that may assist the researcher in understanding the effect of uncertainty on spatial applications as well as in communicating the results of the research to a wider audience. A nonlinear method for interpolation between the surface realizations is introduced which allows for smooth animation while maintaining the surface characteristics prescribed by the uncertainty model. (C) 1997 Elsevier Science Ltd. CR *USGS, 1996, USGS 7 5 MIN DEM COV BEARD K, 1991, 9126 NCGIA BROWN B, 1992, SG3D SUPPORTING INFO CHURCH RL, 1992, COMPUT GEOSCI, V18, P1095 DETTINGER MD, 1981, WATER RESOUR RES, V17, P149 DIBIASE D, 1990, EARTH MINERAL SCI, V59, P13 DORLING D, 1992, CARTOGR GEOGR INFORM, V19, P215 DORLING D, 1992, ENVIRON PLANN B, V19, P639 EHLSCHLAEGER CR, 1994, P GIS LIS 94 PHOEN A, P246 EHLSCHLAEGER CR, 1996, P SPATIAL DATA HANDL, V2 EHLSCHLAEGER CR, 1994, P WORKSH GIS C INF K, P86 EHLSCHLAEGER CR, 1994, UNPUB RANDOM FIELDS EVANS BJ, 1996, CARTOGRAPHIC DISPLAY FISHER PF, 1993, CARTOGRAPHICA, V30, P20 FISHER PF, 1993, INT J GEOGR INF SYST, V7, P331 GOODCHILD MF, 1986, CATMOG 47 GOODCHILD MF, 1992, COMPUTAT GEOSCI, V18, P401 GOODCHILD MF, 1992, INT J GEOGR INF SYST, V6, P87 HEUVELINK GBM, 1989, INT J GEOGR INF SYST, V3, P303 HOOTSMAN R, 1993, P EGIS 1993, P1035 HORN BKP, 1981, P IEEE, V69, P14 MACEACHREN AM, 1991, CARTOGR GEOGR INFORM, V18, P221 OPENSHAW S, 1979, ENVIRON PLANN A, V11, P879 THEOBALD DM, 1989, ACCURACY SPATIAL DAT, P99 VANDERWEL FJM, 1994, VISUALIZATION MODERN, P313 TC 5 BP 387 EP 395 PG 9 JI Comput. Geosci. PY 1997 PD MAY VL 23 IS 4 GA XP416 PI OXFORD RP Ehlschlaeger CR CUNY HUNTER COLL,DEPT GEOG,NEW YORK,NY 10021 J9 COMPUT GEOSCI PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1997XP41600004 ER PT Journal AU Filliben, JJ Li, KC TI A systematic approach to the analysis of complex interaction patterns in two-level factorial designs SO TECHNOMETRICS LA English DT Article NR 14 SN 0040-1706 PU AMER STATISTICAL ASSOC C1 NATL INST STAND & TECHNOL,STAT ENGN DIV,GAITHERSBURG,MD 20899 UNIV CALIF LOS ANGELES,DEPT MATH,LOS ANGELES,CA 90024 DE data visualization; dimension reduction; linear-domain analysis; partition tree; principal Hessian direction; residual analysis; splitting ID DIMENSION REDUCTION; TESTABILITY AB Analysis of data from two-level full factorial designs often ends up with a final prediction equation that gives only the significant main-effect and interaction terms. When the number of interactions is small, simple and useful interpretation of the equation can then be drawn immediately. This article addresses a different situation in which the number of significant interactions may be large so that additional efforts are needed to sort but the pattern and the relationship between them. In particular, we bring out a class of models in which most interactions can be attributed to just one or two (or very few) factors, and conditional on these factors, the models become essentially linear. We offer a strategy for uncovering this structure by linear domain splitting, whereby a complicated global model is replaced by a series of local domain-specific linear models. We present a recommended methodology (PHD-principal Hessian direction) for systematically proceeding from the global equation to local split-domain analyses. The net result is that guided tree- structured paths are offered for visiting the source-of- interaction factors in sequence, which appropriately reflects their relative importance and mutual relationship. The final stage modeling is simpler (linear). The quality of the fit can be assessed separately in each region, and the analyst comes away with greater insight as to the sensitivity and robustness of the various factor effects over various regions. Applications in digital electronics testing are illustrated by analyzing a dataset collected for studying the conversion error of a digital-to-analog converter. CR BOX GEP, 1978, STATISTICS EXPT BOYLESTAD R, 1987, ELECT DEVICES CIRCUI BREIMAN L, 1984, CLASSIFICATION REGRE CHENG CS, 1995, STAT SINICA, V5, P617 KATO T, 1976, PERTURBATION THEORY LI KC, 1990, DATA VISUALIZATION S LI KC, 1992, J AM STAT ASSOC, V87, P1025 LI KC, 1991, J AM STAT ASSOC, V86, P316 LOH WY, 1988, J AM STAT ASSOC, V83, P715 MALLOWS CL, 1973, TECHNOMETRICS, V15, P661 SOUDERS TM, 1985, 1985 IEEE INT TEST C, P813 STENBAKKEN GN, 1989, IEEE T INSTRUM MEAS, V38, P941 STENBAKKEN GN, 1987, IEEE T INSTRUM MEAS, V36, P406 TIERNEY L, 1990, LISP STAT OBJECT ORI TC 5 BP 286 EP 297 PG 12 JI Technometrics PY 1997 PD AUG VL 39 IS 3 GA XM272 PI ALEXANDRIA RP Filliben JJ NATL INST STAND & TECHNOL,STAT ENGN DIV,GAITHERSBURG,MD 20899 J9 TECHNOMETRICS PA 1429 DUKE ST, ALEXANDRIA, VA 22314 UT ISI:A1997XM27200005 ER PT Journal AU Fisher, SH Dempsey, JV Marousky, RT TI Data visualization: Preference and use of two-dimensional and three-dimensional graphs SO SOCIAL SCIENCE COMPUTER REVIEW LA English DT Article NR 18 SN 0894-4393 PU SAGE PUBLICATIONS INC C1 UNIV S ALABAMA,DEPT POLIT SCI & CRIMINAL JUSTICE,MOBILE,AL 36688 UNIV S ALABAMA,COLL EDUC,MOBILE,AL 36688 DE data visualization; graphing; charts; datagraphics AB This study considered the interplay of simple versus perspective graphical information on aesthetic preference, instructional effectiveness, and retention. Students in an introductory U.S. government course were presented with examples of 2-D and 3-D graphs and asked to choose which was pleasing to the eye and which was most useful in answering questions about the graph's content. The results of this study indicated that when visual appeal was the only criterion, subject choices overall were approximately evenly matched. When subjects were required to extract information from graphs, they used simple graphs almost 3 times more often than elaborate graphs. information drawn from bar and circle graphs was extracted more accurately than the other three types of graphs. CR *SYSTAT INC, 1992, SYSTAT WIND GRAPH BERTIN J, 1983, SEMIOLOGY GRAPHICS BRASELL HM, 1990, WHAT RES SAYS SCI TE CLEVELAND WS, 1985, ELEMENTS GRAPHING DA COREN S, 1978, SEEING IS DECEIVING CROXTON FE, 1932, J AM STAT ASSOC, V27, P54 GREGORY RL, 1969, EYE BRAIN HARTLEY J, 1987, DESIGNING INSTRUCTIO JAHNEL F, 1952, PICTOGRAPHS GRAPHS M MACDONALDROSS M, 1977, AUDIO VISUAL COMMUNI, V25, P359 MILLER GA, 1956, PSYCHOL REV, V63, P81 NORMAN DA, 1993, THINGS MAKE US SMART SCHMIDT CF, 1983, STAT GRAPHICS DESIGN TUFTE ER, 1983, VISUAL DISPLAY QUANT WAINER H, 1992, EDUC RES, V21, P14 WERTHEIMER M, 1938, LAWS ORG PERCEPTUAL WINN W, 1992, INSTRUCTIONAL MESSAG WURMAN RS, 1990, INFORMATION ANXIETY TC 4 BP 256 EP 263 PG 8 JI Soc. Sci. Comput. Rev. PY 1997 PD FAL VL 15 IS 3 GA XL748 PI THOUSAND OAKS RP Fisher SH UNIV S ALABAMA,DEPT POLIT SCI & CRIMINAL JUSTICE,MOBILE,AL 36688 J9 SOC SCI COMPUT REV PA 2455 TELLER RD, THOUSAND OAKS, CA 91320 UT ISI:A1997XL74800003 ER PT Journal AU Sydow, A Lux, T Mieth, P Schafer, RP TI Simulation of traffic-induced air pollution for mesoscale applications SO MATHEMATICS AND COMPUTERS IN SIMULATION LA English DT Article NR 9 SN 0378-4754 PU ELSEVIER SCIENCE BV C1 GMD,RES INST COMP ARCHITECTURE & SOFTWARE TECHNOL,FIRST,RUDOWER CHAUSSEE 5,D-12489 BERLIN,GERMANY AB Recent investigations have shown that vehicular traffic is the main source for emissions leading to summer smog. A study of the impact of traffic emission on urban air quality requires a complex air-pollution simulation system. This paper presents results of the development and application of an air-pollution simulation system at GMD FIRST which aims at supporting users in government administration and industry with forecasting and operative decision-making as well as short- to long-term regional planning. The components of the simulation system are parallelly implemented simulation models for meteorology, transport and air chemistry, data bases for model input and simulation results, as well as a graphic user interface for spatial data visualization. In order to study the influence of traffic emissions, a traffic-flow and a traffic-emission model have been added to the simulation system. Results presented are from two recent applications in the regions of Berlin/Brandenburg and Munich (Germany). Further applications are in preparation. CR GERHARZ I, 1996, P COMPUTATIONAL ENG GERY MW, 1988, EPA600388012 HEIMANN D, 1985, THESIS U MUNICH GERM KAPITZA H, 1992, BEITR PHYS ATMOS, V65, P129 MIETH P, 1995, SIMULATION OZONPRODU SCHAFER RP, 1996, P COMPUTATIONAL ENG SCHWERDTFEGER T, 1984, P 9 INT S TRANSP TRA SMID K, 1996, GMD SPIEGEL SPECIAL SYDOW A, 1994, 13 WORLD COMP C IFIP TC 0 BP 285 EP 290 PG 6 JI Math. Comput. Simul. PY 1997 PD MAR VL 43 IS 3-6 GA XJ411 PI AMSTERDAM RP Sydow A GMD,RES INST COMP ARCHITECTURE & SOFTWARE TECHNOL,FIRST,RUDOWER CHAUSSEE 5,D-12489 BERLIN,GERMANY J9 MATH COMPUT SIMULAT PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1997XJ41100007 ER PT Journal AU Litvinenko, EI TI Interactive data analysis for neutron spectrometers data based on Visual Numerics' PV-WAVE software package SO NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A- ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT LA English DT Article NR 0 SN 0168-9002 PU ELSEVIER SCIENCE BV C1 DUBNA JOINT NUCL RES INST,FRANK LAB NEUTRON PHYS,DUBNA 141980,MOSCOW REGION,RUSSIA DE data visualization; neutron scattering; time-of-flight experiments AB The presented work is aimed at the development of tools for data access, visualization, and manipulation of time-of-flight neutron spectrum data acquired from different spectrometers on the IBR-2 fast-pulsed reactor of the Frank Laboratory of Neutron Physics, JINR. Special tools, based on the PV-WAVE software package, for importing neutron data from IBR-2, exporting them in some common format, and data manipulations of such data have been developed by the author. Information about PV-WAVE-based tools for FLNP users is available on the Web. TC 1 BP 93 EP 94 PG 2 JI Nucl. Instrum. Methods Phys. Res. Sect. A-Accel. Spectrom. Dect. Assoc. Equip. PY 1997 PD APR 11 VL 389 IS 1-2 GA XJ015 PI AMSTERDAM RP Litvinenko EI DUBNA JOINT NUCL RES INST,FRANK LAB NEUTRON PHYS,DUBNA 141980,MOSCOW REGION,RUSSIA J9 NUCL INSTRUM METH PHYS RES A PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1997XJ01500024 ER PT Journal AU Lehtinen, JC Forsstrom, J Koskinen, P Penttila, TA Jarvi, T Anttila, L TI Visualization of clinical data with neural networks, Case study: Polycystic ovary syndrome SO INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS LA English DT Article NR 13 SN 1386-5056 PU ELSEVIER SCI IRELAND LTD C1 UNIV TURKU,DEPT COMP SCI,DATACITY A 2,FIN-20520 TURKU,FINLAND UNIV TURKU,CENT HOSP,DEPT CLIN CHEM,FIN-20520 TURKU,FINLAND UNIV TURKU,CENT HOSP,DEPT OBSTET & GYNECOL,FIN-20520 TURKU,FINLAND DE neural networks; self-organizing map; topology-preserving feed- forward network ID HORMONE AB In medicine, the use of neural networks has concentrated mainly on classification problems. Clinicians are often interested in knowing what a patient's status is compared with other similar cases. Compared with biostatistics neural networks have one major drawback: the reliability of the classification is difficult to express. Therefore, clear visualization of the measurements can be more helpful than the calculated probability of a disease. The self-organizing map is the most widely used neural network for data visualization. Although, visualization can be attached to almost any feed-forward network as well. In this paper, we describe a topology- preserving feed-forward network and compare it with the self- organizing map. The two neural network models are used in a case study on the diagnosis of polycystic ovary syndrome, which is a common female endocrine disorder characterized by menstrual abnormalities, hirsutism and infertility. (C) 1997 Elsevier Science Ireland Ltd. CR ADAMS J, 1985, LANCET, V2, P1375 CASTRO A, 1974, STEROIDS, V23, P625 FRANKS S, 1989, CLIN ENDOCRINOL, V31, P82 KOHONEN T, 1982, BIOL CYBERN, V43, P59 KOHONEN T, 1995, SELF ORG MAPS KOSKI A, 1995, P APPL DEC TECHN NEU, P253 KOSKINEN P, 1996, FERTIL STERIL, V65, P517 LEJEUNELENAIN C, 1979, CLIN CHIM ACTA, V94, P327 MATINLAURI IH, 1995, DIABETES CARE, V18, P1357 ROBINSON S, 1992, BRIT J OBSTET GYNAEC, V99, P232 ROSNER W, 1972, J CLIN ENDOCR METAB, V34, P938 RUMELHART DE, 1986, PARALLEL DISTRIBUTED, V1 VAPOLA M, 1994, P C ART INT RES FINL, P55 TC 1 BP 145 EP 155 PG 11 JI Int. J. Med. Inform. PY 1997 PD APR VL 44 IS 2 GA XH037 PI CLARE RP Lehtinen JC UNIV TURKU,DEPT COMP SCI,DATACITY A 2,FIN-20520 TURKU,FINLAND J9 INT J MED INFORM PA CUSTOMER RELATIONS MANAGER, BAY 15, SHANNON INDUSTRIAL ESTATE CO, CLARE, IRELAND UT ISI:A1997XH03700006 ER PT Journal AU Krzanowski, WJ TI Recent trends and developments in computational multivariate analysis SO STATISTICS AND COMPUTING LA English DT Article NR 88 SN 0960-3174 PU CHAPMAN HALL LTD C1 UNIV EXETER,DEPT MATH STAT & OPERAT RES,EXETER EX4 4QE,DEVON,ENGLAND DE data visualization; high-dimensional data; non-linear ordination; non-parametric fitting; resampling methods; stochastic simulation ID PROJECTION PURSUIT; DISCRIMINANT-ANALYSIS; PRINCIPAL CURVES; GIBBS SAMPLER; REGRESSION; BOOTSTRAP; VARIABLES; DISTRIBUTIONS; CALIBRATION; MATRICES AB Many traditional multivariate techniques such as ordination, clustering, classification and discriminant analysis are now routinely used in most fields of application. However, the past decade has seen considerable new developments, particularly in computational multivariate methodology. This article traces some of these developments and highlights those trends that may prove most fruitful for future practical implementation. CR *SAS I INC, 1990, SAS US GUID ALTMAN NS, 1992, AM STAT, V46, P175 ARNOLD SF, 1993, HDB STAT, V9, P599 BABU GJ, 1993, HDB STAT, V9, P627 BAILEY TC, 1966, P 28 S INT SYDN AUST BANFIELD JD, 1992, J AM STAT ASSOC, V87, P7 BARNETT V, 1976, J ROYAL STATISTICA A, V139, P318 BARTLETT MS, 1951, ANN MATH STAT, V22, P107 BECKER RA, 1988, NEW S LANGUAGE BELLMAN R, 1961, ADAPTIVE CONTROL PRO BHATTACHARYYA A, 1943, B CALCUTTA MATH SOC, V35, P99 BOLLEN KA, 1989, STRUCTURAL EQUATIONS BOSWELL MT, 1993, HDB STAT, V9, P661 BREIMAN L, 1984, CLASSIFICATION REGRE CAMPBELL NA, 1985, APPL STAT, V34, P235 CASELLA G, 1992, AM STAT, V46, P167 COX DR, 1996, MULTIVARIATE DEPENDE CRESSIE NA, 1991, STAT SPATIAL DATA CROWDER MJ, 1990, ANAL REPEATED MEASUR DAVISON AC, 1986, BIOMETRIKA, V73, P555 DENHAM MC, 1993, APPL STAT-J ROY ST C, V42, P515 DONOHO DL, 1995, J ROY STAT SOC B MET, V57, P301 EFRON B, 1992, J ROY STAT SOC B MET, V54, P83 EVERITT B, 1993, CLUSTER ANAL FANG KT, 1990, GEN MULTIVARIATE ANA FIX E, 1951, 4 US AIR FORC SCH AV FLURY B, 1995, DESCRIPTIVE MULTIVAR, P14 FRIEDMAN JH, 1991, ANN STAT, V19, P1 FRIEDMAN JH, 1984, J AM STAT ASSOC, V79, P599 FRIEDMAN JH, 1981, J AM STAT ASSOC, V76, P817 GABRIEL KR, 1962, ANN MATH STAT, V33, P201 GABRIEL KR, 1971, BIOMETRIKA, V58, P453 GELFAND AE, 1990, J AM STAT ASSOC, V85, P398 GEMAN S, 1984, IEEE T PATTERN ANAL, V6, P721 GIFI A, 1990, NONLINEAR MULTIVARIA GNANADESIKAN R, 1977, METHODS STATISTICAL GOODALL C, 1991, J ROY STAT SOC B MET, V53, P285 GOWER JC, 1988, BIOMETRIKA, V73, P445 GOWER JC, 1966, BIOMETRIKA, V53, P325 GOWER JC, 1996, BIPLOTS GREEN PJ, 1980, INTERPRETING MULTIVA, P3 GREEN PJ, 1993, NONPARAMETRIC REGRES HARDLE W, 1991, SMOOTHING TECHNIQUES HASTIE T, 1989, J AM STAT ASSOC, V84, P502 HASTIE T, 1986, STAT SCI, V1, P297 HASTIE TJ, 1990, GEN ADDITIVE MODELS HASTINGS WK, 1970, BIOMETRIKA, V57, P97 HERTZ J, 1991, INTRO THEORY NEURAL JONES MC, 1992, AM STAT, V46, P140 JONES MC, 1987, J ROY STAT SOC A GEN, V150, P1 JORESKOG KG, 1969, PSYCHOMETRIKA, V34, P183 JORESKOG KG, 1967, PSYCHOMETRIKA, V32, P443 KENWARD MG, 1987, APPL STAT-J ROY ST C, V36, P296 KRZANOWSKI WJ, 1995, APPL STAT-J ROY ST C, V44, P101 KRZANOWSKI WJ, 1995, COMPUT STAT DATA AN, V19, P419 KRZANOWSKI WJ, 1993, MULTIVARIATE ANAL FU, V2, P87 KRZANOWSKI WJ, 1988, PRINCIPLES MULTIVARI LAURITZEN SL, 1996, GRAPHICAL MODELS MATUSITA K, 1956, ANN I STAT MATH, V8, P67 MCCULLAGH P, 1990, GENERALIZED LINEAR M MCLACHLAN GJ, 1992, DISCRIMINANT ANAL ST METROPOLIS N, 1953, J CHEM PHYS, V21, P1087 MEULMAN JJ, 1986, DISTANCE APPROACH NO MEULMAN JJ, 1992, PSYCHOMETRIKA, V54, P539 PEDDADA SD, 1993, HDB STAT, V9, P723 QUENOUILLE MH, 1956, BIOMETRIKA, V43, P353 RAMSAY JO, 1991, J ROY STAT SOC B MET, V53, P539 RICE JA, 1991, J ROY STAT SOC B MET, V53, P233 RIPLEY BD, 1994, J ROY STAT SOC B MET, V56, P409 RIPLEY BD, 1993, NETWORKS CHAOS STAT, P40 SCOTT DW, 1992, MULTIVARIATE DENSITY SILVERMAN BW, 1986, DENSITY ESTIMATION S SMITH AFM, 1993, J ROYAL STAT SOC B, V55, P3 SMITH AFM, 1996, SYDN INT STAT C SYDN SMITH DJ, 1993, STAT COMPUT, V3, P71 SOLLA SA, 1988, COMPLEX SYSTEMS, V2, P625 STONE M, 1974, J ROY STAT SOC B MET, V36, P111 SUNDBERG R, 1989, TECHNOMETRICS, V31, P365 TANNER MA, 1987, J AM STAT ASSOC, V82, P528 TITTERINGTON DM, 1979, APPL STAT, V27, P227 VALCHONIKOLIS IG, 1994, COMMUNICATIONS STA A, V23, P1087 VANOOYEN A, 1992, NEURAL NETWORKS, V5, P465 WEBB AR, 1996, STAT COMPUT, V6, P159 WEGMAN EJ, 1993, HDB STAT, V9, P857 WELCH BL, 1939, BIOMETRIKA, V31, P218 WHITTAKER J, 1990, GRAPHICAL MODELS APP YOUNG FW, 1993, HDB STAT, V9, P959 YOUNG GA, 1994, STAT SCI, V9, P382 TC 1 BP 87 EP 99 PG 13 JI Stat. Comput. PY 1997 PD JUN VL 7 IS 2 GA XD410 PI LONDON RP UNIV EXETER,DEPT MATH STAT & OPERAT RES,EXETER EX4 4QE,DEVON,ENGLAND J9 STAT COMPUT PA 2-6 BOUNDARY ROW, LONDON, ENGLAND SE1 8HN UT ISI:A1997XD41000002 ER PT Journal AU Herman, D TI Language for data visualization SO MECHANICAL ENGINEERING LA English DT Editorial Material NR 0 SN 0025-6501 PU ASME-AMER SOC MECHANICAL ENG TC 0 BP 16 EP 16 PG 1 JI Mech. Eng. PY 1997 PD JUN VL 119 IS 6 GA XD117 PI NEW YORK J9 MECH ENG PA 345 E 47TH ST, NEW YORK, NY 10017 UT ISI:A1997XD11700014 ER PT Journal AU Nelson, JC TI QGENE: Software for marker-based genomic analysis and breeding SO MOLECULAR BREEDING LA English DT Article NR 19 SN 1380-3743 PU KLUWER ACADEMIC PUBL C1 CORNELL UNIV,DEPT PLANT BREEDING & BIOMETRY,252 EMERSON HALL,ITHACA,NY 14853 DE marker-assisted breeding; QTL analysis; software ID QUANTITATIVE TRAIT LOCI; LINES AB Efficient use of DNA markers for genomic research and crop improvement will depend as much on computational tools as on laboratory technology. The large size and multidimensional character of marker datasets invite novel approaches to data visualization. Described here is a software application embodying two design principles: conventional reduction of raw genetic marker data to numerical summary statistics, and fast, interactive graphical display of both data and statistics. The program performs various analyses for mapping quantitative- trait loci in real or simulated datasets and other analyses in aid of phenotypic and marker-assisted breeding. Functionality is described and some output is illustrated. CR BAILEY NTJ, 1961, MATH THEORY INFECTIO DARVASI A, 1995, GENETICS, V141, P1199 DOERGE RW, 1995, THEOR APPL GENET, V90, P980 HALEY CS, 1994, GENETICS, V136, P1197 HALEY CS, 1992, HEREDITY, V69, P315 HYNE V, 1995, MOL BREEDING, V1, P273 JANSEN RC, 1994, GENETICS, V136, P1447 KENDALL MG, 1990, RANK CORRELATION MET KNOTT SA, 1992, GENETICS, V132, P1211 KRUGLYAK L, 1995, GENETICS, V139, P1421 LANDER ES, 1987, GENOMICS, V1, P174 MARTNEZ O, 1994, HEREDITY, V7, P198 MATTHEWS DM, 1996, GRAINGENES TRITICEAE MURIGNEUX A, 1993, THEOR APPL GENET, V86, P837 PRESS WH, 1992, NUMERICAL RECIPES C TANKSLEY SD, 1996, THEOR APPL GENET, V92, P191 TINKER NA, 1995, JQTL, V1 YOUNG ND, 1989, THEOR APPL GENET, V77, P95 ZENG ZB, 1994, GENETICS, V136, P1457 TC 101 BP 239 EP 245 PG 7 JI Mol. Breed. PY 1997 VL 3 IS 3 GA XB245 PI DORDRECHT RP Nelson JC CORNELL UNIV,DEPT PLANT BREEDING & BIOMETRY,252 EMERSON HALL,ITHACA,NY 14853 J9 MOL BREEDING PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS UT ISI:A1997XB24500010 ER PT Journal AU Bennett, MR Hambrey, MJ Huddart, D TI Modification of clast shape in high-arctic glacial environments SO JOURNAL OF SEDIMENTARY RESEARCH LA English DT Article NR 40 SN 1073-130X PU SEPM-SOC SEDIMENTARY GEOLOGY C1 UNIV GREENWICH,SCH EARTH SCI,MEDWAY TOWNS CAMPUS,CHATHAM ME4 4AW,KENT,ENGLAND LIVERPOOL JOHN MOORES UNIV,SCH BIOL & EARTH SCI,LIVERPOOL L3 3AF,MERSEYSIDE,ENGLAND LIVERPOOL JOHN MOORES UNIV,SCH EDUC & COMMUNITY STUDIES,LIVERPOOL L17 6EN,MERSEYSIDE,ENGLAND ID WITHIN-VALLEY ASYMMETRY; PEBBLE SHAPE; CONTINENTAL-SHELF; MARINE-SEDIMENTS; SOUTHERN-NORWAY; TILL GENESIS; END MORAINES; TRANSPORT; JOTUNHEIMEN; SIZE AB Clast Shape data from a range of different glacial environments at several high-arctic valley glaciers in Svalbard are presented. These data add to the growing body of reference information about clast shape in modern glacial environments and is used to explore the role of lithology in clast morphogenesis and to evaluate the different methodological approaches to the analysis of clast shape data. The following conclusions are drawn: (1) it is possible to distinguish clasts transported subglacially from those moved supraglacially; (2) It is not possible to differentiate among different types of subglacial sediment or to distinguish them collectively from glaciofluvial samples; (3) lithology has some influence on clast shape, although not as much as previously suggested; and (4) covariant plots of the RA (percentage of angular and very angular clasts) versus C-40 (percentage of clasts with c to a axial ratio less than or equal to 0.4) index give superior data visualization and discriminate more effectively among different glacial sediments than sphericity and roundness plots. 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Sediment. Res. PY 1997 PD MAY VL 67 IS 3 PN A GA XA563 PI TULSA RP Bennett MR UNIV GREENWICH,SCH EARTH SCI,MEDWAY TOWNS CAMPUS,CHATHAM ME4 4AW,KENT,ENGLAND J9 J SEDIMENT RES PA 1731 E 71ST STREET, TULSA, OK 74136-5108 UT ISI:A1997XA56300018 ER PT Journal AU Miller, MK TI Three-dimensional atom probes SO JOURNAL OF MICROSCOPY-OXFORD LA English DT Review NR 47 SN 0022-2720 PU BLACKWELL SCIENCE LTD C1 OAK RIDGE NATL LAB,DIV MET & CERAM,MICROSCOPY & MICROANALYT SCI GRP,POB 2008,OAK RIDGE,TN 37831 ID FIELD-ION MICROSCOPY; EVAPORATION; DESIGNS; SYSTEM AB A three-dimensional atom probe permits the elemental reconstruction of a small volume of a specimen by determining the x, y and z positions and mass-to-charge ratio of the atoms in that volume. The historical development of this new type of atom probe is described. Several variants of these instruments including the position-sensitive atom probe, the optical atom probe and the tomographic atom probe are reviewed. The various methods of data visualization and analysis are summarized. The performance of the three-dimensional atom probe is compared with the energy-compensated atom probe. 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Microsc.-Oxf. PY 1997 PD APR VL 186 PN 1 GA WZ021 PI OXFORD RP Miller MK OAK RIDGE NATL LAB,DIV MET & CERAM,MICROSCOPY & MICROANALYT SCI GRP,POB 2008,OAK RIDGE,TN 37831 J9 J MICROSC-OXFORD PA P O BOX 88, OSNEY MEAD, OXFORD, OXON, ENGLAND OX2 0NE UT ISI:A1997WZ02100001 ER PT Journal AU Karavanic, KL Myllymaki, J Livny, M Miller, BP TI Integrated visualization of parallel program performance data SO PARALLEL COMPUTING LA English DT Article NR 14 SN 0167-8191 PU ELSEVIER SCIENCE BV C1 UNIV WISCONSIN,DEPT COMP SCI,1210 W DAYTON ST,MADISON,WI 53706 DE data visualization; parallel programming; performance profiling AB Performance tuning a parallel application involves integrating performance data from many components of the system, including the message passing library, performance monitoring tool, resource manager, operating system, and the application itself. The current practice of visualizing these data streams using a separate, customized tool for each source is inconvenient from a usability perspective, and there is no easy way to visualize the data in an integrated fashion. We demonstrate a solution to this problem using Devise, a generic visualization tool which is designed to allow an arbitrary number of different but related data streams to be integrated and explored visually in a flexible manner. We display data emanating from a variety of sources side by side in three case studies. First we interface the Paradyn parallel performance tool and Devise, using two simple data export modules and Paradyn's simple visualization interface. We show several Devise/Paradyn visualizations which are useful for performance tuning parallel codes, and which incorporate data from Unix utilities and application output. Next we describe the visualization of trace data from a parallel application running in a Condor cluster of workstations. Finally we demonstrate the utility of Devise visualizations in a study of Condor cluster activity. CR 1994, INT J SUPERCOMPUTER, V8 CHENG M, 1995, P SOC PHOTO-OPT INS, V2410, P108 CHENG M, 1995, THESIS U WISCONSIN M GESIT GA, 1990, ORNLTM11130 GRAHAM RL, 1969, SIAM J APPL MATH, V17, P416 LITZKOW MJ, 1988, 8TH P INT C DISTR CO, P104 LIVNY M, 1996, P SPIE INT SOC OPT E, V2657 MILLER BP, 1995, IEEE COMPUTER, V28 OUSTERHOUT JK, 1994, TCL TK TOOLKIT PRUYNE J, 1995, LECT NOTES COMPUTER, V949 RAMAKRISHNAN R, 1994, P INT C MAN DAT DEC SESHADRI P, 1994, P ACM SIGMOD INT C M, P430 SHELTON WA, 1994, P SHPCC MAY, P103 WEBB D, 1993, 324 I OC SCI TC 1 BP 181 EP 198 PG 18 JI Parallel Comput. PY 1997 PD APR VL 23 IS 1-2 GA WW795 PI AMSTERDAM RP Karavanic KL UNIV WISCONSIN,DEPT COMP SCI,1210 W DAYTON ST,MADISON,WI 53706 J9 PARALLEL COMPUT PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1997WW79500013 ER PT Journal AU Drapcho, DL Crocombe, RA Seebode, J TI Advances in photoacoustic step-scan FT-IR spectroscopy SO MIKROCHIMICA ACTA LA English DT Article NR 9 SN 0026-3672 PU SPRINGER-VERLAG WIEN C1 BIORAD DIGILAB DIV,237 PUTNAM AVE,CAMBRIDGE,MA 02139 BIORAD LABS GMBH,D-47809 KREFELD,GERMANY DE FT-IR; photoacoustic; depth profiling; step-scan; digital signal processing AB An integrated system for FT-IR photoacoustic depth-profiling experiments is described, including data-visualization techniques. An example using a multilayer polymer is presented. Exten sions of the current method to other spectral regions and using digital signal-processing are discussed. CR CURBELO R, UNPUB DITTMAR RM, 1991, APPL SPECTROSC, V45, P1104 GRAHAM JA, 1985, FOURIER TRANSFORM IN MANNING CJ, 1993, APPL SPECTROSC, V47, P1345 MCCLELLAND JF, 1983, ANAL CHEM, V55, PA89 MCCLELLAND JF, 1993, P SOC PHOTO-OPT INS, V2809, P302 NODA I, 1990, APPL SPECTROSC, V44, P550 NODA I, 1989, J AM CHEM SOC, V111, P8116 STOUT PJ, 1993, P SOC PHOTO-OPT INS, V2809, P300 TC 0 BP 585 EP 588 PG 4 JI Mikrochim. Acta PY 1997 SU 14 GA WW404 PI VIENNA RP Drapcho DL BIORAD DIGILAB DIV,237 PUTNAM AVE,CAMBRIDGE,MA 02139 J9 MIKROCHIM ACTA PA SACHSENPLATZ 4-6, PO BOX 89, A-1201 VIENNA, AUSTRIA UT ISI:A1997WW40400147 ER PT Journal AU vanTeylingen, R Ribarsky, W vanderMast, C TI Virtual data visualizer SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS LA English DT Article NR 29 SN 1077-2626 PU IEEE COMPUTER SOC C1 DELFT UNIV TECHNOL,POB 356,NL-2600 AJ DELFT,NETHERLANDS GEORGIA INST TECHNOL,GRAPH VISUALIZAT & USABIL CTR,ATLANTA,GA 30332 DE visualization systems; virtual reality; multivariate analysis AB We present the Virtual Data Visualizer, a highly interactive, immersive environment for visualizing and analyzing data. VDV is a set of tools for exploratory data visualization that does not focus on just one type of application. It employs a data organization with data arranged hierarchically in classes that can be modified by the user within the virtual environment. The class structure is the basis for bindings or mappings between data variables and glyph elements, which the user can make, change, or remove. The binding operation also has a set of defaults so that the user can quickly display the data. The VDV requires a user interface that is fairly complicated for a virtual environment. We have taken the approach that a combination of more-or-less traditional menus and more direct means of icon manipulation will do the job. This work shows that a useful interface and set of tools can be built. Controls in VDV include a panel for controlling animation of the data and zooming in and out. Tools include a workbench for changing the glyphs and setting glyph/variable ranges and a boundary tool for defining new classes spatially. CR *WAV TECHN I, 1993, WAV DAT VIS US GUID BESHERS C, 1992, P VIS 92 BOST, P283 BRIJS PAL, 1993, 9391 DELFT U TECHN BROOKS FP, 1993, P IEEE S RES FRONT V, P2 BRYSON S, INTRO VIRTUAL REALIT BRYSON S, 1993, P IEEE VRAIS SEATTL, P20 BUTTERWORTH J, 1992, 1992 P S INT 3D GRAP, P135 CHENG HP, 1993, SCIENCE, V260, P1304 CRUZNEIRA C, 1993, P IEEE S RES FRONT V, P59 FOLEY JD, 1986, IEEE COMPUT GRAPH, V6, P16 GIBSON JJ, 1979, ECOLOGICAL APPROACH GOMEZ D, 1995, P IEEE VRAIS 95, P198 HABER R, 1990, VISUALIZATION SCI CO HABER RB, 1991, P VIS 91 SAN DIEG CA, P298 HARMON R, 1996, P IEEE VIRT REAL ANN, P239 JACOBY RH, USING VIRTUAL MENUS KOLLER D, 1995, P VIS 95 OCT, P94 LAUREL B, 1993, COMPUTERS THEATRE LEEUW WC, 1993, P VIS 93 SAN JOS CAL, P39 RIBARSKY W, 1994, COMPUTER JUL RIBARSKY W, 1994, FRONTIERS VISUALIZAT, P103 SMETS GJF, 1993, ECOLOGY HUMAN MACHIN SMITH M, COMMUNICATION TAYLOR RM, 1993, COMPUTER GRAPHICS SI, V27, P127 TREINISH L, 1992, P BRIT COMP SOC C AU TREINISH LA, 1993, BYTE, V18, P132 UPSON C, 1989, IEEE COMPUT GRAPH, V9, P30 VANDAM A, 1993, IEEE S RES FRONT VIR, P5 WARD MO, 1994, P IEEE C VIS SAN JOS, P326 TC 5 BP 65 EP 74 PG 10 JI IEEE Trans. Vis. Comput. Graph. PY 1997 PD JAN-MAR VL 3 IS 1 GA WU099 PI LOS ALAMITOS RP vanTeylingen R DELFT UNIV TECHNOL,POB 356,NL-2600 AJ DELFT,NETHERLANDS J9 IEEE TRANS VISUAL COMPUT GR PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 UT ISI:A1997WU09900006 ER PT Journal AU Babu, GP TI Self-organizing neural networks for spatial data SO PATTERN RECOGNITION LETTERS LA English DT Article NR 23 SN 0167-8655 PU ELSEVIER SCIENCE BV C1 NATL UNIV SINGAPORE,INST SYST SCI,NOVEL FUNCT ISS LAB,REAL WORLD COMP PARTNERSHIP,KENT RIDGE,SINGAPORE 0511,SINGAPORE DE spatial data; clustering; kohonen network; indexing ID ALGORITHM AB In this paper, we present a self-organization neural network approach for spatial data visualization and spatial data indexing. Spatial data is typically used to represent multi- dimensional objects. Generally, for efficient processing such as indexing and retrieval, each multi-dimensional object is represented by an isothetic minimum bounding rectangle. Direct visualization of these multi-dimensional rectangles, denoting spatial objects, is not possible, if the number of dimensions exceeds three. Many linear and non-linear mapping techniques have been proposed in the literature for mapping point data, i.e., data that are points in multi-dimensional space. These approaches map points in higher-dimensional space to lower- dimensional space. Making use of these point data mapping approaches is a computationally intensive task as the number of points to be mapped is very large. In this paper, we propose a Kohonen's self-organization neural network approach for clustering spatial data. Cluster prototypes associated with nodes in the network are mapped into lower dimensions for data visualization using a non-linear mapping technique. We explain the applicability of this approach for efficient indexing of spatial data. (C) 1997 Elsevier Science B.V. 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Lett. PY 1997 PD FEB VL 18 IS 2 GA WQ651 PI AMSTERDAM RP Babu GP NATL UNIV SINGAPORE,INST SYST SCI,NOVEL FUNCT ISS LAB,REAL WORLD COMP PARTNERSHIP,KENT RIDGE,SINGAPORE 0511,SINGAPORE J9 PATTERN RECOGNITION LETT PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1997WQ65100003 ER PT Journal AU Healy, TM Ellis, JT Fontaine, AA Jarrett, CA Yoganathan, AP TI An automated method for analysis and visualization of laser Doppler velocimetry data SO ANNALS OF BIOMEDICAL ENGINEERING LA English DT Article NR 13 SN 0090-6964 PU BLACKWELL SCIENCE INC C1 GEORGIA INST TECHNOL,SCH CHEM ENGN,CARDIOVASC FLUID MECH LAB,ATLANTA,GA 30332 GEORGIA INST TECHNOL,SCH MECH ENGN,ATLANTA,GA 30332 GEORGIA INST TECHNOL,INST BIOENGN & BIOSCI,ATLANTA,GA 30332 DE laser Doppler velocimetry; computerized data analysis; computerized data visualization; pulsatile flow; principal stress analysis ID HEART-VALVES; VELOCITY AB The analysis and visualization of large data sets collected by use of laser Doppler velocimetry has presented a challenge to researchers using this technique to investigate complex flow fields. This paper describes an automated procedure for analysis and animation of two- and three-dimensional laser Doppler velocimetry data. The procedure consists of a suite of FORTRAN programs for calculating phase window averages of velocity and the Reynolds stress tensor, calculating the principal normal stresses, maximum shear stresses, and preparation of data files for input into Plot-3D compatible data visualization software. An example application of these techniques to data collected from an in vitro investigation of the retrograde flow field associated with a bileaflet mechanical heart valve is also presented. CR ATKINSON KE, 1989, INTRO NUMERICAL ANAL BALDWIN JT, 1994, J BIOMECH ENG-T ASME, V116, P190 BALDWIN JT, 1993, J BIOMECH ENG-T ASME, V115, P396 BLUESTEIN D, 1995, J BIOMECH, V28, P915 FONTAINE AA, 1996, ASAIO J, V42, P154 GARVER D, 1995, TEX HEART I J, V22, P86 GROSS JM, 1991, ASAIO T, V37, PM357 HIGDON A, 1985, MECH MAT HORSTKOTTE D, 1995, J HEART VALVE DIS, V4, P141 SELBY SM, 1975, HDB TABLES MATH, P129 SUNG HW, 1994, J HEART VALVE DIS, V3, P673 SUTERA SP, 1992, THROMBOSIS EMBOLISM, P149 YOGANATHAN AP, 1992, THROMBOSIS EMBOLISM, P123 TC 3 BP 335 EP 343 PG 9 JI Ann. Biomed. Eng. PY 1997 PD MAR-APR VL 25 IS 2 GA WN694 PI MALDEN RP GEORGIA INST TECHNOL,SCH CHEM ENGN,CARDIOVASC FLUID MECH LAB,ATLANTA,GA 30332 J9 ANN BIOMED ENG PA 350 MAIN ST, MALDEN, MA 02148 UT ISI:A1997WN69400010 ER PT Journal AU Forgionne, GA TI HADTS: A decision technology system to support army housing management SO EUROPEAN JOURNAL OF OPERATIONAL RESEARCH LA English DT Article NR 27 SN 0377-2217 PU ELSEVIER SCIENCE BV C1 UNIV MARYLAND,DEPT INFORMAT SYST,CATONSVILLE,MD 21228 DE computerized mapping; geographic information systems; decision support systems; military housing management; decision technology systems; data visualization; database management systems; executive information systems ID INFORMATION-SYSTEMS; MODEL AB The Department of Army must provide its personnel with acceptable housing at minimum cost within the vicinity of military installations. To achieve these housing objectives, the Army often must enter into agreements for the longterm construction of onpost housing or the leasing of existing offpost housing. A decision support system, called HANS, has been developed to project the necessary construction or leasing. HANS had some gaps in supporting the construction and leasing decisions. This paper describes the gaps and shows how a decision technology system, called the Housing Analysis Decision Technology System (HADTS), can help Army managers to overcome the support gaps. It also overviews HADTS's benefits, challenges, and limitations. CR ADELMAN L, 1992, EVALUATING DECISION BENBASAT I, 1990, DECISION SUPPORT SYS, V6, P203 BILLMAN B, 1993, DECISION SCI, V24, P23 BLACKLEY P, 1988, REV ECON STAT, V70, P266 BRUNO L, 1992, OPEN SYSTEMS TODAY, V12, P50 CARRUTHERS DT, 1989, URBAN STUD, V26, P214 COOK GJ, 1993, DECISION SCI, V24, P683 DADAM P, 1989, IBM SYST J, V28, P661 FISCHER MM, 1993, GEOGRAPHIC INFORMATI FORGIONNE GA, 1991, INTERFACES, V21, P37 FORGIONNE GA, 1991, J INFORMATION SYSTEM, V8, P34 FORGIONNE GA, 1992, SYST RES, V9, P65 FRANKLIN C, 1992, DATABASE, V15, P12 GOODMAN A, 1988, J URBAN ECON, V23, P327 GRUPE FH, 1992, INFORMATION STRATEGY, V8, P41 GRUPE FH, 1992, INFORMATION SYSTEMS, V9, P38 HUXHOLD WE, 1991, INTRO URBAN GEOGRAPH KAPLAN EH, 1988, INTERFACES, V18, P14 SENGUPTA K, 1993, MANAGE SCI, V39, P411 SILVER M, 1991, MIS Q, V18, P105 TARGOWSKI A, 1990, ARCHITECTURE PLANNIN TURBAN E, 1993, DECISION SUPPORT EXP TURNBULL AP, 1988, EDUC TRAIN MENT RET, V23, P261 TURNBULL GK, 1989, J URBAN ECON, V25, P103 WANG M, 1989, COMPUT IND, V13, P215 WATSON HJ, 1992, EXECUTIVE INFORMATIO WEST LA, 1993, DECISION SCI, V24, P229 TC 2 BP 363 EP 379 PG 17 JI Eur. J. Oper. Res. PY 1997 PD MAR 1 VL 97 IS 2 GA WK648 PI AMSTERDAM RP Forgionne GA UNIV MARYLAND,DEPT INFORMAT SYST,CATONSVILLE,MD 21228 J9 EUR J OPER RES PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1997WK64800012 ER PT Journal AU [Anon] TI Data visualization software helps engineers and scientists be more creative and more productive SO MICROELECTRONICS JOURNAL LA English DT Article NR 0 SN 0026-2692 PU ELSEVIER ADVANCED TECHNOLOGY AB Tecplot 7.0 addresses the diverse needs of real-world research and design, from interactive analysis to presentation. TC 0 BP R17 EP R18 PG 2 JI Microelectron. J. PY 1997 PD MAR VL 28 IS 3 GA WJ654 PI OXFORD J9 MICROELECTR J PA OXFORD FULFILLMENT CENTRE THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, OXON, ENGLAND OX5 1GB UT ISI:A1997WJ65400031 ER PT Journal AU Toutin, T TI Qualitative aspects of chromo-stereoscopy for depth perception SO PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING LA English DT Article NR 30 SN 0099-1112 PU AMER SOC PHOTOGRAMMETRY C1 CANADA CTR REMOTE SENSING,588 BOOTH ST,OTTAWA,ON K1A OY7,CANADA ID MULTISOURCE DATA AB The display of three-dimensional (3D) quantitative data sets is a basic topic of research in cartography, image processing, and applications related to spatial information. A new application for data visualization and analysis, which combines color vision and depth perception, has been developed using the effect known as chromo-stereoscopy based on Einthoven's theory. It enables the generation of flat color composite images from multisource data in which depth information is coded into colors. When viewed with double prism refraction ChromaDepth((TM)) glasses, a ''dramatic'' 3D effect is produced. Following a description of the method, the geometric and radiometric processing parameters are qualitatively analyzed to assess their impact on the quality of the chrome- stereoscopic images and depth perception. CR ARMENAKIS C, 1995, GEOMATICA, V49, P433 BEMIS SV, 1988, HUM FACTORS, V30, P162 BENTON SA, 1985, OPTICAL ENG, V24, P338 BRAUNSTEIN ML, 1976, DEPTH PERCEPTION MOT EINTHOVEN W, 1885, A VONGRAEFES ARCH OP, V31, P211 FRIEDHOFF RM, 1991, 2 COMPUTER REVOLUTIO GABOR D, 1948, NATURE, V161, P777 HANES RM, 1959, J OPT SOC AM, V49, P1060 HOFFMAN RR, 1990, GEOCARTO INT, V5, P3 HOFFMAN RR, 1993, WEATHER FORECAST, V8, P505 HOFFMAN RR, 1991, WEATHER FORECAST, V6, P98 IMSAND DJ, 1986, 4567515, US JONES ER, 1984, P SOC PHOTO-OPT INST, V457, P16 JUDD DB, 1971, ILLUMINATION ENG, V66, P256 LEITH EN, 1964, J OPT SOC AM, V54, P1295 LIVINGSTONE MS, 1988, SCI AM, V258, P78 MCCORMIK EJ, 1976, HUMAN FACTORS ENG DE, P62 MCLAURIN AP, 1988, IEEE T GEOSCIENCE RE, V26, P437 MILLERJACOBS HH, 1984, ADV DISPLAY TECHNOLO, V457, P14 OKOSHI T, 1976, 3 DIMENSIONAL IMAGIN PENNINGTON KS, 1965, APPL PHYS LETT, V7, P56 SMITH SL, 1962, J EXP PSYCHOL, V64, P434 STEENBLIK RA, 1986, 4597634, US STEENBLIK RA, 1991, 5002364, US TOUTIN T, 1995, EARSEL J ADV REMOTE, V4, P118 TOUTIN T, 1995, INT J REMOTE SENS, V16, P2795 TOUTIN T, 1995, PHOTOGRAMM ENG REM S, V61, P1209 URSERY L, 1993, PHOTOGRAMMETRIC ENG, V50, P1737 WALKO J, 1995, PHOTON SPECTRA, V29, P28 WARE C, 1988, HUM FACTORS, V30, P127 TC 2 BP 193 EP 203 PG 11 JI Photogramm. Eng. Remote Sens. PY 1997 PD FEB VL 63 IS 2 GA WG253 PI BETHESDA RP Toutin T CANADA CTR REMOTE SENSING,588 BOOTH ST,OTTAWA,ON K1A OY7,CANADA J9 PHOTOGRAMM ENG REMOTE SENSING PA 5410 GROSVENOR LANE SUITE 210, BETHESDA, MD 20814-2160 UT ISI:A1997WG25300009 ER PT Journal AU Hofmann, T Buhmann, JM TI Pairwise data clustering by deterministic annealing SO IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE LA English DT Article NR 47 SN 0162-8828 PU IEEE COMPUTER SOC C1 UNIV BONN,INST INFORMATIK 3,ROMERSTR 164,D-53117 BONN,GERMANY ID VECTOR QUANTIZATION; EM ALGORITHM; OPTIMIZATION; DISTRIBUTIONS; FILTERS AB Partitioning a data set and extracting hidden structure from the data arises in different application areas of pattern recognition, speech and image processing. Pairwise data clustering is a combinatorial optimization method for data grouping which extracts hidden structure from proximity data. We describe a deterministic annealing approach to painwise clustering which shares the robustness properties of maximum entropy inference. The resulting Gibbs probability distributions are estimated by mean-field approximation. A new structure-preserving algorithm to cluster dissimilarity data and to simultaneously embed these data in a Euclidian vector space is discussed which can be used for dimensionality reduction and data visualization. The suggested embedding algorithm which outperforms conventional approaches has been implemented to analyze dissimilarity data from protein analysis and from linguistics. The algorithm for pairwise data clustering is used to segment textured images. 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Pattern Anal. Mach. Intell. PY 1997 PD JAN VL 19 IS 1 GA WE528 PI LOS ALAMITOS RP Hofmann T UNIV BONN,INST INFORMATIK 3,ROMERSTR 164,D-53117 BONN,GERMANY J9 IEEE TRANS PATT ANAL MACH INT PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 UT ISI:A1997WE52800001 ER PT Journal AU Hyman, DE Whitehouse, DR Taylor, JA Larson, JW Hansen, DP Lindesay, JA TI The ANU translator: Facilitating computer visualization and data analysis of climate model outputs SO ENVIRONMENTAL SOFTWARE LA English DT Article NR 13 SN 0266-9838 PU ELSEVIER SCI LTD C1 AUSTRALIAN NATL UNIV,CTR RESOURCE & ENVIRONM STUDIES,CANBERRA,ACT,AUSTRALIA AUSTRALIAN NATL UNIV,SUPERCOMP FACIL,CANBERRA,ACT,AUSTRALIA AUSTRALIAN NATL UNIV,DEPT GEOG,CANBERRA,ACT,AUSTRALIA DE climate models; scientific visualization AB Computer visualization and data analysis tools are an essential component in the study and evaluation of climate model output. A number of useful visualization and analysis tools already exist, each with its own set of unique strengths. A translation tool is needed to convert data from the large number of model output formats into formats suitable for use with visualization and analysis tools. Such a translator would allow a researcher using a climate model to easily exploit a large number of visualization and analysis tools. Our goal is to develop a translation tool for UNIX computer systems that will be highly portable, very simple to use, and easily expandable. The translation program, The ANU translator will feature a graphical user interface when run on any system with an X11 interface. It will work with a number of climate model formats, including: CCM1, CCM2, RegCM2, MM5, CSIR09, and ANU-CTM. It will produce output in a range of data file formats, including Vis5D, GrADS, and netCDF. NetCDF allows access to a range of analysis and visualization tools, including AVS, HDF, and GMT. Vis5D, GrADS, and the tools compatible with netCDF will enable the user to view data statically or in animation, including the following critical options: (1) geographic distribution plots; (2) two-dimensional graphics; and (3) fully three-dimensional renderings. It is intended that this translation package be made widely available for use by the climate modelling community as a research and teaching aid. Copyright (C) 1996 Elsevier Science Ltd CR ANTHES RA, 1987, NCARTN282STR BUJA LE, 1992, NCARTN383IA BUJA LE, 1993, NCARTN384IA DICKINSON RE, 1993, NCARTN387STR GIORGI F, 1993, J CLIMATE, V6, P75 GRELL GA, 1994, NCARTN398STR HACK JJ, 1993, NCARTN382 MCGREGOR JL, 1993, 26 CSIRO DIV ATM RES OUSTERHOUT JK, 1994, TCL TK TOOLKIT TAYLOR JA, 1991, J GEOPHYS RES-ATMOSP, V96, P3013 TAYLOR JA, 1989, TELLUS B, V41, P272 TRENBERTH KE, 1992, NCARTN373 STR WILLIAMSON DL, 1987, NCARTN285 STR TC 0 BP 65 EP 72 PG 8 JI Environ. Softw. PY 1996 VL 11 IS 1-3 GA WE392 PI OXFORD RP AUSTRALIAN NATL UNIV,CTR RESOURCE & ENVIRONM STUDIES,CANBERRA,ACT,AUSTRALIA J9 ENVIRON SOFTWARE PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, OXON, ENGLAND OX5 1GB UT ISI:A1996WE39200010 ER PT Journal AU Pardey, J Roberts, S Tarassenko, L Stradling, J TI A new approach to the analysis of the human sleep/wakefulness continuum SO JOURNAL OF SLEEP RESEARCH LA English DT Article NR 20 SN 0962-1105 PU BLACKWELL SCIENCE LTD C1 UNIV OXFORD,MED ENGN UNIT,43 BANBURY RD,OXFORD OX2 6PE,ENGLAND UNIV LONDON IMPERIAL COLL SCI TECHNOL & MED,LONDON,ENGLAND CHURCHILL HOSP,OSLER CHEST UNIT,OXFORD OX3 7LJ,ENGLAND DE sleep EEG analysis; autoregressive modelling; self-organizing feature maps; neural networks ID AUTOMATIC-ANALYSIS; NEURAL NETWORKS; SLEEP; EEG; MODELS AB The conventional approach to the analysis of human sleep uses a set of pre-defined rules to allocate each 20 or 30-s epoch to one of six main sleep stages. The application of these rules is performed either manually, by visual inspection of the electroencephalogram and related signals, or, more recently, by a software implementation of these rules on a computer. This article evaluates the limitations of rule-based sleep staging and then presents a new method of sleep analysis that makes no such use of pre-defined rules and stages, tracking instead the dynamic development of sleep on a continuous scale. The extraction of meaningful features from the electroencephalogram is first considered, and for this purpose a technique called autoregressive modelling was preferred to the more commonly- used methods of band-pass filtering or the fast Fourier transform. This is followed by a qualitative investigation into the dynamics of the electroencephalogram during sleep using a technique for data visualization known as a self-organizing feature map. The insights gained using this map led to the subsequent development of a new, quantitative method of sleep analysis that utilizes the pattern recognition capabilities of an artificial neural network. The outputs from this network provide a second-by-second quantification of the sleep/wakefulness continuum with a resolution that far exceeds that of rule-based sleep staging. This is demonstrated by the neural network's ability to pinpoint micro-arousals and highlight periods of severely disturbed sleep caused by certain sleep disorders. Both these phenomena are of considerable clinical value, but neither are scored satisfactorily using rule-based sleep staging. CR BARLOW JS, 1985, J CLIN NEUROPHYSIOL, V2, P267 BORBELY AA, 1992, J SLEEP RES, V1, P63 COHEN A, 1986, BIOMEDICAL SIGNAL PR, V1 CROSS SS, 1995, LANCET, V346, P1075 HAUSTEIN W, 1986, ELECTROEN CLIN NEURO, V64, P364 HUANG SC, 1991, IEEE T NEURAL NETWOR, V2, P47 ISAKSSON A, 1981, P IEEE, V69, P451 KELLEY JT, 1985, CLIN ELECTROENCEPHAL, V16, P16 KEMP B, 1995, J SLEEP RES, V2, P179 KLOPPEL B, 1994, NEUROPSYCHOBIOLOGY, V29, P33 KOHONEN T, 1982, BIOL CYBERN, V43, P59 PARDEY J, 1996, MED ENG PHYS, V18, P2 PARDEY J, 1994, P 5 INT S BIOM ENG S, P167 RECHTSCHAFFEN A, 1968, MANUAL STANDARDIZED RICHARD MD, 1991, NEURAL COMPUT, V3, P461 RUMELHART DE, 1986, NATURE, V323, P533 SCHALTENBRAND N, 1993, COMPUT BIOMED RES, V26, P157 SCHALTENBRAND N, 1996, SLEEP, V19, P26 SMITH JR, 1986, HDB ELECTROENCEPHALO, V2, P131 STRADLING JR, 1992, J SLEEP RES, V1, P265 TC 16 BP 201 EP 210 PG 10 JI J. Sleep Res. PY 1996 PD DEC VL 5 IS 4 GA WE285 PI OXFORD RP Pardey J UNIV OXFORD,MED ENGN UNIT,43 BANBURY RD,OXFORD OX2 6PE,ENGLAND J9 J SLEEP RES PA P O BOX 88, OSNEY MEAD, OXFORD, OXON, ENGLAND OX2 0NE UT ISI:A1996WE28500001 ER PT Journal AU Coleman, J Goettsch, A Savchenko, A Kollmann, H Wang, K Klement, E Bono, P TI TeleInViVo(TM): Towards collaborative volume visualization environments SO COMPUTERS & GRAPHICS LA English DT Article NR 7 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 FRAUNHOFER CTR RES COMP GRAPH INC,167 ANGELL ST,PROVIDENCE,RI 02906 FRAUNHOFER INST COMP GRAPH,D-64283 DARMSTADT,GERMANY AB Converging technologies in the areas of telecommunications, volume visualization, and computer hardware and peripherals have made possible in recent years the development of new tools for collaboration that extend the reach of health care professionals and other consumers of volumetric data around the world. We describe a recent development at the Center for Research in Computer Graphics in Providence, RI, that makes a significant contribution to this area. TeleInViVo(TM) is an application that supports collaborative volumetric data visualization and exploration. It is an extension and partial reworking of InViVo(TM) a volume visualization application developed at the Fraunhofer IGD, in Darmstadt, Germany. InViVo, which is largely focused around the medical community and with an emphasis on diagnostic ultrasound, has been augmented with new modes of interaction, an intuitive collaboration mechanism, and an architectural modification to support future developments in this area. Copyright (C) 1996 Elsevier Science Ltd. CR ELVINS TT, 1996, COMPUTERS GRAPHICS, V20, P219 LIU PW, 1996, IEEE COMPUT GRAPH, V16, P42 MACEDONIA MR, 1995, P 1995 S INT 3D GRAP MACEDONIA MR, 1995, P 1995 WORKSH NETW R SAKAS G, 1991, COMPUTER GRAPHIK TOP, V4, P12 SAKAS G, 1995, SIGGRAPH 95 C P LOS, P465 SAKAS G, 1993, VISUAL COMPUT, V9, P425 TC 3 BP 801 EP 811 PG 11 JI Comput. Graph. PY 1996 PD NOV-DEC VL 20 IS 6 GA WC380 PI OXFORD RP Coleman J FRAUNHOFER CTR RES COMP GRAPH INC,167 ANGELL ST,PROVIDENCE,RI 02906 J9 COMPUT GRAPH PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1996WC38000008 ER PT Journal AU Arya, M Cody, W Faloutsos, C Richardson, J Toga, A TI A 3D medical image database management system SO COMPUTERIZED MEDICAL IMAGING AND GRAPHICS LA English DT Article NR 43 SN 0895-6111 PU PERGAMON-ELSEVIER SCIENCE LTD C1 IBM CORP,ALMADEN RES CTR,650 HARRY RD,K55-B1,SAN JOSE,CA 95120 DE image database; spatial database; medical imaging; data visualization; query processing ID BRAIN; ATLAS AB We describe the design and implementation of QBISM (Query By Interactive, Spatial Multimedia), a prototype for querying and visualizing 3D spatial data. Our medical image application is focused on the brain mapping requirements for multimodality relationships across multiple subjects. It incorporates data describing both structure and function. It includes data structures that describe anatomy, physiology, coordinates using rendered imagery and statistical output. The system is built on top of the Starburst DBMS extended to handle spatial data types, specifically, scalar fields and arbitrary regions of space within such fields. In this paper we list the requirements of the application, discuss the logical and physical database design issues, and present timing results from our prototype. We observed that the DBMS' early spatial filtering results in significant performance savings because the system response time is dominated by the amount of data retrieved, transmitted, and rendered. Copyright (C) 1996 Elsevier Science Ltd. 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PY 1996 PD JUL-AUG VL 20 IS 4 GA VW065 PI OXFORD RP Arya M IBM CORP,ALMADEN RES CTR,650 HARRY RD,K55-B1,SAN JOSE,CA 95120 J9 COMPUT MED IMAGING GRAPH PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1996VW06500009 ER PT Journal AU Gibbs, B TI GeoScene 3: Horizontal well handling, 3D visualization, and data handling tools SO GEOTIMES LA English DT Software Review NR 0 SN 0016-8556 PU AMER GEOLOGICAL INST C1 GIBBS ASSOC,EARTH SCI SOFTWARE INFORMAT,POB 706,BOULDER,CO 80306 TC 0 BP 33 EP 33 PG 1 JI Geotimes PY 1996 PD DEC VL 41 IS 12 GA VV691 PI ALEXANDRIA RP Gibbs B GIBBS ASSOC,EARTH SCI SOFTWARE INFORMAT,POB 706,BOULDER,CO 80306 J9 GEOTIMES PA 4220 KING ST, ALEXANDRIA, VA 22302-1507 UT ISI:A1996VV69100019 ER PT Journal AU [Anon] TI SIG/HCI, SIG/VIS and SIG/LAN-navigating complexity: New interfaces for the World Wide Web and data visualization SO PROCEEDINGS OF THE ASIS ANNUAL MEETING LA English DT Meeting Abstract NR 0 SN 0044-7870 PU INFORMATION TODAY INC TC 0 BP 282 EP 282 PG 1 JI Proc. ASIS Annu. Meet. PY 1996 VL 33 GA VR673 PI MEDFORD J9 PROC ASIS ANNU MEET PA 143 OLD MARLTON PIKE, MEDFORD, NJ 08055-8750 UT ISI:A1996VR67300066 ER PT Journal AU Mathews, GJ Towheed, SS TI WWW-based data systems for interactive manipulation of science data SO COMPUTER NETWORKS AND ISDN SYSTEMS LA English DT Article NR 6 SN 0169-7552 PU ELSEVIER SCIENCE BV C1 NASA,GODDARD SPACE FLIGHT CTR,MAIL CODE 633,GREENBELT RD,GREENBELT,MD 20771 HUGHES STX,GREENBELT,MD 20771 DE research; analysis; visualization; distribution; CGI; HTML; VRML AB The World Wide Web (WWW), which was originally conceived for document delivery, has now evolved into a vehicle supporting interactive data visualization and distribution. Two WWW-based data systems, OMNIWeb and COHOWeb, for providing enhanced access to scientific data have been developed at the National Space Science Data Center (NSSDC) at NASA's Goddard Space Flight Center. This paper discusses these systems and how the underlying model can be extended to support large amounts of data. Research in using VRML for scientific visualization is discussed with its potential in an interactive data system. CR *IDL SCI DAT FORM, 1994, VERS 3 6 GOUCHER GW, 1994, COMPREHENSIVE LOOK C MATHEWS GJ, 1995, COMPUTER NETWORKS IS, V27, P801 MATHEWS GJ, 1995, NSSDC NEWS, V11 MATHEWS GJ, VIRTUAL COHO SPACE P PESCE M, VIRTUAL REALITY MODE TC 0 BP 1857 EP 1864 PG 8 JI Comput. Netw. ISDN Syst. PY 1996 PD OCT VL 28 IS 13 GA VP077 PI AMSTERDAM RP Mathews GJ NASA,GODDARD SPACE FLIGHT CTR,MAIL CODE 633,GREENBELT RD,GREENBELT,MD 20771 J9 COMPUT NETWORKS ISDN SYST PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1996VP07700010 ER PT Journal AU [Anon] TI Data visualization software SO AEROSPACE ENGINEERING LA English DT News Item NR 0 SN 0736-2536 PU SOC AUTOMOTIVE ENG INC TC 0 BP 46 EP 46 PG 1 JI Aerosp. Eng. PY 1996 PD OCT VL 16 IS 10 GA VN006 PI WARRENDALE J9 AEROSPACE ENG PA 400 COMMONWEALTH DRIVE, WARRENDALE, PA 15096 UT ISI:A1996VN00600028 ER PT Journal AU Moni, S White, DW TI FrameView: Object-oriented visualization system for frame analysis SO JOURNAL OF COMPUTING IN CIVIL ENGINEERING LA English DT Article NR 21 SN 0887-3801 PU ASCE-AMER SOC CIVIL ENG C1 PURDUE UNIV,SCH CIVIL ENGN,W LAFAYETTE,IN 47907 AB Rapidly developing desktop computing capabilities, which include high resolution graphics and interactive graphical user interfaces, are leading to a new generation of engineering software. One of the challenges in engineering software development is the effective use of computer graphics for visualization of data. Object-oriented methodologies hold the greatest promise to support reuse and rapid prototyping for large-scale software development. This paper outlines some of the essential characteristics of FrameView, an object-oriented software system for visualization of responses from any type of frame analysis model. FrameView provides a graphical user interface that is based on the X-Window system and Motif. The graphics library used is PEX, which is a three-dimensional (3D) extension to the X-Window system. The programming language used is C++. Important attributes of PEX, and tedious programming details required when using PEX, have been encapsulated in a number of classes. General classes are developed to abstract the tools needed for viewing frame-element responses and two- dimensional (2D) and 3D graphics modeling. Viewing of results from 2D frame elements based on an assumed cubic transverse displacement field for drawing the deflected shape, and an assumed linear function for any other general response quantities along the element length, have been incorporated in FrameView. An example of how FrameView has been extended to handle other graphical representations of frame-element responses is discussed. CR *HIBB KARLSS SOR I, 1994, ABAQUS STAND US MAN *MSC PATRAN, 1995, P3 PATRAN US MAN VER ABDALLA JA, 1992, J COMPUT CIVIL ENG A, V6, P302 BASS L, 1991, DEV SOFTWARE USER IN BELL K, 1973, NORSAM PROGRAMMING S BOOCH G, 1994, OBJECT ORIENTED ANAL DESALVO GJ, 1989, ANSYS USERS MANUAL V FERGUSON PM, 1993, MOTIF REFERENCE MANU GASKINS T, 1992, PEXLIB PROGRAMMING M MCCORMICK JM, 1972, NASTRAN THEORETICAL MO O, 1978, COMPUT STRUCT, V8, P703 MODAK S, 1994, CESTR9422 PURD U SCH MONI S, 1995, THESIS PURDUE U W LA MORALES LE, 1994, THESIS PURDUE U W LA RAJAGOPALA M, 1994, CESTR9418 PURD U SCH ROSS TJ, 1992, J COMPUT CIVIL ENG, V6, P480 RYE A, 1993, XLIB REFERENCE MANUA SOTELINO ED, 1992, STEEL STRUCTURES J S, V3, P47 TURK Z, 1994, J COMPUT CIV ENG ASC, V8, P248 WHITE DW, 1996, SECSDE FILE INTERFAC ZIEMIAN RD, 1992, J STRUCT ENG-ASCE, V118, P2532 TC 0 BP 276 EP 285 PG 10 JI J. Comput. Civil. Eng. PY 1996 PD OCT VL 10 IS 4 GA VH866 PI NEW YORK RP Moni S PURDUE UNIV,SCH CIVIL ENGN,W LAFAYETTE,IN 47907 J9 J COMPUT CIVIL ENG PA 345 E 47TH ST, NEW YORK, NY 10017-2398 UT ISI:A1996VH86600005 ER PT Journal AU Menon, A Dhodi, N Mandella, W Chakrabarti, S TI Identifying fluid-bed parameters affecting product variability SO INTERNATIONAL JOURNAL OF PHARMACEUTICS LA English DT Article NR 20 SN 0378-5173 PU ELSEVIER SCIENCE BV C1 INT SPECIALTY PROD,1361 ALPS RD,WAYNE,NJ 07470 DE fluid-bed granulation; fractional factorial; lactose; normal plots; povidone; pseudo standard error; dispersion ID RESPONSE-SURFACE METHODOLOGY; WET GRANULATION; FACTORIALS AB Recent statistical/graphical methodology identifies the importance of bringing the mean to target while maintaining a low variance. Fluid-bed literature abounds in optimization strategies while overlooking product variability. In the present study, active factors (location effects) related to fluid-bed granulation of lactose with povidone (PVP) were identified using normal probability plotting and basing the slope on the calculated critical pseudo standard error value. An unreplicated fractional factorial design was employed. Residual-Fit spread plots indicated adequate empirical models through least squares. Dispersion analysis using computed residuals and resulting variances indicated spray rate binder state, and certain interactions effecting particle size variability. Additional runs confirmed the feasibility of the approach in maximizing crushing strength and minimizing particle size variability. Various factor effects could be attributed to the solubility of lactose during granulation. Variability due to spray rate was ascribed to the high inlet temperature and the surface area of the fluid-bed charge exposed to the binder solution. Rapid binder film formation promoted intragranular bonds and enhanced compactibility. Using simple empirical modeling and data visualization approaches, factors influencing product characteristics and variability were identified. The applicability of this approach in robust process development is discussed. CR BOX GEP, 1988, TECHNOMETRICS, V30, P1 BOX GEP, 1986, TECHNOMETRICS, V28, P1 BOX GEP, 1986, TECHNOMETRICS, V28, P19 CLEVELAND WS, 1959, TECHNOMETRICS, V1, P311 DAVIES WL, 1971, J PHARM SCI, V60, P1869 HAALAND PD, 1995, TECHNOMETRICS, V37, P82 KHATTAB I, 1993, J PHARM PHARMACOL, V45, P687 LENTH RV, 1989, TECHNOMETRICS, V31, P469 LIPPS DM, 1994, J PHARM SCI, V83, P937 MONTGOMERY DC, 1990, QUALITY ENG, V3, P193 OGAWA S, 1994, J PHARM SCI, V83, P439 REKHI G, 1994, PHARMACEUT RES, V11, PS142 SCHAEFER T, 1978, ARCH PHARM CHEMI SCI, V6, P69 SCHAEFER T, 1977, ARCH PHARM CHEMI SCI, V5, P51 SCHWARTZ JB, 1973, J PHARM SCI, V62, P1165 SCHWARTZ JB, 1990, MODERN PHARM, P803 VALAZZA M, 1994, PHARM TECH C P, P442 WAN LSC, 1989, STP PHARM, V5, P244 WEHRLE P, 1993, DRUG DEV IND PHARM, V19, P1983 WURSTER DE, 1959, J AM PHARM ASSOC SCI, V48, P451 TC 4 BP 207 EP 218 PG 12 JI Int. J. Pharm. PY 1996 PD AUG 30 VL 140 IS 2 GA VH369 PI AMSTERDAM RP Menon A INT SPECIALTY PROD,1361 ALPS RD,WAYNE,NJ 07470 J9 INT J PHARM PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1996VH36900008 ER PT Journal AU Shelly, MA TI Exploratory data analysis: Data visualization or torture? SO INFECTION CONTROL AND HOSPITAL EPIDEMIOLOGY LA English DT Article NR 12 SN 0899-823X PU SLACK INC C1 HIGHLAND HOSP,1000 SOUTH AVE,BOX 45,ROCHESTER,NY 14620 UNIV ROCHESTER,ROCHESTER,NY ID BOX AB Exploratory Data Analysis offers a set of graphical and statistical tools to find the full meaning from data sets. The user visualizes, analyzes, and transforms data distributions with these tools. Graphs reveal relationships between variables; the residuals left after fitting data show the adequacy of the model. Without this careful examination and understanding of the data, rote data analysis using standard statistical tests can give misleading results. Exploratory Data Analysis has its own set of pitfalls and must be used with confirmatory statistics and studies. Increasing power and resolution in personal computers enables modern statistical software to make these methods widely accessible. By easily moving between data and their graphic representation, analysis can be comprehensive without being tedious. Exploratory Data Analysis can add an exciting and useful to ol to the epidemiologist's repertoire. This article illustrates several tools from an evolving list. 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PY 1996 PD SEP VL 17 IS 9 GA VG245 PI THOROFARE RP Shelly MA HIGHLAND HOSP,1000 SOUTH AVE,BOX 45,ROCHESTER,NY 14620 J9 INFECT CONTROL HOSP EPIDEMIOL PA 6900 GROVE RD, THOROFARE, NJ 08086 UT ISI:A1996VG24500011 ER PT Journal AU Kangas, J Kohonen, T TI Developments and applications of the self-organizing map and related algorithms SO MATHEMATICS AND COMPUTERS IN SIMULATION LA English DT Article NR 32 SN 0378-4754 PU ELSEVIER SCIENCE BV C1 HELSINKI UNIV TECHNOL,NEURAL NETWORKS RES CTR,RAKENTAJANAUKIO 2 C,FIN-02150 ESPOO,FINLAND AB In this paper the basic principles and developments of an unsupervised learning algorithm, the self-organizing map (SOM) and a supervised learning algorithm, the learning vector quantization (LVQ) are explained. Some practical applications of the algorithms in data analysis, data visualization and pattern recognition tasks are mentioned. At the end of the paper new results are reported about increased error tolerance in the transmission of vector quantized images, provided by the topological ordering of codewords by the SOM algorithm. 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Comput. Simul. PY 1996 PD JUN VL 41 IS 1-2 GA UU018 PI AMSTERDAM RP Kangas J HELSINKI UNIV TECHNOL,NEURAL NETWORKS RES CTR,RAKENTAJANAUKIO 2 C,FIN-02150 ESPOO,FINLAND J9 MATH COMPUT SIMULAT PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:A1996UU01800002 ER PT Journal AU Meyers, S Mills, E Chen, A Demsetz, L TI Building data visualization for diagnostics SO ASHRAE JOURNAL-AMERICAN SOCIETY OF HEATING REFRIGERATING AND AIR-CONDITIONING ENGINEERS LA English DT Article NR 8 SN 0001-2491 PU AMER SOC HEAT REFRIG AIR- CONDITIONING ENG INC C1 UNIV CALIF BERKELEY,LAWRENCE BERKELEY LAB,CTR BLDG SCI,BERKELEY,CA 94720 CR DEALMEIDA AT, 1994, LBL32941 HEIEMEIER K, 1993, P NAT C BUILD COMM P HEINEMEIER K, 1992, P ACEEE 1992 SUMM ST, V3 KAPLAN M, 1994, P 2 NAT C BUILD COMM LIU M, 1994, P HE ACEEE 1994 SUMM, V5 LOVINS AB, 1992, ENERGY EFFICIENT BUI MILLS E, 1994, INT ASS ENERGY EFFIC, V3, P1 MILLS E, P 1994 ACEEE SUMM ST TC 0 BP 63 EP & PG 8 JI ASHRAE J.-Am. Soc. Heat Refrig. Air-Cond. Eng. PY 1996 PD JUN VL 38 IS 6 GA UQ757 PI ATLANTA RP Meyers S UNIV CALIF BERKELEY,LAWRENCE BERKELEY LAB,CTR BLDG SCI,BERKELEY,CA 94720 J9 ASHRAE J-HEAT REFRIG AIR-COND PA 1791 TULLIE CIRCLE NE, ATLANTA, GA 30329 UT ISI:A1996UQ75700018 ER PT Journal AU Rao, AR Lohse, GL TI Towards a texture naming system: Identifying relevant dimensions of texture SO VISION RESEARCH LA English DT Article NR 38 SN 0042-6989 PU PERGAMON-ELSEVIER SCIENCE LTD C1 IBM CORP,THOMAS J WATSON RES CTR,YORKTOWN HTS,NY 10598 UNIV PENN,WHARTON SCH,PHILADELPHIA,PA 19104 DE human texture perception; classification; feature extraction; image databases; visualization; repetition; randomness; directionality ID PERCEPTION; FEATURES; FIELDS AB Recently, researchers have started using texture for data visualization, The rationale behind this is to exploit the sensitivity of the human visual system to texture in order to overcome the limitations inherent in the display of multidimensional data, A fundamental issue that must be addressed is what textural features are important in texture perception, and how they are used, We designed an experiment to help identify the relevant higher order features of texture perceived by humans, We used twenty subjects, who were asked to rate 56 pictures from Brodatz's album on 12 nine-point Likert scales. Each subject was also asked to group these pictures into as many classes as desired, We applied the techniques of hierarchical cluster analysis and non-parametric multidimensional scaling (MDS) to the pooled similarity matrix generated from the subjects' groupings, We used Classification and Regression Tree Analysis (CART), discriminant analysis, and principal component analysis on the data from the scale ratings, The clusters generated from hierarchical cluster analysis remained intact in the MDS plots, We found that the MDS solutions fit the data well, The stress in the three- dimensional case is 0.12, The CART and discriminant analyses provided further justification for our interpretation, The three orthogonal dimensions we identified for texture are repetitive vs non-repetitive; high-contrast and non-directional vs low-contrast and directional; granular, coarse and low- complexity vs non-granular, fine and high-complexity. Copyright (C) 1996 Elsevier Science Ltd. CR AHUJA N, 1981, IEEE T PATTERN ANAL, V3, P1 AMADASUN M, 1989, IEEE T SYST MAN CYB, V19, P1264 BECK J, 1983, THEORY TEXTURAL SEGM BERK T, 1982, IEEE COMPUT GRAPH, V2, P37 BREIMAN L, 1984, CLASSIFICATION REGRE BRODATZ P, 1966, TEXTURES PHOTOGRAPHI CHOU P, 1993, SPIE C MACHINE VISIO, V1907 CUCCU F, 1993, COMPUT GRAPH, V17, P131 DILLON WR, 1984, MULTIVARIATE ANAL ME FRANCOS J, 1991, VISUAL COMMUNICATION, V1666, P554 HARALICK RM, 1973, IEEE T SMC, V6, P610 HARALICK RM, 1979, P IEEE, V67, P786 HORN GF, 1974, TEXTURE DESIGN ELEME JULESZ B, 1983, BELL SYST TECH J, V62, P1619 KRUSKAL JB, 1978, MULTIDIMENSIONAL SCA KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P1 LOHSE G, 1991, BEHAV INFORM TECHNOL, V10, P419 LOHSE G, 1990, VISUALIZATION 90, P131 NIBLACK W, 1993, P SPIE C STOR RETR I, V1908 PICARD RW, 1993, P IEEE C COMPUTER VI, P638 PICKETT RM, 1990, IEEE C VISUALIZATION RAO AR, 1993, 19140 IBM RC RAO AR, 1993, CVGIP-GRAPH MODEL IM, V55, P218 RAO AR, 1991, CVGIP-GRAPH MODEL IM, V53, P157 RAO AR, 1992, IEEE T PATTERN ANAL, V14, P693 RAO AR, 1992, SPIE C HUMAN VISION, V3, P424 RAO AR, 1990, TAXONOMY TEXTURE DES ROGOWITZ B, 1992, HUMAN VISION VISUA 3, V1666, P504 SHEPARD RN, 1962, PSYCHOMETRIKA, V27, P125 SHEPARD RN, 1962, PSYCHOMETRIKA, V27, P219 TAMURA H, 1978, IEEE T SYST MAN CYB, V8, P460 TOMITA F, 1982, IEEE T PATTERN ANAL, V4, P183 VANDYKE M, 1982, ALBUM FLUID MOTION VILNROTTER FM, 1986, IEEE T PATTERN ANAL, V8, P76 VOORHEES H, 1987, 1ST P INT C COMP VIS, P250 WARD JH, 1963, J AM STAT ASSOC, V53, P236 WARE C, 1992, ACM C HUMAN FACTORS, P203 WIJK JV, 1991, COMPUT GRAPH, V25, P309 TC 11 BP 1649 EP 1669 PG 21 JI Vision Res. PY 1996 PD JUN VL 36 IS 11 GA UQ365 PI OXFORD RP Rao AR IBM CORP,THOMAS J WATSON RES CTR,YORKTOWN HTS,NY 10598 J9 VISION RES PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, ENGLAND OX5 1GB UT ISI:A1996UQ36500013 ER PT Journal AU [Anon] TI Demonstration package and brochure highlights numerical analysis and data visualization capabilities HiQ software SO LUBRICATION ENGINEERING LA English DT News Item NR 0 SN 0024-7154 PU SOC TRIBOLOGISTS & LUBRICATION ENGINEERS TC 0 BP 378 EP 378 PG 1 JI Lubric. Eng. PY 1996 PD MAY VL 52 IS 5 GA UL028 PI PARK RIDGE J9 LUBRIC ENG PA 838 BUSSE HIGHWAY, PARK RIDGE, IL 60068 UT ISI:A1996UL02800006 ER PT Journal AU Socha, DG Watkins, JL Brierley, AS TI A visualization-based post-processing system for analysis of acoustic data SO ICES JOURNAL OF MARINE SCIENCE LA English DT Article NR 11 SN 1054-3139 PU ACADEMIC PRESS LTD C1 BRITISH ANTARCTIC SURVEY,CAMBRIDGE CB3 0ET,ENGLAND DE acoustic analysis; AVS; echo-sounders; post-processing; SIMRAD EK500; visualization AB A commercial data-visualization package, AVS, and database are used as the basis for a powerful and highly flexible acoustic data analysis system. The system is easy to use and can be modified by the user to incorporate novel visualization and analysis capabilities as required. Multi-Frequency ping-by-ping or integrated data from a variety of echo-sounders may be viewed and manipulated within the system. Here, we describe the main features of the system and illustrate how it may be used to mark, transform, analyse, and compare dual-frequency acoustic data. (C) 1996 International Council for the Exploration of the Sea CR CHEU D, 1990, SQL LANGUAGE REFEREN DAWSON JJ, 1989, P I ACOUST, V11, P131 ELLIS MA, 1990, ANNOTATED C PLUS PLU FOOTE KG, 1991, J ACOUST SOC AM, V90, P37 HOLLIDAY DV, 1989, J CONSEIL, V46, P52 KUNDSEN HP, 1990, J CONSEIL INT EXPLOR, V47, P167 MACLENNAN DN, 1990, J ACOUST SOC AM, V87, P1 MADUREIRA LSP, 1993, J PLANKTON RES, V15, P787 MADUREIRA LSP, 1993, MAR ECOL-PROG SER, V93, P17 UPSON C, 1989, IEEE COMPUT GRAPH, V9, P30 WATKINS JL, 1996, ICES J MAR SCI, V53, P339 TC 7 BP 335 EP 338 PG 4 JI ICES J. Mar. Sci. PY 1996 PD APR VL 53 IS 2 GA UJ679 PI LONDON RP BRITISH ANTARCTIC SURVEY,CAMBRIDGE CB3 0ET,ENGLAND J9 ICES J MAR SCI PA 24-28 OVAL RD, LONDON, ENGLAND NW1 7DX UT ISI:A1996UJ67900034 ER PT Journal AU Eick, SG Lucas, PJ TI Displaying trace files SO SOFTWARE-PRACTICE & EXPERIENCE LA English DT Article NR 12 SN 0038-0644 PU JOHN WILEY & SONS LTD C1 AT&T BELL LABS,ROOM 1G-351,1000 E WARRENVILLE RD,NAPERVILLE,IL 60566 DE software visualization; log files; log file visualization; interactive command usages; graphics data visualization AB Computers generate trace files containing reports on system performance, status and faults. To analyze these trace files more efficiently, we have developed a graphical technique embodied in an interactive system for displaying large trace files. Our system uses abstraction, color, aggregation, filtering, interaction, and a drill-down capability to find patterns among the reports. We apply our system and technique to analyze command accounting trace files from a Unix compute server, showing what commands were executed, by which users, when, and how long the commands ran. We identify resource intensive commands, sequences of commands initiated by a compilations, and commands run with super-user permissions. CR AHO AV, 1988, AWK PROGRAMMING LANG EICK SG, 1994, COMMUN ACM, V37, P50 EICK SG, 1992, IEEE T SOFTWARE ENG, V18, P957 EICK SG, 1994, INT 94 C P RAL N CAR, P143 EICK SG, 1993, VISUALIZATION 93 C P, P204 HANSON SJ, 1984, ACM T OFFIC INFORM S, V2, P42 HEATH MT, 1991, IEEE SOFTWARE SEP, P29 KRAUT RE, 1983, P CHI 83 C HUMAN FAC, P120 REED DA, 1993, P SCAL PAR LIB C, P104 SCHEIFLER RW, 1983, ACM T GRAPHIC, V5, P57 WALL L, 1990, PROGRAMMING PERL WAUGY W, 1993, COMMUNICATION TC 2 BP 399 EP 409 PG 15 JI Softw.-Pract. Exp. PY 1996 PD APR VL 26 IS 4 GA UG360 PI W SUSSEX RP Eick SG AT&T BELL LABS,ROOM 1G-351,1000 E WARRENVILLE RD,NAPERVILLE,IL 60566 J9 SOFTWARE-PRACT EXP PA BAFFINS LANE CHICHESTER, W SUSSEX, ENGLAND PO19 1UD UT ISI:A1996UG36000002 ER PT Journal AU Asahi, T Turo, D Shneiderman, B TI Using treemaps to visualize the Analytic Hierarchy Process SO INFORMATION SYSTEMS RESEARCH LA English DT Article NR 16 SN 1047-7047 PU INST OPERATIONS RESEARCH MANAGEMENT SCIENCES C1 UNIV MARYLAND,HUMAN COMP INTERACT LAB,COLLEGE PK,MD 20740 UNIV MARYLAND,DEPT COMP SCI,COLLEGE PK,MD 20740 UNIV MARYLAND,SYST RES INST,COLLEGE PK,MD 20740 DE visualization; treemap; Analytic Hierarchy Process; AHP; decision support; user interfaces AB Treemaps, a visualization method for large hierarchical data spaces, are used to augment the capabilities of the Analytic Hierarchy Process (AHP) for decision-making. Two direct manipulation tools, presented metaphorically as a ''pump'' and a ''hook,'' were developed and applied to the treemap to support AHP sensitivity analysis, Users can change the importance of criteria dynamically on the two-dimensional treemap and immediately see the impact on the outcome of the decision, This fluid process dramatically speeds up exploration and provides a better understanding of the relative impact of the component criteria, A usability study with six subjects using a prototype AHP application showed that treemap representation was acceptable from a visualization and data operation standpoint. CR CHIN JP, 1988, P CHI 88 HUM FACT CO, P213 FINNIE GR, 1983, J SYST SOFTWARE, V22, P129 FORMAN EH, 1989, EXPERT CHOICE SOFTWA JUNGMEISTER W, 1992, CARTR648 U MAR DEP C JUNGMEISTER W, 1992, CSTR2996 U MAR DEP C JUNGMEISTER W, 1992, SRCTR92120 U MAR DEP SAATY TL, 1980, ANAL HIERARCHY PROCE SAATY TL, 1982, DECISION MAKING LEAD SAATY TL, 1991, LOGIC PRIORITIES SHNEIDERMAN B, 1992, ACM T GRAPHIC, V11, P92 SHNEIDERMAN B, 1992, DESIGNING USER INTER TONE K, 1989, ANAL HIERARCHY PROCE, P242 TONE K, 1990, GAME LIKE DECISION M TURO D, 1992, P VIS 92, P124 TURO D, 1993, THESIS U MARYLAND ZAHEDI F, 1986, INTERFACES, V16, P96 TC 3 BP 357 EP 375 PG 19 JI Inf. Syst. Res. PY 1995 PD DEC VL 6 IS 4 GA UF476 PI LINTHICUM HTS RP UNIV MARYLAND,HUMAN COMP INTERACT LAB,COLLEGE PK,MD 20740 J9 INF SYSTEMS RES PA 901 ELKRIDGE LANDING RD, STE 400, LINTHICUM HTS, MD 21090-2909 UT ISI:A1995UF47600004 ER PT Journal AU Halfon, E TI Volume visualization of temperature in Hamilton Harbour, Lake Ontario SO JOURNAL OF GREAT LAKES RESEARCH LA English DT Article NR 33 SN 0380-1330 PU INT ASSOC GREAT LAKES RES C1 ENVIRONM CANADA,CANADA CTR INLAND WATERS,BURLINGTON,ON L7R 4A6,CANADA DE Lake Ontario; temperature; oxygen; 3D data visualization ID SURFACES; DISPLAY AB The inner structures of lakes can be revealed using volume visualization algorithms since lakes are three-dimensional objects that are explored by taking samples at various stations and at different depths. These algorithms did not exist 20 years ago, they could only be run on supercomputers 10 years ago, on workstations 3 years ago, and now they can be run on personal computers. Using computer graphics it is now possible to combine data, their three-dimensional location, and lake topography to create images of water quality patterns which supersede conventional surface, two-dimensional, graphics. Through solid modeling, temperature data collected on 28 May 1990 and 8 August 1990 in Hamilton Harbour, Lake Ontario, are mapped into voxels and projected onto two-dimensional screens. Various three-dimensional representations of temperature data are displayed including water masses with temperatures of less than 12 degrees C, 13 to 14 degrees C, 16 degrees C to 17 degrees C, and greater than 23 degrees C. The calculation of the 3D representations allows the computation of volumetric properties, e.g., masses, since each voxel has water quality values associated with it and these values can be summed or elaborated numerically as needs arise. For example the harbor has a volume of 254 x 10(6) m(3), and the water mass on 28 May 1990 at 12-13 degrees C had a complex three-dimensional shape with a volume of 61 x 10(6) m(3). A third benefit of visualization is that the data can be viewed interactively from different viewpoints thus increasing the interaction between scientist and the data. These methods should also be able to be used in other limnological applications such as visualization of sediments, algal blooms, and other biological and chemical data. 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Gt. Lakes Res. PY 1996 VL 22 IS 1 GA UE692 PI ANN ARBOR RP Halfon E ENVIRONM CANADA,CANADA CTR INLAND WATERS,BURLINGTON,ON L7R 4A6,CANADA J9 J GREAT LAKES RES PA 2200 BONISTEEL BLVD, ANN ARBOR, MI 48109-2099 UT ISI:A1996UE69200003 ER PT Journal AU Matanga, GB TI Stream and pseudopotential functions in visualizing groundwater flow and transport processes SO WATER RESOURCES RESEARCH LA English DT Article NR 12 SN 0043-1397 PU AMER GEOPHYSICAL UNION C1 RADIAN CORP,10339 OLD PLACEVILLE RD,SACRAMENTO,CA 95827 ID DIRECTION GALERKIN TECHNIQUE; CONTAMINANT TRANSPORT; SIMULATION AB Scientific visualization is increasingly being applied in many large-scale groundwater modeling efforts as an effective means of presentation and interpretation of model results. An interpolation or statistical approach is applied to develop three-dimensional spatial distributions of geologic, hydraulic, and chemical data from a model or field measurements. The distributions become the basis for evaluating spatial variation of data. The evaluation is accomplished by displaying data in the form of isosurfaces of values of data or as contours of data on a surface or plane. This approach of analyzing data is known as data visualization. In addition to data visualization, some of the problems encountered in groundwater hydrology require visualization of groundwater flow and transport processes. Display of hydraulic and chemical data for analysis of groundwater flow and transport processes is herein referred to as process visualization. In both data and process visualization, hydraulic and chemical data are displayed as color contours or isolines on surfaces. However, in data visualization the surface on which data are displayed may be oriented in any direction, whereas in process visualization the surfaces need to be tangential or orthogonal to the direction of groundwater flow. In three-dimensional groundwater flow, stream surfaces and pseudopotential surfaces are tangential and orthogonal, respectively, to the direction of groundwater flow. Therefore stream and pseudopotential surfaces provide natural platforms on which to visualize groundwater flow and transport processes. To demonstrate application of stream and pseudopotential surfaces in process visualization, the three- dimensional groundwater flow beneath the Borden Landfill is considered. CR BEAR J, 1987, MODELING GROUNDWATER BURNETT RD, 1987, WATER RESOUR RES, V23, P683 DAUS AD, 1985, WATER RESOUR RES, V21, P653 FREEZE RA, 1979, GROUNDWATER MACFARLANE DS, 1980, HYDROGEOLOGIC STUDIE MACFARLANE DS, 1983, J HYDROL, V63, P1 MATANGA GB, 1996, J HYDROL ENG AM SOC, V1, P49 MATANGA GB, 1993, WATER RESOUR RES, V29, P3125 MATANGA GB, 1988, WATER RESOUR RES, V24, P553 MOLSON JWH, 1988, THESIS U WATERLOO WA SRIVASTAVA R, 1992, ADV WATER RESOUR, V15, P275 ZACHMANN D, 1994, GROUNDW MOD C WORKSH TC 1 BP 953 EP 957 PG 5 JI Water Resour. Res. PY 1996 PD APR VL 32 IS 4 GA UC939 PI WASHINGTON RP Matanga GB RADIAN CORP,10339 OLD PLACEVILLE RD,SACRAMENTO,CA 95827 J9 WATER RESOUR RES PA 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 UT ISI:A1996UC93900017 ER PT Journal AU Slatt, RM Thomasson, MR Romig, PR Pasternack, ES Boulanger, A Anderson, RN Nelson, HR TI Visualization technology for the oil and gas industry: Today and tomorrow SO AAPG BULLETIN-AMERICAN ASSOCIATION OF PETROLEUM GEOLOGISTS LA English DT Article NR 0 SN 0149-1423 PU AMER ASSN PETROL GEOLOGISTS C1 COLORADO SCH MINES,DEPT GEOL & GEOL ENGN,GOLDEN,CO 80401 THOMASSON PARTNER & ASSOCIATES,DENVER,CO 80202 COLORADO SCH MINES,DEPT GEOPHYS,GOLDEN,CO 80401 ARCO INT OIL & GAS CO,PLANO,TX 75075 COLUMBIA UNIV,LAMONT DOHERTY EARTH OBSERV,PALISADES,NY 10964 HYPERMEDIA CORP,HOUSTON,TX 77079 AB The fifth Archie Conference, ''Visualization Technology to Find and Develop More Oil and Gas,'' brought together 130 scientists and technologists to review current and future visualization technologies that are being developed and used in the petroleum and other industries. Visualization in the oil and gas industry can be considered a tool for characterizing and understanding surface and subsurface phenomena. In addition to allowing one to view and more easily understand large quantities of data, visualization is dramatically enhancing communications, and thus interaction, among members of integrated exploration and development teams. Current and potential end-users of visualization technology consider the most important aspects to include common formats for data interchange, greater availability to consultants and independents (perhaps through PC-based visualization hardware and software), bigger bandwidth capabilities to drive more powerful machines, and the application of sensitivity analysis to document uncertainty in visualizations at all scales. Visualization technology is in its infancy, but growing so rapidly that it promises to have major impact on many aspects of the petroleum industry, from improved day-to-day communications to better technology transfer and more powerful interpretive capabilities, all of which can ultimately lead to better economic decision making. TC 2 BP 453 EP 459 PG 7 JI AAPG Bull.-Am. Assoc. Petr. Geol. PY 1996 PD APR VL 80 IS 4 GA UC859 PI TULSA RP Slatt RM COLORADO SCH MINES,DEPT GEOL & GEOL ENGN,GOLDEN,CO 80401 J9 AAPG BULL-AMER ASSN PETROL G PA 1444 S BOULDER AVE PO BOX 979, TULSA, OK 74101 UT ISI:A1996UC85900002 ER PT Journal AU Naim, O Hey, T Zaluska, E TI Do-loop-surface: An abstract representation of parallel program performance SO CONCURRENCY-PRACTICE AND EXPERIENCE LA English DT Article NR 39 SN 1040-3108 PU JOHN WILEY & SONS LTD C1 UNIV SOUTHAMPTON,DEPT ELECTR & COMP SCI,SOUTHAMPTON SO17 1BJ,HANTS,ENGLAND ID VISUALIZATION AB Performance is a critical issue in current massively parallel processors, However, delivery of adequate performance is not automatic and performance evaluation tools are required in order to help the programmer to understand the behaviour of a parallel program, In recent years, a wide variety of tools have been developed for this purpose, including tools for monitoring and evaluating performance and visualization tools, However, these tools do not provide an abstract representation of performance, Massively parallel processors can generate a huge amount of performance data, and sophisticated methods for representing and displaying these data (e.g. visual and aural) are required, Performance views are not scalable in general and do not represent an abstraction of the performance data. The do-loop-surface display is proposed as an abstract representation of the performance of a particular do-loop in a program, It has been used to improve the performance of a matrix multiply parallel algorithm as well as to understand the behaviour of the following applications: matrix transposition (TRANS1) and fast fourier transform (FFT1) from the Genesis benchmarks, and the kernel of a fluid dynamics package (FIRE). These experiments were performed on a CM-5, a Meiko CS-1 and a PARSYS Supernode, The examples demonstrate that the do-loop- surface display is a useful way to represent performance. It is implemented using AVS (application visualization system), a standard data visualization package. CR 1993, CS93214 1991, PA TOOLS PERFORMANCE *ADV VIS SYST INC, 1992, AVS US GUID REL 4 *PAR CORP, 1990, PAR EXPR US GUID ADDISON C, 1993, CONCURRENCY-PRACT EX, V5, P1 ALMASI G, 1991, 2 S HIGH PERF COMP O, P195 BODE A, 1992, WORKSH PERF MEAS VIS BRANDES T, 1993, RAPS OP WORKSH PAR B CARTER L, 1993, E COMMUNICATION JAN COUCH AL, 1993, J PARALLEL DISTR COM, V18, P195 DUNLOP A, 1994, UNPUB SCI PROGRAM FRANCIONI J, 1991, P 6 DISTR MEM COMP C FRANCIONI J, 1992, SCAL HIGH PERF COMP, P433 FRANCIONI JM, 1991, SIGPLAN NOTICES, V26, P68 GEIST A, 1993, ORNLTM12187 GETOV V, 1993, GENESIS DISTRIBUTED GOLDBERG AJ, 1993, IEEE T PARALL DISTR, V4, P28 HEATH M, 1992, WORKSH PERF MEAS VIS HEATH MT, 1991, IEEE SOFTWARE SEP, P29 HEMPEL R, 1991, ANL GMD MACROS PARMA HOARE CAR, 1985, COMMUNICATING SEQUEN LEBLANC TJ, 1990, J PARALLEL DISTR COM, V9, P203 MALONY A, 1992, WORKSH PERF MEAS VIS MALONY AD, 1992, IEEE T PARALL DISTR, V3, P433 MERLIN J, 1993, HPF VISUALIZATION TO MILLER B, 1990, IEEE T PARALL DISTR, P206 MILLER BP, 1993, J PARALLEL DISTR COM, V18, P265 MOHR B, 1993, E COMMUNICATION MAR MOHR B, 1991, EDMCC2 MUN APR NAIM O, 1994, HPCC94003 U SOUTH DE NAIM O, 1994, HPCN EUR 94 MUN NAIM O, 1994, IFIP TRANS A, V44, P319 NAIM O, 1993, PERFORMANCE ANAL PAR NAIM O, 1993, TRANSPUTER OCCAM RES, V33, P91 REED D, 1992, PABLO PERFORMANCE AN ROVER DT, 1993, J PARALLEL DISTR COM, V18, P129 SARUKKAI S, 1992, SCAL HIGH PERF COMP, P424 SIMMONS M, 1990, FRONTIER SERIES WYLIE B, 1992, EPCCTN920504 U ED TC 1 BP 205 EP 234 PG 30 JI Concurrency-Pract. Exp. PY 1996 PD APR VL 8 IS 3 GA UB836 PI W SUSSEX RP UNIV SOUTHAMPTON,DEPT ELECTR & COMP SCI,SOUTHAMPTON SO17 1BJ,HANTS,ENGLAND J9 CONCURRENCY-PRACT EXPER PA BAFFINS LANE CHICHESTER, W SUSSEX, ENGLAND PO19 1UD UT ISI:A1996UB83600003 ER PT Journal AU Domine, D Devillers, J Wienke, D Buydens, L TI The heuristic potency of art networks for QSAR data visualization and interpretation. SO ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY LA English DT Meeting Abstract NR 0 SN 0065-7727 PU AMER CHEMICAL SOC C1 CTIS,F-69003 LYON,FRANCE CATHOLIC UNIV NIJMEGEN,ANALYT CHEM LAB,6525 ED NIJMEGEN,NETHERLANDS TC 0 BP 108 EP COMP PG 1 JI Abstr. Pap. Am. Chem. Soc. PY 1996 PD MAR 24 VL 211 PN 1 GA UA482 PI WASHINGTON RP CTIS,F-69003 LYON,FRANCE J9 ABSTR PAP AMER CHEM SOC PA 1155 16TH ST, NW, WASHINGTON, DC 20036 UT ISI:A1996UA48202299 ER PT Journal AU Morhac, M Matousek, V Turzo, I TI Multiparameter data acquisition and analysis system with on- line compression SO IEEE TRANSACTIONS ON NUCLEAR SCIENCE LA English DT Article NR 12 SN 0018-9499 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 SLOVAK ACAD SCI,INST PHYS,DUBRAVSKA CESTA 9,BRATISLAVA 84228,SLOVAKIA AB A modular system to determine correlations among many detectors in radiation measurements is described. It is based on PC computer and supports several types of CAMAC crate controllers. Besides standard techniques and procedures such as data taking, event building, event sorting, also sophisticated data visualization, processing, and a new efficient method of multidimensional data analysis with on-line compression have been implemented in the system. CR AHMED N, 1975, ORTHOGONAL TRANSFORM FLIBOTTE S, 1992, NUCL INSTRUM METH A, V320, P325 HLAVAC S, 1994, C NUCL DAT SCI TECHN HLAVAC S, 1994, CROSS SECTIONS 16 O KRISTIAK J, 1993, J PHYS IV, V3, P265 KRISTIAKOVA K, 1992, P SLOW POS BEAM TECH, P150 MORHAC M, 1995, IEEE T NUCL SCI, V42, P1 MORHAC M, 1994, MULTIPARAMETER DAT 1 MORHAC M, 1994, MULTIPARAMETER DAT 2 MORHAC M, 1993, P 8 C REAL TIM COMP, P220 MORHAC M, 1995, SIGNAL PROCESSING, V43 SOUCEK B, 1972, MINICOMPUTERS DATA P TC 4 BP 140 EP 148 PG 9 JI IEEE Trans. Nucl. Sci. PY 1996 PD FEB VL 43 IS 1 PN 1 GA TX791 PI NEW YORK RP Morhac M SLOVAK ACAD SCI,INST PHYS,DUBRAVSKA CESTA 9,BRATISLAVA 84228,SLOVAKIA J9 IEEE TRANS NUCL SCI PA 345 E 47TH ST, NEW YORK, NY 10017-2394 UT ISI:A1996TX79100027 ER PT Journal AU Chen, H Hughes, DD Chan, TA Sedat, JW Agard, DA TI IVE (Image Visualization Environment): A software platform for all three-dimensional microscopy applications SO JOURNAL OF STRUCTURAL BIOLOGY LA English DT Article NR 6 SN 1047-8477 PU ACADEMIC PRESS INC JNL-COMP SUBSCRIPTIONS C1 UNIV CALIF SAN FRANCISCO,DEPT BIOCHEM & BIOPHYS,SAN FRANCISCO,CA 94143 UNIV CALIF SAN FRANCISCO,HOWARD HUGHES MED INST,SAN FRANCISCO,CA 94143 AB IVE (image Visualization Environment) is a software platform designed from the outset to handle all aspects of modern computerized multidimensional microscopy. This platform provides users with an execution environment in which 5D data (XYZ, wavelength, and time) can be easily manipulated for the purpose of data collection, processing, display, and analysis. During the entire process, powerful data display functions are readily available for extracting complicated three-dimensional information through data visualization. By employing both the shared memory and multitasking features of the UNIX operation system, individual functions can be implemented as separate programs, and multiple programs can access the same data pool simultaneously. This enables users to combine the functionalities of different programs to facilitate each unique data analysis task. Furthermore, by defining an appropriate program execution model, commonly shared functional components such as data display, data I/O and user interface, etc. can be implemented using simple IVE library calls. This dramatically reduces the program development time and ensures consistency throughout the entire software system. As a result, users can quickly master the microscopy software system and new functions can be easily integrated, as different functional requirements arise for different research projects. (C) 1996 Academic Press, Inc. CR AGARD DA, 1989, METHOD CELL BIOL, V30, P353 ANDREWS HC, 1972, IEEE SPECTRUM, V9, P20 CASTLEMAN KR, 1979, DIGITAL IMAGE PROCES CHEN H, 1995, HDB BIOL CONFOCAL MI, P197 DREIBIN RA, 1988, COMPUT GRAPH, V22, P65 HOUTSMULLER AB, 1992, CYTOMETRY, V14, P501 TC 46 BP 56 EP 60 PG 5 JI J. Struct. Biol. PY 1996 PD JAN-FEB VL 116 IS 1 GA TX606 PI SAN DIEGO RP UNIV CALIF SAN FRANCISCO,DEPT BIOCHEM & BIOPHYS,SAN FRANCISCO,CA 94143 J9 J STRUCT BIOL PA 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 UT ISI:A1996TX60600010 ER PT Journal AU Eick, SG Fyock, DE TI Visualizing corporate data SO AT&T TECHNICAL JOURNAL LA English DT Article NR 9 SN 8756-2324 PU AT&T BELL LABORATORIES C1 AT&T BELL LABS,DATA VISUALIZAT RES GRP,NAPERVILLE,IL 60540 AT&T BELL LABS,ENTERPRISE SOFTWARE SOLUT DEPT,SPECIALIZED SOFTWARE SOLUT GRP,NAPERVILLE,IL 60540 AB Visualization is an emerging technology for understanding large, complex, information-rich data sets. Just as spreadsheets revolutionized our ability to understand small amounts of data, visualization is revolutionizing the way we understand large data sets. AT&T has developed a suite of applications, based on a common software infrastructure, to analyze strategic data sets and solve key business problems. Even as these software tools are being used internally, AT&T is also selling them in the commercial marketplace. The four case studies presented in this paper demonstrate the technology's general applicability and its use within AT&T to address strategic business problems and motivate its guiding research principles. CR COX KC, 1995, INF VIS S ATL, P129 EICK SG, 1994, J COMPUTATIONAL GRAP, V3, P127 ERICK SG, 1993, VIS 93 C P SAN JOS, P204 HADARY JJ, 1995, SYSTEMS REENGINEERIN, V2, P4 MADHYASTHA TM, 1995, IEEE SOFTWARE, V12, P45 ROCHKIND MJ, 1975, IEEE T SOFTWARE ENG, V1, P364 SMITH CR, 1995, INSIDE GARTNER 0816, P9 TICHY WF, 1985, SOFTWARE PRACT EXPER, V15, P637 TUFTE ER, 1983, VISUAL DISPLAY QUANT, P162 TC 5 BP 74 EP 86 PG 13 JI AT&T Tech. J. PY 1996 PD JAN-FEB VL 75 IS 1 GA TX187 PI MURRAY HILL RP Eick SG AT&T BELL LABS,DATA VISUALIZAT RES GRP,NAPERVILLE,IL 60540 J9 AT&T TECH J PA 600 MOUNTAIN AVE,RM 3C-417 CIRCULATION GROUP P O BOX 636, MURRAY HILL, NJ 07974-0636 UT ISI:A1996TX18700012 ER PT Journal AU Conners, SR TI Informing decision makers and identifying niche opportunities for windpower - Use of multiattribute trade off analysis to evaluate non-dispatchable resources SO ENERGY POLICY LA English DT Article NR 6 SN 0301-4215 PU BUTTERWORTH-HEINEMANN LTD C1 MIT,ENERGY LAB,77 MASSACHUSETTS AVE,ROOM E40-465,CAMBRIDGE,MA 02139 DE renewables; resource planning; environment AB The operational and flexibility characteristics of renewable energy technologies are often overlooked in traditional head to head technology comparisons, This impedes their adoption since identification of environmental and risk mitigation advantages requires evaluation of such nondispatchable technologies in a systemwide context, Use of multiattribute resource planning tools in a trade off analysis framework identifies the complementary emissions reduction and fuel diversification characteristics of renewables, Data visualization using trade off analysis communicates electric resource interactions and the risks of following various strategies to diverse stakeholder audiences, promoting acceptance. This paper provides an overview of the multiattribute trade off approach and applies it to resource strategies incorporating windpower in the New England regional power system, Examples focus on the interaction of wind resources with demand-side management and supply-side options under fuel cost uncertainty. CR *AN GROUP REG EL A, 1993, BACKGR INF 1992 1993 *EPRI, 1993, EPRI TR102275VIR7 RE, V1 *NEPL NEW ENGL POW, 1993, SUMM GEN TASK FORC L AWERBUCH S, 1995, ELECTRICITY J, V8, P50 CARDELL JB, 1994, THESIS MIT CAMBRIDGE CONNERS SR, 1993, P NATIONAL REGULATOR TC 2 BP 165 EP 176 PG 12 JI Energy Policy PY 1996 PD FEB VL 24 IS 2 GA TV923 PI OXFORD RP Conners SR MIT,ENERGY LAB,77 MASSACHUSETTS AVE,ROOM E40-465,CAMBRIDGE,MA 02139 J9 ENERG POLICY PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD, OXON, ENGLAND OX5 1GB UT ISI:A1996TV92300006 ER PT Journal AU Schmid, T Egger, O Kuster, N TI Automated E-field scanning system for dosimetric assessments SO IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES LA English DT Article NR 18 SN 0018-9480 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 SWISS FED INST TECHNOL,CH-8092 ZURICH,SWITZERLAND ID BIOLOGICAL BODIES; HEAD; SAR; MHZ AB The interest in accurate dosimetric measurements inside phantoms that simulate biological bodies has burgeoned since several regulatory commissions began calling for or recommending the testing for compliance with safety standards of low power devices. This paper presents a newly developed, robot-based system that allows automated E-field scanning in tissue simulating solutions. The distinguishing characteristics of the system are its high sensitivity and its broad dynamic range (1 mu W/g to 100 mW/g) over the entire frequency range (10 MHz to over 3 GHz) used for mobile communications. The reproducibility of the dosimetric evaluations has been shown to be considerably better than -/+5%. This has been accomplished by the use of an improved isotropic E-field probe connected to amplifiers with extremely low noise and drift characteristics in conjunction with digital processing of the data. Special emphasis has been placed on system reliability, user- friendliness and graphic visualization of data. CR 1992, ANSI IEEE C9511991 1994, CENELEC CLCSC111B 1994, FCC94144 TECH REP 1992, SCHUTZ ELEKTROMAGNET, P3 BASSEN HI, 1983, IEEE T ANTENN PROPAG, V31, P710 CLEVELAND RF, 1989, BIOELECTROMAGNETICS, V10, P173 DIMBYLOW PJ, 1994, PHYS MED BIOL, V39, P1537 DIMBYLOW PJ, 1993, PHYS MED BIOL, V38, P361 FLEMING AHJ, 1994, 14TH P ANN M BIOEL S GANDHI OP, 1994, 14TH P ANN M BIOEL S JENSEN MA, 1995, P IEEE, V83, P7 KUSTER N, 1992, ACES J, V7, P43 KUSTER N, 1993, IEEE T BIO-MED ENG, V40, P611 KUSTER N, 1992, IEEE T VEH TECHNOL, V41, P17 KUSTER N, 1993, VDE FACHBERICHT, V45, P135 MEIER K, 1995, 9TH P S EL COMP ZURI SCHMID T, UNPUB IEEE T VEH TEC SMITH GS, 1979, IEEE T MICROWAVE THE, V27, P270 TC 36 BP 105 EP 113 PG 9 JI IEEE Trans. Microw. Theory Tech. PY 1996 PD JAN VL 44 IS 1 GA TQ354 PI NEW YORK RP Schmid T SWISS FED INST TECHNOL,CH-8092 ZURICH,SWITZERLAND J9 IEEE TRANS MICROWAVE THEORY PA 345 E 47TH ST, NEW YORK, NY 10017-2394 UT ISI:A1996TQ35400016 ER PT Journal AU Mendelzon, A Sametinger, J TI Reverse engineering by visualizing and querying SO SOFTWARE-CONCEPTS AND TOOLS LA English DT Article NR 20 SN 0945-8115 PU SPRINGER VERLAG C1 TEXAS A&M UNIV,DEPT COMP SCI,COLLEGE STN,TX 77843 UNIV TORONTO,COMP SYST RES INST,TORONTO,ON M5S 1A1,CANADA DE reverse engineering; visualization; query; software metrics; constraints; design pattern; object-oriented programming; database; Hy+; GraphLog AB The automatic extraction of high-level structural information from code is important for both software maintenance and reuse. Instead of using special-purpose tools, we explore the use of a general-purpose data visualization system called Hy+ for querying and visualizing information about object-oriented software systems. Hy+ supports visualization and visual querying of arbitrary graph-like databases. We store information about software systems in a database and use Hy+ for analyzing the source code and visualizing various relationships. In this paper we demonstrate the use of Hy+ for evaluating software metrics, verifying constraints, and identifying design patterns. Software metrics can be used to find components with low reusability or components that are hard to understand. Checking the source code against constraints can help bring design flaws to light, eliminate sources of errors, and guarantee consistent style. Identifying design patterns in a software system can reveal design decisions and facilitate understanding the code. We conclude that the flexibility achieved by using a general-purpose system like Hy+ gives this approach advantages over special-purpose reverse-engineering tools, although specialized tools will have better performance and more knowledge of specific software engineering tasks. Combining the advantages of the two approaches is an interesting challenge. CR CHIDAMBER SR, 1991, P OOPSLA 91, P197 CHIKOFSKY EJ, 1990, IEEE SOFTWARE, V7, P13 CHOWDHURY A, 1993, 1993 IEEE C SOFTW MA COAD P, 1992, ACM, V35, P152 CONSENS M, 1990, 9TH P ACM SIGACT SIG, P404 CONSENS M, 1991, IBM TR74053 CAN LAB CONSENS MP, 1994, IBM SYSTEMS J AUG CONSENS MP, 1989, THESIS U TORONTO EIGLER FC, 1993, GXF GRAPH EXCHANGE F GAMMA E, 1995, DESIGN PATTERNS ELEM GAMMA E, 1993, EUROPEAN C OBJECTORI HAREL D, 1988, COMM ASS COMPUT MACH, V31, P514 MENDELZON AO, 1993, CSRI285 U TOR COMP S MEYERS S, 1993, CS9312 BROWN U TECHN MEYERS S, 1992, EFFECTIVE CPLUSPLUS MULLER HA, 1993, RES PRACTICE, V5, P181 PREE W, 1995, DESIGN PATTERNS OBJE SAMETINGER J, 1992, DOGMA TOOL DOCUMENTA TSALIDIS C, 1992, RES PRACTICE, V4, P61 WEINAND A, 1989, STRUCTURED PROGRAMMI, V10 TC 2 BP 170 EP 182 PG 13 JI Softw.-Concepts Tools PY 1995 VL 16 IS 4 GA TL396 PI NEW YORK RP TEXAS A&M UNIV,DEPT COMP SCI,COLLEGE STN,TX 77843 J9 SOFTWARE-CONCEPTS TOOLS PA 175 FIFTH AVE, NEW YORK, NY 10010 UT ISI:A1995TL39600003 ER PT Journal AU Searls, DB TI Bioinformatics tools for whole genomes SO ANNUAL REVIEW OF GENOMICS AND HUMAN GENETICS LA English DT Review NR 170 SN 1527-8204 PU ANNUAL REVIEWS C1 SmithKline Beecham Pharmaceut, Bioinformat Dept, King Of Prussia, PA 19406 USA SmithKline Beecham Pharmaceut, Bioinformat Dept, King Of Prussia, PA 19406 USA DE genome analysis; algorithms; databases ID PROTEIN-CODING REGIONS; SINGLE-NUCLEOTIDE POLYMORPHISMS; GENE- EXPRESSION; DNA-SEQUENCES; COMPUTATIONAL METHODS; MULTIPLE ALIGNMENTS; REGULATORY ELEMENTS; NONCODING SEQUENCES; DATA VISUALIZATION; BIOLOGICAL DATA AB The advent of whole-genome data resources-not only sequence but also other genome-scale data collections such as gene expression, protein interaction, and genetic variation-is having two marked, complementary effects on the relatively new discipline of bioinformatics. First, the veritable flood of data is creating a need and demand for new tools for dealing adequately with the deluge, and, second, the unprecedented extent, diversity, and impending completeness of the data sets are creating opportunities for new approaches to discovery based on computational methods. 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WOOTTON JC, 1993, COMPUT CHEM, V17, P149 XU Y, 1997, J COMPUT BIOL, V4, P325 ZHANG JH, 1997, GENOME RES, V7, P649 ZHANG MQ, 1998, GENOME RES, V9, P681 ZHANG MQ, 1997, P NATL ACAD SCI USA, V94, P565 ZHANG Z, 1998, NUCLEIC ACIDS RES, V26, P3986 TC 6 BP 251 EP 279 PG 29 JI Annu. Rev. Genomics Hum. Genet. PY 2000 VL 1 GA 381NG PI PALO ALTO RP Searls DB SmithKline Beecham Pharmaceut, Bioinformat Dept, King Of Prussia, PA 19406 USA J9 ANNU REV GENOMIC HUM GENET PA 4139 EL CAMINO WAY, PO BOX 10139, PALO ALTO, CA 94303-0139 USA UT ISI:000165768900011 ER PT Journal AU Philips, JE TI A program for speeding the processing and visualization of data SO AMERICAN LABORATORY LA English DT Article NR 0 SN 0044-7749 PU INT SCIENTIFIC COMMUN INC C1 Univ N Carolina Hosp, McLendon Clin Lab, 101 Manning Dr, Chapel Hill, NC 27514 USA Univ N Carolina Hosp, McLendon Clin Lab, Chapel Hill, NC 27514 USA AB Analysis of the amount of data produced by X-ray diffraction analysis is a daunting challenge. This article describes a software program that provides a significant increase in productivity by automating much of the data processing. With this software, graphs and tables are generated with the click of a mouse, doubling the productivity of the laboratory. TC 0 BP 13 EP + PG 4 JI Am. Lab. PY 2000 PD NOV VL 32 IS 22 GA 377RC PI SHELTON RP Philips JE Univ N Carolina Hosp, McLendon Clin Lab, 101 Manning Dr, Chapel Hill, NC 27514 USA J9 AMER LAB PA PO BOX 870, 30 CONTROLS DRIVE, SHELTON, CT 06484-0870 USA UT ISI:000165526300002 ER PT Journal AU Morse, E Lewis, M Olsen, KA TI Evaluating visualizations: using a taxonomic guide SO INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES LA English DT Article NR 22 SN 1071-5819 PU ACADEMIC PRESS LTD C1 Natl Inst Stand & Technol, 100 Bur Dr,Stop 8940, Gaithersburg, MD 20899 USA Univ Pittsburgh, Sch Informat Sci, Pittsburgh, PA 15260 USA Molde Coll, N-6401 Molde, Norway ID INFORMATION-RETRIEVAL; ALGORITHM AB Although visualizations are components of many information interfaces, testing of these visual elements is rarely undertaken except as a part of overall usability testing. For this reason, it is unclear what role, if any, visualizations actually perform. Our method involves the creation of simple visual prototypes and task sets based on a visual taxonomy which allows testing of the visualization in isolation from the rest of the system. By defining tests using a visual taxonomy rather than customary tasks from the application domain, our method circumvents the problems of restricting evaluation of newer more capable systems to only those tasks which might be accomplished with older, less capable ones. This paper will discuss methods for exhaustively testing the capabilities of a visualization by mapping from a domain-independent taxonomy of visual tasks to a specific domain, i.e. information retrieval. Experimental results are presented illustrating this approach to determining the role visualizations may play in supporting users in information-seeking environments. Our methods could easily be extended to other domains including data visualization. (C) 2000 Academic Press. CR AALBERSBERG IJ, 1996, COMMUNICATION CHALMERS M, 1996, P IEEE VIS 96 OCT NO, P127 HEARST MA, 1995, P CHI 9K, P213 HEMMJE M, 1994, P 17 ANN INT ACM SIG, P249 KENNEDY JB, 1996, SIGMOD REC, V25, P30 KIM H, 1994, P 1994 IEEE S VIS LA, P176 KOSHMAN S, 1997, THESIS U PITTSBURGH LIN X, 1997, J AM SOC INFORM SCI, V48, P40 LOHSE G, 1990, VISUALIZATION 90, P131 MARCHIONINI G, 1995, INFORMATION SEEKING MORSE E, 1999, EVALUATION VISUAL IN MORSE E, 1998, P IEEE INT C SYST MA, P1026 MORSE E, 1997, P IEEE INT C SYST MA NUCHPRAYOON A, 1996, THESIS U PITTSBURGH OLSEN KA, 1993, INFORM PROCESS MANAG, V29, P69 OLSEN KA, 1992, MULTIMEDIA REV, V3, P48 PEJTERSEN AM, 1988, TASKS ERRORS MENTAL, P171 ROGOWITZ BE, 1993, P IEEE C VIS 93 SAN, P236 SPOERRI A, 1993, P IEEE VIS 93, P150 WEHREND S, 1990, P IEEE VISUALIZATION, P139 WILLETT P, 1985, INFORM PROCESS MANAG, V21, P225 ZHOU M, 1998, P ACM C HUM FACT COM, P392 TC 0 BP 637 EP 662 PG 26 JI Int. J. Hum.-Comput. Stud. PY 2000 PD NOV VL 53 IS 5 GA 376ZZ PI LONDON RP Morse E Natl Inst Stand & Technol, 100 Bur Dr,Stop 8940, Gaithersburg, MD 20899 USA J9 INT J HUM-COMPUT STUDIES PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND UT ISI:000165488600002 ER PT Journal AU Nolle, A Pfister, G Seckmeyer, G Wilhelms, H Richards, ML Hartmann, GK TI DUST: An interactive data visualization tool for selected atmospheric data SO PHYSICS AND CHEMISTRY OF THE EARTH PART A-SOLID EARTH AND GEODESY LA English DT Article NR 8 SN 1464-1895 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Sci SoftCon, Burg 4, D-63477 Maintal, Germany Sci SoftCon, D-63477 Maintal, Germany Graz Univ, IGAM, A-8010 Graz, Austria Univ Hannover, Inst Meteorol, D-30419 Hannover, Germany WebFormatting, D-81827 Munich, Germany Max Planck Inst Aeron, D-37191 Katlenburg Lindau, Germany AB This paper describes the DUST-2 (Data Utilization software Tools) software, which is a possibility in visualizing and processing ozone and water vapor data of the Earth atmosphere as measured by satellite instruments (TOMS, COME, MAS) and provided by different data centers. In addition, a new search tool (S-4 toot) which allows to search for comparable ozone and water vapor data in four dimensions (location and time) within the DUST-2 data base and a hdf2csv conversion tool are represented. The software package as well as complementary information and data examples are published on CD-ROM ("Data Utilization Software Tools - 2", DUST-2, Hartmann et al. 2000) under ISBN 3-9804852-3-0 which is available via www.copernicus.org. (C) 2000 Published by Elsevier Science Ltd. All rights reserved. CR FARMAN JC, 1985, NATURE, V315, P207 HAHNE A, 1995, ESA B, V83 HARTMANN GK, 2000, DATA UTILIZATION SOF HARTMANN GK, 1996, GEOPHYS RES LETT, V23 MOLINA MJ, 1974, NATURE NOLLE A, 1999, DATA UTILIZATION SOF STOLARSKI RS, 1991, GEOPHYSICAL REV LETT, V18 WILHELMS H, 1998, 66 EUR COMM TC 0 BP 635 EP 638 PG 4 JI Phys. Chem. Earth Pt. A-Solid Earth Geod. PY 2000 VL 25 IS 8 GA 374YB PI OXFORD RP Nolle A Sci SoftCon, Burg 4, D-63477 Maintal, Germany J9 PHYS CHEM EARTH P A-SOLID E G PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000165371500007 ER PT Journal AU Park, J Park, SI TI Strain analysis and visualization: left ventricle of a heart SO COMPUTERS & GRAPHICS-UK LA English DT Article NR 23 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Korea Adv Inst Sci & Technol, Dept Elect Engn & Comp Sci, Div Comp Sci, 373-1 Kusong Dong Yusong Ku, Taejon 305701, South Korea Korea Adv Inst Sci & Technol, Dept Elect Engn & Comp Sci, Div Comp Sci, Taejon 305701, South Korea DE model validation and analysis; visualization; life and medical sciences ID 3-DIMENSIONAL MOTION; MECHANICS; SHAPE; MODEL AB Clinical utility of computational models is crucial in the applications of medical data visualization. Previously we have developed a new class of volumetric models whose parameters are functions in conjunction with physically based deformable modeling framework, and have applied the technique to estimate the left ventricular (LV) wall motion. We have successfully showed that the model parameter functions characterize the LV motion of normal and abnormal stares and that no further non- trivial post-processing is required for anatomically meaningful interpretation. In an effort to evaluate the LV model, this paper presents a method and results from a strain analysis based on the nodal displacements of the deformable LV model. Furthermore, in order to visualize the local quantities on the volumetric model for an effective analysis, we also developed a methodology to assist in assessing the cardiac function utilizing principal strains, Von-Mises' yield criteria, and a smoothing filter. Each strain tensor component,vas in the range of values observed in other reported studies. The application of a smoothing filter on the model improved in visualizing the overall trend of each strain variation. With our platform for a comprehensive strain analysis, we have augmented a clinical utility to the deformable models with parameter functions, (C) 2000 Elsevier Science Ltd. All rights reserved. CR ARTS T, 1993, ADV EXP MED BIOL, V346, P383 AXEL L, 1989, RADIOLOGY, V17, P841 BARDINET E, 1994, P IEEE WORKSH BIOM I, P184 BEYAR R, 1985, IEEE T BIO-MED ENG, V32, P764 CHEN CW, 1994, IEEE T PATTERN ANAL, V16, P342 FRIBOULET D, 1992, INT J CARDIAC IMAG, V8, P175 FUNG YF, 1976, FDN SOLID MECH GUCCIONE JM, 1995, J BIOMECH, V28, P1167 HUNTER PJ, 1988, PROG BIOPHYS MOL BIO, V52, P101 KASS M, 1988, INT J COMPUT VISION, V1, P321 MATHENY A, 1995, IEEE T PATTERN ANAL, V17, P967 MCINERNEY T, 1995, COMPUT MED IMAG GRAP, V19, P69 METAXAS D, 1993, IEEE T PATTERN ANAL, V15, P569 MOORE CC, 2000, RADIOLOGY, V214, P453 NASTAR C, 1996, IEEE T PATTERN ANAL, V18, P1069 PARK J, 1996, IEEE T MED IMAGING, V15, P278 PARK J, 1996, MED IMAGE ANAL J, V1, P1 PARK J, 1997, P INT SOC MAGN RES M, P147 PARK J, 1997, P SPIE INT S MED IM, P298 SHI P, 1995, P IEEE C COMP VIS PA, P687 SHOEMAKE K, 1992, P GRAPH INT 92 MAY, P151 YOUNG AA, 1992, RADIOLOGY, V185, P241 ZERHOUNI EA, 1988, RADIOLOGY, V169, P59 TC 0 BP 701 EP 714 PG 14 JI Comput. Graph.-UK PY 2000 PD OCT VL 24 IS 5 GA 372VR PI OXFORD RP Park J Korea Adv Inst Sci & Technol, Dept Elect Engn & Comp Sci, Div Comp Sci, 373-1 Kusong Dong Yusong Ku, Taejon 305701, South Korea J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000165255200006 ER PT Journal AU Xiao, YC Ziebarth, JP TI FEM-based scattered data modeling and visualization SO COMPUTERS & GRAPHICS-UK LA English DT Article NR 33 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Univ Akron, Dept Math & Comp Sci, Ayer Hall 235, Akron, OH 44325 USA Univ Akron, Dept Math & Comp Sci, Akron, OH 44325 USA NASA, Ames Res Ctr, Numer Aerosp Simulat Div, Moffett Field, CA 94035 USA DE scattered data; modeling; visualization; finite element method; interpolation ID INTERPOLATION AB A critical challenge in visualizing scattered data is to correctly model the sample data so that data variation throughout the volume of interest call be accurately rendered. The commonly used interpolation-based approach is unsatisfactory, as it often generates physically impossible data values in the modeling process. In addition, it does not provide a systematic way of estimating errors. The interpolation methods used for modeling are usually different from those used for rendering, which causes inconsistency and misrepresentation. Furthermore, interpolation methods cannot handle discontinuities, due to their inherent assumption that the data are continuous. To eliminate these and other problems, we construct an alternative approach to scattered data visualization. Based on the finite element method (FEM), this FEM-based approach incorporates the governing equations of the da ta into the modeling process to ensure the modeled data to be physically meaningful. It provides error estimates that can guide the refinement of the finite element network to obtain the desired accuracy. It allows the selection of basis functions in the modeling process to march with the interpolation functions used by the rendering process so that consistency can be achieved. It handles discontinuities with the help of the double-layer scheme. Furthermore, it converts the data-modeling problem from an interpolation problem into a boundary-value problem, and therefore reduces the requirement on the density of the input sample data, a feature which is very valuable to applications where sample data a re hard to obtain. This paper presents the Framework and a sample implementation of the FEM-based approach along with some examples. (C) 2000 Elsevier Science Ltd. All rights reserved. CR CARBRAL B, 1994, P S VOL VIS, P91 DUCHON J, 1975, CONSTRUCTIVE THEORY, P85 FOLEY AT, 1990, P 1 IEEE C VIS SAN F, P247 FRANKE R, 1982, MATH COMPUT, V38, P181 GALLAGHER RS, 1989, COMPUT GRAPH, V23, P185 GONZALEZ RC, 1987, DIGITAL IMAGE PROCES HABERMAN R, 1983, ELEMENTARY APPL PART HARDY RL, 1971, J GEOPHYS RES, V76, P1905 JOE B, 1991, COMPUT AIDED GEOM D, V8, P123 JOE B, 1989, SIAM J SCI STAT COMP, V10, P719 KARDESTUNCER H, 1987, FINITE ELEMENT HDB KAUFMAN A, 1990, VOLUME VISUALIZATION KRIGE DG, 1966, GEOEXPLORATION, V4, P43 LORENSEN WE, 1987, COMPUT GRAPHICS, V21, P163 MIKHLIN SG, 1964, VARIATIONAL METHODS NEIDER J, 1993, OPENGL PROGRAMMING G NIELSON G, 1979, MATH COMPUT, V40, P318 NIELSON GM, 1993, IEEE COMPUT GRAPH, V13, P60 NIELSON GM, 1994, STATE ART COMPUTER G, P67 RENKA RJ, 1984, ROCKY MT J MATH, V14, P223 SCHROEDER W, VISUALIZATION TOOLKI SCHROEDER W, 1998, VISUALIZATION TOOLKI SEDGEWICK R, 1990, ALGORITHMS C SHEPARD D, 1968, P 23 NAT C ACM, P517 SHUMAKER L, 1987, TOPICS MULTIVARIATE, P219 SPERAY D, 1990, COMPUT GRAPH, V24, P5 STRANG G, 1973, ANAL FINITE ELEMENT WATSON DF, 1984, COMPUT VISION GRAPH, V26, P217 WESTOVER L, 1990, COMP GRAPH, V24, P367 XIAO Y, 1999, INT J COMPUTERS APPL, V21, P56 XIAO Y, 1996, P IEEE VIS 96 C SAN, P413 ZIENKIEWICZ OC, 1977, FINITE ELEMENT METHO ZUIDERVELD KJ, 1992, P VIS BIOM COMP 1992, P324 TC 0 BP 775 EP 789 PG 15 JI Comput. Graph.-UK PY 2000 PD OCT VL 24 IS 5 GA 372VR PI OXFORD RP Xiao YC Univ Akron, Dept Math & Comp Sci, Ayer Hall 235, Akron, OH 44325 USA J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000165255200011 ER PT Journal AU Wegman, EJ TI Affordable environments for 3D collaborative data visualization SO COMPUTING IN SCIENCE & ENGINEERING LA English DT Article NR 5 SN 1521-9615 PU IEEE COMPUTER SOC C1 George Mason Univ, Ctr Computat Stat, MS 4A7, Fairfax, VA 22030 USA George Mason Univ, Ctr Computat Stat, Fairfax, VA 22030 USA CR BULLINGER HJ, 1999, P 3 INT IMM PROJ TEC CRUZNEIRA C, 1992, COMMUN ACM, V35, P64 WEGMAN EJ, 1993, HDB STAT, V9, P857 WEGMAN EJ, 1996, P 2 INT C MIL APPL S, P203 WEGMAN EJ, 1996, P 3 INT IMM PROJ TEC, P179 TC 0 BP 68 EP + PG 6 JI Comput. Sci. Eng. PY 2000 PD NOV-DEC VL 2 IS 6 GA 369HU PI LOS ALAMITOS RP Wegman EJ George Mason Univ, Ctr Computat Stat, MS 4A7, Fairfax, VA 22030 USA J9 COMPUT SCI ENG PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000165060500011 ER PT Journal AU Erbacher, RF Pang, A TI Special section on visualization and data analysis SO JOURNAL OF ELECTRONIC IMAGING LA English DT Editorial Material NR 0 SN 1017-9909 PU I S & T - SOC IMAGING SCIENCE TECHNOLOGY C1 SUNY Albany, Dept Comp Sci, Albany, NY 12222 USA SUNY Albany, Dept Comp Sci, Albany, NY 12222 USA Univ Calif Santa Cruz, Dept Comp Sci, Santa Cruz, CA 95064 USA TC 0 BP 354 EP 355 PG 2 JI J. Electron. Imaging PY 2000 PD OCT VL 9 IS 4 GA 365YM PI SPRINGFIELD RP Erbacher RF SUNY Albany, Dept Comp Sci, Albany, NY 12222 USA J9 J ELECTRON IMAGING PA 7003 KILWORTH LANE, SPRINGFIELD, VA 22151 USA UT ISI:000089978100001 ER PT Journal AU Berchtold, S Jagadish, HV Ross, KA TI Independence diagrams: A technique for data visualization SO JOURNAL OF ELECTRONIC IMAGING LA English DT Article NR 21 SN 1017-9909 PU I S & T - SOC IMAGING SCIENCE TECHNOLOGY C1 STB Software Technol Beratung Gmbh, Ulrichspl 6, D-86150 Augsburg, Germany STB Software Technol Beratung Gmbh, D-86150 Augsburg, Germany Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA Columbia Univ, Dept Comp Sci, New York, NY 10027 USA AB An important issue in data visualization is the recognition of complex dependencies between attributes. Past techniques for identifying attribute dependence include correlation coefficients, scatterplots, and equi-width histograms. These techniques are sensitive to outliers, and often are not sufficiently informative to identify the kind of attribute dependence present We propose a new approach, which we call independence diagrams. We divide each attribute into ranges; for each pair of attributes, the combination of these ranges defines a two-dimensional grid. For each cell of this grid, we store the number of data items in it We display the grid, sealing each attribute axis so that the displayed width of a range is proportional to the total number of data items within that range. The brightness of a cell is proportional to the density of data items in it As a result, both attributes are independently normalized by frequency, ensuring insensitivity to outliers and skew, and allowing specific focus on attribute dependencies. Furthermore, independence diagrams provide quantitative measures of the interaction between two attributes, and allow formal reasoning about issues such as statistical significance. We have addressed several technical challenges in making independence diagrams work, ranging from the treatment of categorical attributes to visual artifacts of cell-to-pixel mapping. Our experimental evaluation, using both AT&T and synthetic data, shows that independence diagrams allow the easy identification of various kinds of attribute dependence that would be difficult to identify using conventional techniques. (C) 2000 SPIE and IS&T. [S1017- 9909(00)01704-9]. CR AGRAWAL R, 1993, P ACM SIGMOD C MAN D, P207 AGRAWAL R, 1995, P COMAD DEC ALSABTI K, 1997, P VLDB ATH AUG, P346 ANDREWS DF, 1972, BIOMETRICS, V29, P125 ANKERST M, 1996, VISUALIZATION 96 SAN BERCHTOLD S, 1995, P INT C KNOWL DISC D, P139 BERCHTOLD S, UNPUB APPROXIMATE HI DUMOUCHEL W, 1996, AI STAT, V5, PCH39 FUKUDA T, 1996, P ACM SIGMOD C MAN D, P13 HUANG CS, 1997, J COMPUT GRAPH STAT, V6, P383 INSELBERG A, 1985, VISUAL COMPUT, V1, P29 IOANNIDIS YE, 1995, P 1995 ACM SIGMOD IN, P233 JAGADISH HV, 1998, P 24 INT C VER LARG, P275 KEIM D, 1994, P 10 DAT ENG, P302 KEIM DA, 1997, TUTORIAL VLDB LEVKOWITZ H, 1997, COLOR THEORY MODELIN MURALIKRISHNA M, 1988, P ACM SIGMOD C, P28 POOSALA V, 1997, P 23 INT C VER LARG, P486 POOSALA V, 1996, P ACM SIGMOD INT C M, P294 SRIKANT R, 1996, P ACM SIGMOD INT C M, P1 VANWIJK JJ, 1993, VISUALIZATION 93 LOS, P119 TC 0 BP 375 EP 384 PG 10 JI J. Electron. Imaging PY 2000 PD OCT VL 9 IS 4 GA 365YM PI SPRINGFIELD RP Berchtold S STB Software Technol Beratung Gmbh, Ulrichspl 6, D-86150 Augsburg, Germany J9 J ELECTRON IMAGING PA 7003 KILWORTH LANE, SPRINGFIELD, VA 22151 USA UT ISI:000089978100004 ER PT Journal AU Han, JC Cercone, N TI Visualizing the process of knowledge discovery SO JOURNAL OF ELECTRONIC IMAGING LA English DT Article NR 26 SN 1017-9909 PU I S & T - SOC IMAGING SCIENCE TECHNOLOGY C1 Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada AB Most existing visualization systems stress either the original data visualization or the discovered knowledge visualization, such as decision tree, neural network. rules, etc., but lack the abilities to visualize the entire process of knowledge discovery. We propose an interactive model, RuleViz, for visualizing the process of knowledge discovery and data mining. The RuleViz model consists of five components, each or which can be interacted and visualized by using different visualization techniques. According to this model two interactive systems, AViz and CViz, for visualizing the process of discovering numerical association rules and the process of learning classification rules have been implemented, respectively. To preprocess the data, each system provides users with three approaches for discretizing numerical attributes and the corresponding rule discovery algorithms. The discretization approaches and the algorithms for discovering association rules and learning classification rules are presented, and the approaches to visualizing discretized data and discovered rules are developed. The discovery of numerical association rules in AViz is based on image-based mining algorithm, while, in CViz, the classification rules are learned in terms of a logical rule induction algorithm. We also demonstrate our experimental results with AViz and CViz on the census data sets. UCI data sets, and artificial data sets. (C) 2000 SPIE and IS&T. [S107-9909(00)01304-0]. CR AGRAWAL R, 1996, ADV KNOWLEDGE DISCOV, P307 AHLBERG C, 1996, ACM SIGMOD RECORD, V25, P25 AN A, 1998, P 12 BIENN C CAN SOC, P426 AN A, 1999, P 3 PAC AS C KNOWL D, P509 ANKERST M, 1999, P 5 INT C KNOWL DISC, P392 BERCHTOLD S, 1998, P KDD NEW YORK, P139 CHEN JX, 1996, IEEE COMPUT SCI ENG, V3, P13 CURTIS JV, 1998, P ACM S US INT SOFTW, P29 DERTHICK M, 1997, P 3 INT C KNOWL DISC, P2 FAYYAD UM, 1993, P 13 INT JOINT C ART, P1022 FUKUDA T, 1996, P ACM SIGMOD C MAN D, P13 GORDON AG, 1981, CLASSIFICATION GROTH R, 1998, DATA MINING HANDS ON HAN J, 1999, P 3 PAC AS C KNOWL D, P390 HAN J, 2000, P 4 PAC AS C KNOWL D, P269 INSELBERG A, 1990, P VISUALIZATION 90, P361 KEIM DA, 1994, IEEE COMPUT GRAPH, V14, P40 KEIM DA, 1996, IEEE T KNOWL DATA EN, V8, P923 LIU H, 1998, FEATURE EXTRACTION C MILLER RJ, 1997, P ACM SIGMOD INT C M, P452 MURPHY PM, 1994, UCI REPOSITORY MACHI PIATETSKYSHAPIR.G, 1991, KNOWLEDGE DISCOVERY, P229 QUINLAN JR, 1993, C4 5 PROGRAMS MACHIN RAO JS, 1997, P 3 INT C KNOWL DISC, P243 SRIKANT R, 1996, P ACM SIGMOD INT C M, P1 THOMPSON SK, 1992, SAMPLING TC 0 BP 404 EP 420 PG 17 JI J. Electron. Imaging PY 2000 PD OCT VL 9 IS 4 GA 365YM PI SPRINGFIELD RP Han JC Univ Waterloo, Dept Comp Sci, Waterloo, ON N2L 3G1, Canada J9 J ELECTRON IMAGING PA 7003 KILWORTH LANE, SPRINGFIELD, VA 22151 USA UT ISI:000089978100007 ER PT Journal AU Toussaint, M Malkomes, M Hagen, M Holler, H Meischner, P TI A real time data visualization and analysis environment, scientific data management of large weather radar archives SO PHYSICS AND CHEMISTRY OF THE EARTH PART B-HYDROLOGY OCEANS AND ATMOSPHERE LA English DT Article NR 0 SN 1464-1909 PU PERGAMON-ELSEVIER SCIENCE LTD C1 GAMIC MBH Aachen, Roermonder Str 151, D-52072 Aachen, Germany GAMIC MBH Aachen, D-52072 Aachen, Germany DLR, Inst Phys Atmosphare, D-82230 Wessling, Germany AB This paper presents the DLR-TOOLKIT software environment designed to process polarimetric and Doppler weather radar data of POLDIRAD. In addition it integrates BISTATIC Doppler radar wind data. The large amount of information and radar- meteorological moments generated by the multi-polarization Doppler radar in conjunction with the bistatic radar receivers represents a great challenge for data processing with respect to performance and data quantity. (C) 2000 Elsevier Science Ltd. All rights reserved. TC 0 BP 1001 EP 1003 PG 3 JI Phys. Chem. Earth Pt B-Hydrol. Oceans Atmos. PY 2000 VL 25 IS 10-12 GA 356ZD PI OXFORD RP Toussaint M GAMIC MBH Aachen, Roermonder Str 151, D-52072 Aachen, Germany J9 PHYS CHEM EARTH P B-HYDROL OC PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000089473300038 ER PT Journal AU Sarfraz, M TI A rational cubic spline for the visualization of monotonic data SO COMPUTERS & GRAPHICS-UK LA English DT Article NR 15 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 King Fahd Univ Petr & Minerals, Dept Informat & Comp Sci, KFUPM 1510, Dhahran 31261, Saudi Arabia King Fahd Univ Petr & Minerals, Dept Informat & Comp Sci, Dhahran 31261, Saudi Arabia DE data visualization; rational spline; interpolation; shape preserving; monotone ID QUADRATIC SPLINE; INTERPOLATION; SHAPE AB A smooth curve interpolation scheme for monotonic data has been developed. This scheme uses piecewise rational cubic functions. The two families of parameters, in the description of the rational interpolant, have been constrained to preserve the shape of the data. The rational spline scheme has a unique representation. The degree of smoothness attained is C-2 which is more powerful than a previous C-1 method. (C) 2000 Elsevier Science Ltd. All rights reserved. CR BRODLIE KW, 1991, COMPUT GRAPHICS, V15, P15 BRODLIE KW, 1985, FUNDAMENTAL ALGORITH, P303 CONSTANTINI P, 1997, ACM T MATH SOFTWARE, V23, P229 DEVORE A, 1986, COMPUT AIDED GEOM D, V3, P205 FRITSCH FN, 1980, SIAM J NUMER ANAL, V17, P238 FRITSCH FN, 1984, SIAM J SCI STAT COMP, V5, P303 GREGORY JA, 1986, COMPUT AIDED DESIGN, V18, P53 GREINER H, 1991, MATH COMPUT MODEL, V15, P97 LAHTINEN A, 1996, ANN NUMERICAL MATH, V3, P151 MCALLISTER DF, 1981, ACM T MATH SOFTWARE, V7, P331 PASSOW E, 1977, SIAM J NUMER ANAL, V14, P904 SARFRAZ M, 1992, B KOREAN MATH SOC, V29, P193 SARFRAZ M, 1997, COMPUT GRAPH, V21, P5 SARFRAZ M, 1992, COMPUT GRAPHICS, V16, P427 SCHUMAKER LL, 1983, SIAM J NUMER ANAL, V20, P854 TC 1 BP 509 EP 516 PG 8 JI Comput. Graph.-UK PY 2000 PD AUG VL 24 IS 4 GA 357LM PI OXFORD RP Sarfraz M King Fahd Univ Petr & Minerals, Dept Informat & Comp Sci, KFUPM 1510, Dhahran 31261, Saudi Arabia J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000089503200002 ER PT Journal AU Wegman, EJ TI Visions: New techniques and technologies in statistics SO COMPUTATIONAL STATISTICS LA English DT Article NR 6 SN 0943-4062 PU PHYSICA-VERLAG GMBH & CO C1 George Mason Univ, Ctr Computat Stat, Fairfax, VA 22030 USA George Mason Univ, Ctr Computat Stat, Fairfax, VA 22030 USA DE data analysis; computational statistics; massive data sets; data visualization; data mining; distributed databases; metedata centers AB This paper attempts to Faint a futurist vision for data analysts and in doing so discusses some of the tools and techniques likely to be used. A major premise of this vision is that mathematical statistics like classical mechanics is essentially a completed discipline. Moreover, that changes in the nature, modes of collection, and scale of data imply new tools and techniques are inevitable. Complexity of algorithms and data structures imply an increased focus on algorithmic efficiency and, to some extent, more automated procedures. Suggestions for advancement in theory are made with respect to data mining, visualization and quantization methods. Suggestions are also made on likely architectures for digital text and data libraries, for modes of accessing distributed databases, and for the implications on collaboration. CR FRIEDMAN JH, 1998, COMPUTING SCI STAT, V29, P3 HUBER PJ, 1994, COMPSTAT 1994 HUBER PJ, 1992, COMPUTATIONAL STAT, V2 KAKU M, 1998, VISIONS SCI WILL REV WEGMAN EJ, 1988, AM STAT ASS P SECT S, P1 WEGMAN EJ, 1988, J WASHINGTON ACADEMY, V78, P310 TC 0 BP 133 EP 144 PG 12 JI Comput. Stat. PY 2000 VL 15 IS 1 GA 352MC PI HEIDELBERG RP Wegman EJ George Mason Univ, Ctr Computat Stat, Fairfax, VA 22030 USA J9 COMPUTATION STAT PA TIERGARTENSTRASSE 17, 69121 HEIDELBERG, GERMANY UT ISI:000089221600015 ER PT Journal AU van Voorthuysen, EJ Platfoot, RA TI A flexible data acquisition system to support process identification and characterization SO PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B- JOURNAL OF ENGINEERING MANUFACTURE LA English DT Article NR 17 SN 0954-4054 PU PROFESSIONAL ENGINEERING PUBLISHING LTD C1 Univ New S Wales, Sch Mech & Mfg Engn, Design Syst Lab, Sydney, NSW 2052, Australia Univ New S Wales, Sch Mech & Mfg Engn, Design Syst Lab, Sydney, NSW 2052, Australia DE data acquisition; process identification; data visualization; cause-effect analysis AB A system has been developed to identify relationships between critical process variables for the purpose of process improvement and upgrading. It incorporates process monitoring, large data-set management, data visualization and fine- precision diagnostics. Implementing the process combines a meticulous and rigorous instrumentation selection with a data acquisition strategy. The identification of key process variables follows an Ishikawa process and is incorporated into a process model that supports the diagnostic function. A case study testing this methodology for a commercial gravure printing process was successfully completed. Understanding the mechanical behaviour of critical variables required data acquisition at sampling times up to 20 ms. Data analysis was carried out in time, amplitude and frequency domains. The result of this work was a series of practical recommendations to reduce waste within the printing process. CR 1998, LOGICAL VISION WIT U 1998, LTC1049 LOW PROPER C 1993, XTR 110 PRECISION VO *MATH WORKS INC, 1995, MATLAB VERS 4 US GUI ALEXANDER SM, 1998, COMPUT IND ENG, V35, P193 BANKS D, 1992, J QUAL TECHNOL JUL, V24 BOYLE E, 1997, PAPER FILM FOIL JUN, P30 BROOKS R, 1998, MULTISENSOR FUSION HATCHER L, 1994, STEP BY STEP APPROAC KING DW, 1995, STAT QUALITY CONTROL MYKYTIUK A, 1997, PAPER FILM FOIL SEP, P90 NEGIZ A, 1998, J PROCESS CONTR, V8, P375 NOVIT E, 1997, PAPER FILM FOIL OCT PUCKHABER C, 1996, GRAVURE FAL, P18 RUTHERFORD B, 1991, GRAVURE PROCESS TECH SILVER E, 1997, J OPS MGMT, P139 YUNG WKC, 1996, J MATER PROCESS TECH, V61, P39 TC 0 BP 569 EP 579 PG 11 JI Proc. Inst. Mech. Eng. Part B-J. Eng. Manuf. PY 2000 VL 214 IS 7 GA 351ZE PI BURY ST EDMUNDS RP Platfoot RA Univ New S Wales, Sch Mech & Mfg Engn, Design Syst Lab, Sydney, NSW 2052, Australia J9 PROC INST MECH ENG B-J ENG MA PA NORTHGATE AVENUE,, BURY ST EDMUNDS IP32 6BW, SUFFOLK, ENGLAND UT ISI:000089189400006 ER PT Journal AU [Anon] TI Internet-technology advances bring data to the expert user onshore SO JOURNAL OF PETROLEUM TECHNOLOGY LA English DT Article NR 0 SN 0149-2136 PU SOC PETROLEUM ENG AB Information technology and drilling practices have converged so that it is already possible for office-based experts to monitor and evaluate rig-site activity and make critical operational decisions. Soon, better communications, visualization tools, data standards, and rig-site automation will enable centrally based engineers to manage operations on many drilling rigs located thousands of miles apart. TC 0 BP 82 EP 84 PG 3 JI J. Pet. Technol. PY 2000 PD SEP VL 52 IS 9 GA 350WX PI RICHARDSON J9 J PETROL TECHNOL PA 222 PALISADES CREEK DR,, RICHARDSON, TX 75080 USA UT ISI:000089125600016 ER PT Book in series AU Vellido, A Lisboa, PJG Meehan, K TI Segmenting the e-commerce market using the Generative Topographic Mapping SO MICAI 2000: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS LA English DT Article NR 29 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Liverpool John Moores Univ, Sch Comp & Math Sci, Byrom St, Liverpool L3 3AF, Merseyside, England Liverpool John Moores Univ, Sch Comp & Math Sci, Liverpool L3 3AF, Merseyside, England Liverpool John Moores Univ, Sch Business, Liverpool L3 5UZ, Merseyside, England ID NEURAL NETWORKS; SEGMENTATION AB The neural network-based Generative Topographic Mapping (GTM) (Bishop et al. 1998a, 1998b) is a statistically sound alternative to the well-known Self Organizing Map (Kohonen 1982, 1995). In this paper we propose the GTM as a principled model for cluster-based market segmentation and data visualization. It has the capability to define, using Bayes' theorem, a posterior probability of cluster/segment membership for each individual in the data sample. This, in turn, enables the GTM to be used to perform segmentation to different levels of detail or granularity, encompassing aggregate segmentation and one-to-one micro-segmentation. The definition of that posterior probability also makes the GTM a tool for fuzzy clustering/segmentation. The capabilities of the model are illustrated by a segmentation case study using real-world data of Internet users opinions on business-to-consumer electronic commerce. CR ALLENBY GM, 1994, J AM STAT ASSOC, V89, P1218 ARABIE P, 1994, ADV METHODS MARKETIN, P160 BISHOP CM, 1998, NEURAL COMPUT, V10, P215 BISHOP CM, 1998, NEUROCOMPUTING, V21, P203 CHEN MS, 1996, IEEE T KNOWL DATA EN, V8, P866 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 FIRAT AF, 1997, EUR J MARKETING, V31, P183 GORDON ME, 1997, INT MARKET REV, V14, P362 GREEN PE, 1995, J MARKET RES SOC, V37, P221 HINTON GE, 1992, ADV NEURAL INFORMATI, V4, P512 KARA A, 1997, EUR J MARKETING, V31, P873 KEHOE C, 1998, 9 GVUS WWW USER SURV KOHONEN T, 1982, BIOL CYBERN, V43, P59 KOHONEN T, 1995, SELF ORG MAPS LEWIS OM, 1997, NEURAL COMPUT APPL, V5, P224 MACKAY DJC, 1995, NETWORK-COMP NEURAL, V6, P469 MACKAY DJC, 1992, NEURAL COMPUT, V3, P448 MCDONALD WJ, 1996, ENHANCING KNOWLEDGE, P338 MCLACHLAN GJ, 1988, MIXTURE MODELS INFER RIPLEY B, 1996, PATTERN RECOGNITION SCHAFFER CM, 1998, J MARKET RES SOC, V40, P155 SCHAPER A, 1898, J COMP NEUROL, V8, P1 SERRANOCINCA C, 1996, DECIS SUPPORT SYST, V17, P227 SLATER D, 1999, P EUR MULT MICR SYST VELLIDO A, 1999, EXPERT SYSTEMS APPL, V17 VELLIDO A, 2000, IN PRESS NEURAL NETW WALLIN EO, 1999, P EUR MULT MICR SYST WEDEL M, 1998, INT SERIES QUANTITAT WIND Y, 1978, J MARKETING RES, V15, P317 TC 0 BP 470 EP 481 PG 12 SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE PY 2000 VL 1793 GA BQ61W PI BERLIN RP Vellido A Liverpool John Moores Univ, Sch Comp & Math Sci, Byrom St, Liverpool L3 3AF, Merseyside, England J9 LECT NOTE ARTIF INTELL PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000088970800043 ER PT Journal AU Bolte, J Nath, S Ernst, D TI Development of decision support tools for aquaculture: the POND experience SO AQUACULTURAL ENGINEERING LA English DT Article NR 43 SN 0144-8609 PU ELSEVIER SCI LTD C1 Oregon State Univ, Dept Bioresource Engn, Corvallis, OR 97331 USA Oregon State Univ, Dept Bioresource Engn, Corvallis, OR 97331 USA Skillings Connolly Inc, Lacey, WA 98503 USA DE aquaculture; decision support; object-orientated modeling; pond dynamics; simulation; tilapia ID OBJECT-ORIENTED SIMULATION; SYSTEMS AB Decision support systems (DSS) are potentially valuable tools for assessing the economic and ecological impacts of alternative decisions on aquaculture production. In this paper, we discuss the philosophy of design, Functional modules and application areas of POND, a decision tool that has been developed to allow analysis of pond aquaculture facilities by the use of a combination of simulation models and enterprise budgeting. We focus less on the details of POND's internal models, and more on the experiences we have gained from going through the process of the designing, developing and using the POND software. POND was designed and implemented using object- oriented programming principles. The software makes use of a simulation framework to provide much of the generic simulation, data handling, time flow synchronization and communication features necessary for complex model-based DSSs. Additionally, an architecture suitable for representing and manipulating pond aquaculture facilities was developed in order to meet the design specifications of POND. This architecture includes a series of mini-databases, a number of knowledge-based components ('experts') models of the pond ecosystem, and various decision support features (e.g. assembling alternate management scenarios, economic analysis, and data visualization). A typical POND simulation consists of assembling a number of appropriate objects or entities (e.g. multiple ponds and fish lots), their management settings together with appropriate experts (e.g. an aquaculture engineer, an aquatic biologist, an economist, etc.), and projecting changes in the facility over time. Our experience with the development of POND and other simulation-based tools indicates that the object-based approach provides a robust foundation for developing tools which allow code reusability, facilitate maintenance of complex software, and enable partition of program development among multiple programmars. Experience gained with POND users suggests that there are largely two groups of aquaculture personnel interested in such applications, namely commercial growers and educators. These two groups have substantially different interests and needs. Consequently, a single tool such as POND may not optimally meet the requirements of both groups. Recent development work on POND, and the need to involve users in the design process of such tools are discussed. (C) 2000 Elsevier Science B.V. All rights reserved. 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Eng. PY 2000 PD SEP VL 23 IS 1-3 GA 342AA PI OXFORD RP Bolte J Oregon State Univ, Dept Bioresource Engn, Corvallis, OR 97331 USA J9 AQUACULT ENG PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND UT ISI:000088622100007 ER PT Journal AU Strohmaier, AR Porwol, T Acker, H Spiess, E TI Three-dimensional organization of microtubules in tumor cells studied by confocal laser scanning microscopy and computer- assisted deconvolution and image reconstruction SO CELLS TISSUES ORGANS LA English DT Article NR 27 SN 1422-6405 PU KARGER C1 Max Planck Inst Mol Physiol, Otto Hahn Str 11, D-44227 Dortmund, Germany Max Planck Inst Mol Physiol, D-44227 Dortmund, Germany Deutsch Krebsforschungszentrum, D-6900 Heidelberg, Germany DE reconstruction; three-dimensional; confocal microscopy; deconvolution; microtubule ID OXYGEN-SENSING PATHWAY; FLUORESCENCE MICROSCOPY; LIVING CELLS; HEPG2 CELLS; VISUALIZATION; CYTOSKELETON; MEMBRANE; DYNAMICS; MEMBERS; COBALT AB Confocal microscopy, image deconvolution and computer-assisted methods have been used to reconstruct the three-dimensional (3- D) distribution of tubulin in cells. The techniques were applied to tumor cells growing under regular culture conditions (planar cultivation) and those penetrating into reconstituted collagen matrices (spatial cultivation). As expected the application of deconvolution algorithms enhanced the resolution of results. The deconvolution using the maximum likelihood estimation included the measurement of the point spread function of the optical setup. The data visualization of the resulting data sets uses volume as well as surface rendering approaches. The 3-D reconstruction gives a clear image of the spatial arrangement of tubulin fibers in relation to cell shape and position of other cellular organelles, particularly the nucleus. The tubulin forms an intricate network of fibers of variable thickness. The highest tubulin concentrations appear in the cell periphery and particularly in pseudopodia/invadopodia. This is indicative of an enhanced transport of intracellular material facilitating cell movement and lysis of the extracellular matrix. The investigation is assumed to stimulate further experiments using a variety of techniques leading to the complete understanding of the spatial architecture of living cells. Copyright (C) 2000 S. Karger AG, Basel. 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Mol. Graph. PY 2000 PD FEB VL 18 IS 1 GA 337XF PI NEW YORK RP Richon A Columbus Mol Software Inc, 1275 Kinnear Rd, Columbus, OH 43212 USA J9 J MOL GRAPH MODEL PA 655 AVENUE OF THE AMERICAS, NEW YORK, NY 10010 USA UT ISI:000088387900012 ER PT Journal AU Maltz, MD Mullany, JM TI Visualizing lives: New pathways for analyzing life course trajectories SO JOURNAL OF QUANTITATIVE CRIMINOLOGY LA English DT Article NR 40 SN 0748-4518 PU KLUWER ACADEMIC/PLENUM PUBL C1 Univ Illinois, Dept Criminal Justice, 1007 W Harrison St,MC 141, Chicago, IL 60607 USA Univ Illinois, Dept Criminal Justice, Chicago, IL 60607 USA Indiana Univ NW, Sch Publ & Environm Affairs, Gary, IN 46408 USA DE longitudinal analysis; graphical analysis; life course; data visualization; exploratory data analysis ID HYPOTHESIS; FUTURE AB The goal of statistical analysis is to find patterns in data. Most statistical methods rely on analyzing the effect of the same set of variables on the population under study, i.e., nomothetic analysis. Therefore, when data are collected in the social sciences, most often they are put in a framework that resembles a spreadsheet: each row represents a separate individual, and each column represents a separate characteristic (or variable) that pertains to that individual. However, not all individuals in the study are affected by the same set of variables: each individual may have his/her own individual set of relevant variables, suggesting that methods be developed that consider them individually, i.e., idiographic analysis. Moreover, lives are lived chronologically, and are often best described in narrative form. These narratives usually have to be condensed, or abridged in other ways, in order to fit the data Framework and permit what one might call "algorithmic analysis". Each set of methods has its advantage: nomothetic methods generate general laws that apply to all, while idiographic methods trace the putative causal relationships that are unique to each individual. This paper describes another data collection and analytic framework, one that (a) is chronological; (b) recognizes that different people may have experienced entirely different events and thus may need different "variables" to understand their behavior; (c) recognizes that, even if people experience similar events, they may have entirely different reactions to them; and (d) can be studied (and patterns inferred) using an exploratory graphical analysis that is more free-form than algorithmic analysis. Examples of this type of analysis used in different medical and criminal justice contexts are given, and suggested directions of research in this area are described. 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Quant. Criminol. PY 2000 PD JUN VL 16 IS 2 GA 336CP PI NEW YORK RP Maltz MD Univ Illinois, Dept Criminal Justice, 1007 W Harrison St,MC 141, Chicago, IL 60607 USA J9 J QUANT CRIMINOL PA 233 SPRING ST, NEW YORK, NY 10013 USA UT ISI:000088284600008 ER PT Journal AU Raymer, ML Punch, WE Goodman, ED Kuhn, LA Jain, AK TI Dimensionality reduction using genetic algorithms SO IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION LA English DT Article NR 41 SN 1089-778X PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 Michigan State Univ, Dept Comp Sci & Engn, E Lansing, MI 48824 USA Michigan State Univ, Dept Comp Sci & Engn, E Lansing, MI 48824 USA Michigan State Univ, Case Ctr Comp Aided Engn & Mfg, E Lansing, MI 48824 USA Michigan State Univ, Dept Biochem, E Lansing, MI 48824 USA DE curse of dimensionality; feature extraction; feature selection; genetic algorithms; pattern classification ID FEATURE-EXTRACTION; FEATURE-SELECTION; NEURAL NETWORKS; PROTEINS AB Pattern recognition generally requires that objects be described in terms of a set of measurable features. The selection and quality of the features representing each pattern have a considerable bearing on the success of subsequent pattern classification. Feature extraction is the process of deriving new features from the original features in order to reduce the cost of feature measurement, increase classifier efficiency, and allow higher classification accuracy, Many current feature extraction techniques involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and increasing classification efficiency, it does not necessarily reduce the number of features that must be measured since each new feature may be a linear combination of all of the features in the original pattern vector, Here, we present a new approach to feature extraction in which feature selection, feature extraction, and classifier training are performed simultaneously using a genetic algorithm, The genetic algorithm optimizes a vector of feature weights, which are used to scale the individual features in the original pattern vectors in either a linear or a nonlinear fashion. A masking vector is also employed to perform simultaneous selection of a subset of the features, We employ this technique in combination with the k nearest neighbor classification rule, and compare the results with classical feature selection and extraction techniques including sequential floating forward feature selection, and linear discriminant analysis. We also present results for the identification of favorable water-binding sites on protein surfaces, an important problem in biochemistry and drug design. 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Evol. Comput. PY 2000 PD JUL VL 4 IS 2 GA 334WL PI NEW YORK RP Raymer ML Michigan State Univ, Dept Comp Sci & Engn, E Lansing, MI 48824 USA J9 IEEE TRANS EVOL COMPUTAT PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000088208600006 ER PT Book in series AU Boyd, DRS Gallop, JR Palmen, KEV Platon, RT Seelig, CD TI VIVRE: User-centred visualization SO HIGH-PERFORMANCE COMPUTING AND NETWORKING, PROCEEDINGS LA English DT Article NR 9 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 CLRC, Rutherford Appleton Lab, Didcot OX11 0QX, Oxon, England CLRC, Rutherford Appleton Lab, Didcot OX11 0QX, Oxon, England AB A new approach to visualization is described which places the user at the centre of this interactive, investigative process. This is achieved by integrating the immersive user interaction environment provided by a virtual reality system with the comprehensive visualization capability provided by two established data visualization systems. Users' existing investment in visualization applications is thus protected while offering them a powerful new way of steering their investigations of complex data. This integrated environment has been specified and is being developed in a user-driven EU ESPRIT demonstrator project, VIVRE. CR BENOELKAN P, 1998, P 4 EUR VIRT ENV WOR BRYSON S, 1991, P IEEE VIS 91 FRUEHAUF T, 1996, P 3 EUR VIRT ENV WOR, P223 FUHRMANN A, 1998, IEEE COMPUT GRAPH, V18, P54 GALLOP JR, 1996, TUTORIAL EUROGRAPHIC HAASE H, 1994, CWI Q, V7, P159 HAASE S, 1996, P EUR 96 SASTRY L, 1998, P 8 INT S FLOW VIS SHERMAN WR, 1993, P 4 EUR WORKSH VIS S TC 0 BP 807 EP 816 PG 10 SE LECTURE NOTES IN COMPUTER SCIENCE PY 1999 VL 1593 GA BQ39Q PI BERLIN RP Boyd DRS CLRC, Rutherford Appleton Lab, Didcot OX11 0QX, Oxon, England J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000088252100082 ER PT Book in series AU Gorbachev, YE Zudilova, EV TI 3D-visualization for presenting results of numerical simulation SO HIGH-PERFORMANCE COMPUTING AND NETWORKING, PROCEEDINGS LA English DT Article NR 2 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Inst High Performance Comp & Data Bases, POB 71, St Petersburg 194291, Russia Inst High Performance Comp & Data Bases, St Petersburg 194291, Russia AB The paper is devoted to the problems of presenting results obtained during the numerical simulation. Two different approaches of data visualization: through 2D and 3D objects are described. 3D-visualization requires more computational resources than 2D, especially when the data volumes are large. So the best way for quick getting necessary data during the numerical simulation is to use supercomputers. We are going to present the two animation films demonstrating the results of 3D-visualization designed by the research fellows of the Institute for High-Performance Computing and Data Bases for simulating multidimension stationary/non-stationary molecular gasdynamic problems and for simulating mature convective clouds. CR BYKOV NY, 1997, EUROMECH C 363 MECH, P10 RAMAROSON IK, 1997, P 3 D C ATM CHEM LON TC 0 BP 1250 EP 1253 PG 4 SE LECTURE NOTES IN COMPUTER SCIENCE PY 1999 VL 1593 GA BQ39Q PI BERLIN RP Gorbachev YE Inst High Performance Comp & Data Bases, POB 71, St Petersburg 194291, Russia J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000088252100142 ER PT Journal AU Strehlow, D TI Software for quantitation and visualization of expression array data SO BIOTECHNIQUES LA English DT Article NR 0 SN 0736-6205 PU EATON PUBLISHING CO C1 Boston Univ, Med Ctr, Dept Med, 80 E Concord St, Boston, MA 02118 USA Boston Univ, Med Ctr, Dept Med, Boston, MA 02118 USA AB Software is described that facilitates the analysis of phosphoimages from large array hybridizations. The Macintosh((R)) PowerPC(TM)-compatible application and its manual are available at no charge from http://people.bu.edu/strehlow. The software is compatible with both custom formats and array filters from three commercial manufacturers. It allows the rapid quantitation of every spot on images of hybridizations to large arrays. The user drags grids of squares over the spots on the image to define the coordinates of each spot, then aligns and edits the position of the grid. The software then corrects the positions as necessary and quantitates up to 27 000 spots per image. It stores the numerical values for each signal in a format called the fingerprint file. Fingerprint files can be directly averaged or compared allowing the user to find mean values or differences in data from independent hybridization experiments. Data can be recalled from the fingerprint file and can be output in a variety of spreadsheet formats with several options for background correction. Finally, the software offers an output format that allows the convenient visualization of data points using animated three-dimensional graphs. TC 0 BP 118 EP 121 PG 4 JI Biotechniques PY 2000 PD JUL VL 29 IS 1 GA 333QP PI NATICK RP Strehlow D Boston Univ, Med Ctr, Dept Med, 80 E Concord St, Boston, MA 02118 USA J9 BIOTECHNIQUES PA 154 E. CENTRAL ST, NATICK, MA 01760 USA UT ISI:000088141000019 ER PT Journal AU Nevell, D TI Using aggregated cumulative hazard plots to visualize failure data SO QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL LA English DT Article NR 4 SN 0748-8017 PU JOHN WILEY & SONS LTD C1 8 Copse Grove,Littleover, Derby DD23 7WW, England Elect Data Syst Ltd, Derby DE24 8BJ, England DE data visualization; Weibull distribution; cumulative hazard; batch models; mixture models; proportional hazard AB The purpose of this paper is to demonstrate how the aggregated cumulative hazard (ACH) plot can be used to complement and extend standard Weibull analyses for non-repairable items. It looks at the shortcomings of using probability plots in isolation and shows how ACH plots can overcome these problems. Several examples are then described covering a range of applications. ACH plots often suggest particular types of models. The three-parameter mixture model is considered in detail. The paper concludes that ACH plots can be regarded as the focal point of a unified and holistic approach to pragmatic part level reliability and safety analysis. Copyright (C) 2000 John Wiley & Sons, Ltd. CR ABERNETHY RB, 1983, WEIBULL ANAL HDB LAKEY MJ, 1993, ANN QUAL C T, P824 NELSON W, 1982, APPL LIFE DATA ANAL TAGUCHI G, 1987, SYSTEM EXPT DESIGN, V2 TC 0 BP 209 EP 219 PG 11 JI Qual. Reliab. Eng. Int. PY 2000 PD MAY-JUN VL 16 IS 3 GA 329FR PI W SUSSEX RP Nevell D 8 Copse Grove,Littleover, Derby DD23 7WW, England J9 QUAL RELIAB ENG INT PA BAFFINS LANE CHICHESTER, W SUSSEX PO19 1UD, ENGLAND UT ISI:000087896800006 ER PT Journal AU Lee, IN Liao, SC Embrechts, M TI Data mining techniques applied to medical information SO MEDICAL INFORMATICS AND THE INTERNET IN MEDICINE LA English DT Article NR 5 SN 1463-9238 PU TAYLOR & FRANCIS LTD C1 12F,2 Lane 548,Chiu Ju 1st Rd, Kaohsiung, Taiwan Rensselaer Polytech Inst, Lally Sch Management & Technol, Troy, NY 12181 USA Kaohsiung Med Univ, Sch Publ Hlth, Kaohsiung, Taiwan Yale Univ, Sch Med, Dept Internal Med, New Haven, CT 06510 USA Chang Gung Mem Hosp, Dept Internal Med, Kaohsiung 83305, Taiwan RPI, Decis Sci & Engn, Troy, NY USA DE data mining; missing data; data visualization; discriminant analysis; correlation analysis; neural networks; heart disease AB Knowledge discovery from the dramatically increased data of an auto-stored medical information system is still in its infancy. The purpose of this study is to use widely available and easily operated techniques that can satisfy general users in extracting specific knowledge to make the medical information system more functional. Data mining techniques, including data visualisation, correlation analysis, discriminant analysis, and neural networks supervised classification, were applied to heart disease databases. These techniques can help to identify high risk patients, define the most important factors (variables) in heart disease, and build a multivariate relationship model to show the relationship between any two variables in a way that such relationships are easy to view. Simple visualization techniques were utilised to construct this model, which corresponds with current medical knowledge. Two nonparametric (distribution assumption fret) classification tools were employed to identify high risk heart disease patients. Both the neural networks supervised classification methods and thr discriminant analysis method produced reliable classification rates for heart disease patients. However, neural networks yielded a higher percentage of correct classifications (averaging 89%) than discriminant analysis (79%). Data visualisation and correlation anal! sis resulted in similar conclusions regarding the most important factors in heart disease. These data milling tools provide simple and effective methods of extracting knowledge from general medical information. The treatment of missing data is also discussed. CR EVANS J, 1997, MED INFORM, V22, P191 GENNARI JH, 1989, ARTIF INTELL, V40, P11 LINDSEY JC, 1998, AIDS PATIENT CARE ST, V12, P275 REISMAN Y, 1996, MED INFORM, V21, P179 SCHMIDT R, 1997, MED INFORM, V22, P237 TC 3 BP 81 EP 102 PG 22 JI Med. Inform. Internet Med. PY 2000 PD APR-JUN VL 25 IS 2 GA 330DZ PI LONDON RP Lee IN 12F,2 Lane 548,Chiu Ju 1st Rd, Kaohsiung, Taiwan J9 MED INFORM INTERNET MED PA 11 NEW FETTER LANE, LONDON EC4P 4EE, ENGLAND UT ISI:000087948700001 ER PT Journal AU Groller, E Hauser, H Ribarsky, W TI Data visualization - Introduction SO COMPUTERS & GRAPHICS-UK LA English DT Editorial Material NR 0 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Vienna Univ Technol, Inst Comp Graph, Vienna, Austria Vienna Univ Technol, Inst Comp Graph, Vienna, Austria Georgia Inst Technol, Graph Visualizat & Usabil Ctr, Atlanta, GA 30332 USA TC 0 BP 321 EP 324 PG 4 JI Comput. Graph.-UK PY 2000 PD JUN VL 24 IS 3 GA 328NM PI OXFORD RP Groller E Vienna Univ Technol, Inst Comp Graph, Vienna, Austria J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000087857000001 ER PT Journal AU Ebert, DS Rohrer, RM Shaw, CD Panda, P Kukla, JM Roberts, DA TI Procedural shape generation for multi-dimensional data visualization SO COMPUTERS & GRAPHICS-UK LA English DT Article NR 14 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Georgia Tech Coll Comp, 801 Atlantic Dr, Atlanta, GA 30332 USA Univ Maryland Baltimore Cty, CSEE Dept, Baltimore, MD 21250 USA George Washington Univ, Dept EECS, Washington, DC 20052 USA Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA DE two-handed 3D user interfaces; procedural shapes; superquadrics; implicit surfaces; scientific and information visualization AB Visualization of multi-dimensional data is a challenging task. The goal is not the display of multiple data dimensions, but user comprehension of the multi-dimensional data. This paper explores several techniques for perceptually motivated procedural generation of shapes to increase the comprehension of multi-dimensional data. Our glyph-based system allows the visualization of both regular and irregular grids of volumetric data. A glyph's location, 3D size, color and opacity encode up to 8 attributes of scalar data per glyph. We have extended the system's capabilities to explore shape variation as a visualization attribute. We use procedural shape generation techniques because they allow flexibility, data abstraction, and freedom from specification of detailed shapes. We have explored three procedural shape generation techniques: fractal detail generation, superquadrics, and implicit surfaces. These techniques allow from 1 to 14 additional data dimensions to be visualized using glyph shape. (C) 2000 Elsevier Science Ltd. All rights reserved. CR BARR AH, 1981, IEEE COMPUT GRAPH, V1, P11 BERTIN J, 1983, SEMIOLOGY GRAPHICS D CLEVELAND WS, 1985, ELEMENTS GRAPHING DA EBERT D, 1996, P IEEE VIS 96 OCT EBERT DS, 1997, COMPUTER SCI ENG HDB, PCH56 EBERT DS, 1988, TEXTURING MODELLING FOLEY JD, 1986, IEEE COMPUT GRAPH, V6, P16 PAKER A, 1992, UNDERSTANDING VISION, PCH8 PEARCE C, 1996, J AM SOC INFORM SCI, V47, P263 POST FJ, 1995, P VISUALIZATION 95, P288 RIBARSKY W, 1994, COMPUTER, V27, P57 ROHRER R, 1998, P IEEE S INF VIS 98, P121 SENAY H, 1994, IEEE COMPUT GRAPH, V14, P36 SENAY H, 1996, RULES PRINCIPLES SCI TC 0 BP 375 EP 384 PG 10 JI Comput. Graph.-UK PY 2000 PD JUN VL 24 IS 3 GA 328NM PI OXFORD RP Shaw CD Georgia Tech Coll Comp, 801 Atlantic Dr, Atlanta, GA 30332 USA J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000087857000007 ER PT Book in series AU Lewis, HG Brown, M Tatnall, ARL TI Incorporating uncertainty in land cover classification from remote sensing imagery SO REMOTE SENSING FOR LAND SURFACE CHARACTERISATION LA English DT Article NR 5 SN 0273-1177 PU PERGAMON PRESS LTD C1 Univ Southampton, Dept Elect & Comp Sci, Image Speech & Intelligent Syst Res Grp, Southampton SO17 1BJ, Hants, England Univ Southampton, Dept Elect & Comp Sci, Image Speech & Intelligent Syst Res Grp, Southampton SO17 1BJ, Hants, England Univ Southampton, Dept Aeronaut & Astronaut, Remote Sensing Res Grp, Southampton SO17 1BJ, Hants, England AB The mapping of land cover is an important application of remotely sensed data. Information in the form of area estimates has particular interest to Government agencies responsible for economic or environmental policy. Satellite sensors have been used to supplement the existing data collection methods used by these agencies but the uncertainty inherent in remote sensing data requires that a robust classification methodology is followed to achieve acceptable accuracy. The European Union project FLIERS (Fuzzy Land Information from Environmental Remote Sensing) aims to increase the accuracy of land cover area. estimates by incorporating and reducing the uncertainty in the data (Lewis et al., 1998). This article discusses uncertainty introduced to the data by the characteristics of the land cover classes of interest. Other sources of uncertainty, such as positional uncertainty, are beyond the scope of this article. Data visualization techniques, developed for project FLIERS, were used to explore and predict the effects of uncertainty on the accuracy of empirical area estimation models that used binary encoded and area proportion encoded data. These predictions were then tested using area estimation models, derived from a set of training data, applied to land cover classes from Landsat Thematic Mapper (TM) imagery. CR ATKINSON PM, 1995, MAPPING SUB PIXEL VA BISHOP CM, 1995, NEURAL NETWORKS PATT KENT JT, 1986, IEEE T PATTERN ANAL, V10, P659 LEWIS HG, 1998, GNE CONFUSION MATRIX LEWIS HG, 1998, ISIS398 U SOUTH DEP TC 1 BP 1123 EP 1126 PG 4 SE ADVANCES IN SPACE RESEARCH PY 2000 VL 26 IS 7 GA BQ23J PI OXFORD RP Lewis HG Univ Southampton, Dept Elect & Comp Sci, Image Speech & Intelligent Syst Res Grp, Southampton SO17 1BJ, Hants, England J9 ADV SPACE RES PA THE BOULEVARD LANGFORD LANE KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000087680700017 ER PT Journal AU Konig, A TI Interactive visualization and analysis of hierarchical neural projections for data mining SO IEEE TRANSACTIONS ON NEURAL NETWORKS LA English DT Article NR 14 SN 1045-9227 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 Dresden Univ Technol, D-8027 Dresden, Germany Dresden Univ Technol, D-8027 Dresden, Germany DE data mining; dimensionality reducing mappings; interactive multivariate data visualization; neural networks; visual exploratory data analysis AB Dimensionality reducing mappings, often also denoted as multidimensional scaling, are the basis for multivariate data projection and visual analysis in data mining. Topology and distance preserving mapping techniques-e.g., Kohonen's self- organizing feature map (SOM) or Sammon's nonlinear mapping (NLM)-are available to achieve multivariate data projections for the following interactive visual analysis process. For large data bases, however, NLM computation becomes intractable. Also, if additional data points or data sets are to be included in the projection, a complete recomputation of the mapping is required, In general, a neural network could learn the mapping and serve for arbitrary additional data projection. However, the computational costs would also be high, and convergence is not easily achieved, In this work, a convenient hierarchical neural projection approach is introduced, where first an unsupervised neural network-e.g., an SOM-quantizes the data base, followed by fast NLM mapping of the quantized data. In the second stage of the hierarchy, an enhancement of the NLM by a recall algorithm is applied. The training and application of a second neural network, which is learning the mapping by function approximation, is quantitatively compared with this new approach. Efficient interactive visualization and analysis techniques, exploiting the achieved hierarchical neural projection for data mining, are presented. CR FUKUNAGA K, 1990, INTRO STAT PATTERN R KOHLER C, 1999, P 3 INT C KNOWL, P397 KOHONEN T, 1989, SELF ORG ASS MEMORY KONIG A, 2000, FEATURE ANAL CLUSTER, V3, P1 KONIG A, 1999, IMAGE PROCESS EU SEP, P10 KONIG A, 1998, P 5 INT C SOFT COMP, P55 KONIG A, 2000, QUICKCOG PAL NR, 1998, IEEE T NEURAL NETWOR, V9, P1 PLATT J, 1991, NEURAL COMPUT, V3, P213 RUMELHART DE, 1986, PARALLEL DISTRIBUTIO, V1 SAMMON JW, 1970, IEEE T COMPUT, V19, P594 SAMMON JW, 1969, IEEE T COMPUT, V18, P401 WANG H, 1995, J PROPUL POWER, V11, P385 ZAHN CT, 1971, IEEE T COMPUT, V20, P68 TC 1 BP 615 EP 624 PG 10 JI IEEE Trans. Neural Netw. PY 2000 PD MAY VL 11 IS 3 GA 326JM PI NEW YORK RP Konig A Dresden Univ Technol, D-8027 Dresden, Germany J9 IEEE TRANS NEURAL NETWORKS PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000087732100007 ER PT Journal AU Wang, Y Luo, L Freedman, MT Kung, SY TI Probabilistic principal component subspaces: A hierarchical finite mixture model for data visualization SO IEEE TRANSACTIONS ON NEURAL NETWORKS LA English DT Article NR 25 SN 1045-9227 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 Catholic Univ Amer, Dept Elect Engn & Comp Sci, Washington, DC 20064 USA Catholic Univ Amer, Dept Elect Engn & Comp Sci, Washington, DC 20064 USA Georgetown Univ, Dept Radiol, Washington, DC 20007 USA Georgetown Univ, Lombardi Canc Ctr, Washington, DC 20007 USA Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA DE computer-aided diagnosis; data visualization; hierarchical mixture distribution; information theoretic criteria; principal component neural network; soft clustering ID NETWORKS AB Visual exploration has proven to be a powerful tool for multivariate data mining and knowledge discovery, Most visualization algorithms aim to find a projection from the data space down to a visually perceivable rendering space, To reveal all of the interesting aspects of multimodal data sets living in a high-dimensional space, a hierarchical visualization algorithm is introduced which allows the complete data set to be visualized at the top level, with clusters and subclusters of data points visualized at deeper levels, The methods involve hierarchical use of standard finite normal mixtures and probabilistic principal component projections, whose parameters are estimated using the expectation-maximization and principal component neural networks under the information theoretic criteria, We demonstrate the principle of the approach on several multimodal numerical data sets, and we then apply the method to the visual explanation in computer-aided diagnosis for breast cancer detection from digital mammograms. CR AKAIKE H, 1974, IEEE T AUTOMATIC CON, V19, P716 BISHOP CM, 1998, IEEE T PATTERN ANAL, V20, P282 BRACEWELL RN, 1995, 2 DIMENSIONAL IMAGIN COVER TM, 1991, ELEMENTS INFORMATION ETEMAD K, 1998, IEEE T IMAGE PROCESS, V7 GOULB TR, 1999, SCIENCE, V286, P531 GRAY R, 1986, RANDOM PROCESSES MAT HAYKIN S, 1999, NEURAL NETWORKS COMP HINTON GE, 1997, IEEE T NEURAL NETWOR, V8, P65 JAYNES ET, 1957, PHYS REV, V106, P620 JORDAN MI, 1994, NEURAL COMPUT, V6, P181 KAMBHATLA N, 1997, NEURAL COMPUT, V9, P1493 KUNG SY, 1994, IEEE T SIGNAL PROCES, V42, P1202 KUNG SY, 1996, PRINCIPAL COMPONENT LUO L, 1999, P IEEE WORKSH AUG NIELSON GM, 1996, IEEE T VIS COMPUT GR, V2, P97 PERLOVSKY LI, 1991, NEURAL NETWORKS, V4, P89 RISSANEN J, 1978, AUTOMATICA, V14, P465 RISSANEN J, 1987, SYSTEM IDENTIFICATIO, P97 ROBERTS SJ, 1998, IEEE T PATTERN ANAL, V20, P1133 TIPPING ME, 1999, NEURAL COMPUT, V11, P443 TITTERINGTON DM, 1985, STAT ANAL FINITE MIX TUFTE ER, 1996, VISUAL EXPLANATION I WANG Y, 1998, IEEE T SIGNAL PROCES, V46, P3378 WAX M, 1985, IEEE T ACOUST SP APR TC 3 BP 625 EP 636 PG 12 JI IEEE Trans. Neural Netw. PY 2000 PD MAY VL 11 IS 3 GA 326JM PI NEW YORK RP Wang Y Catholic Univ Amer, Dept Elect Engn & Comp Sci, Washington, DC 20064 USA J9 IEEE TRANS NEURAL NETWORKS PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000087732100008 ER PT Book in series AU Bhowmick, SS Madria, SK Ng, WK Lim, EP TI Data visualization in a web warehouse SO ADVANCES IN DATABASE TECHNOLOGIES LA English DT Article NR 16 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Nanyang Technol Univ, Sch Appl Sci, Ctr Adv Informat Syst, Singapore 639798, Singapore Nanyang Technol Univ, Sch Appl Sci, Ctr Adv Informat Syst, Singapore 639798, Singapore AB The effective representation and manipulation of web data is currently an active area of research in databases. In a web warehouse, web infer mation coupling provides the means to derive useful information from the WWW. Web information is materialized in the form of web tuples and stored in web tables. In this paper, we discuss web data visualization operators such as web nest, web coalesce, web pack and web sort to provide users with the flexibility to view sets of web documents in perspectives which may be more meaningful. CR ABITEBOUL S, 1997, J DIGITAL LIB, V1, P68 AROCENA G, 1998, P ICDE 98 ORL FLOR F BHOWMICK S, 1998, CAISTR9820 NAN TECHN BHOWMICK S, 1998, P 17 INT C CONC MOD BHOWMICK S, 1998, P 5 INT C FOUND DAT BHOWMICK S, 1998, P INT WORKSH DAT WAR BHOWMICK SS, 1998, P 9 INT C DAT EXP SY BUNEMAN P, 1996, P ACM SIGMOD INT C M FERNANDEZ M, 1997, SIGMOD RECORD, V26 FIEBIG T, 1997, WORKSH MAN SEM DAT P KONOPNICKI D, 1995, P 21 INT C VER LARG LAKSHMANAN LVS, 1996, P 6 INT WORKSH RES I LIM EP, 1998, P 1 AS DIG LIBR WORK LIU M, 1998, P 17 INT C CONC MOD MENDELZON AO, P INT C PAR DISTR IN NG WK, 1998, P IEEE INT C ADV DIG TC 0 BP 68 EP 80 PG 13 SE LECTURE NOTES IN COMPUTER SCIENCE PY 1999 VL 1552 GA BQ22V PI BERLIN RP Bhowmick SS Nanyang Technol Univ, Sch Appl Sci, Ctr Adv Informat Syst, Singapore 639798, Singapore J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000087625100006 ER PT Book in series AU Bhowmick, SS Madria, SK Ng, WK Lim, EP TI Web warehousing: Design and issues SO ADVANCES IN DATABASE TECHNOLOGIES LA English DT Article NR 22 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Nanyang Technol Univ, Sch Appl Sci, Ctr Adv Informat Syst, Singapore 639798, Singapore Nanyang Technol Univ, Sch Appl Sci, Ctr Adv Informat Syst, Singapore 639798, Singapore AB The World Wide Web is a distributed global information resource. It contains a large amount of information that have been placed on the web independently by different organizations and thus, related information may appear across different web sites. To manage and access heterogeneous information on WWW, we have started a project of building a web warehouse, called WHOWEDA ( Warehouse of Web Data). Currently, our work on building a web warehousing system has focused on building a data model and designing a web algebra. In this paper, we discuss design and research issues in a web warehousing system. The issues include are designing algebraic operators for web information access and manipulation, web data visualization and web knowledge discovery. These issues will not only overcome the limitations of available search engines but also provide powerful and friendly query mechanisms for retrieving useful information and knowledge discovery from a web warehouse. CR ABITEBOUL S, 1997, J DIGITAL LIB, V1, P68 AROCENA G, 1998, P INT C DAT ENG ORL BHOWMICK S, 1998, P 17 INT C CONC MOD BHOWMICK S, 1998, P 5 INT C FDN DAT OR BHOWMICK S, 1998, P 9 INT C DAT EXP SY BHOWMICK S, 1998, P INT WORKSH DAT WAR BHOWMICK S, 1998, PRICAI98 WORKSH KNOW BHOWMICK S, UNPUB BAGS WEB WAREH BRAY T, 1996, P 5 INT WORLD WID WE BUNEMAN P, 1996, P ACM SIGMOD INT C M FERNANDEZ M, 1997, SIGMOD RECORD, V26 FIEBIG T, 1997, WORKSH MAN SEM DAT P HAN JW, 1996, IEEE T KNOWL DATA EN, V8, P373 KONOPNICKI D, 1995, P 21 INT C VER LARG LAKSHMANAN LVS, 1996, P 6 INT WORKSH RES I LIN SH, 1996, P 6 INT WORKSH RES I LIU M, 1998, P 17 INT C CONC MOD MADRIA SK, UNPUB QUERY PROCESSI MENDELZON AO, P INT C PAR DISTR IN MOBASHER B, 1997, P 9 IEEE INT C TOOLS NG WK, 1998, P IEEE INT C ADV DIG ZAINE OR, 1998, P IEEE INT C ADV DIG TC 0 BP 93 EP 104 PG 12 SE LECTURE NOTES IN COMPUTER SCIENCE PY 1999 VL 1552 GA BQ22V PI BERLIN RP Bhowmick SS Nanyang Technol Univ, Sch Appl Sci, Ctr Adv Informat Syst, Singapore 639798, Singapore J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000087625100008 ER PT Journal AU Dicks, J TI Graphical tools for comparative genome analysis SO YEAST LA English DT Review NR 15 SN 0749-503X PU JOHN WILEY & SONS LTD C1 John Innes Ctr Plant Sci Res, Norwich Res Pk, Norwich NR4 7UH, Norfolk, England John Innes Ctr Plant Sci Res, Norwich NR4 7UH, Norfolk, England DE graphical tools; comparative genome analysis; Java AB Visualization of data is important for many data-rich disciplines. In biology, where data sets are becoming larger and more complex, graphical analysis is felt to be ever more pertinent. Although some patterns and trends in data sets may only be determined by sophisticated computational analysis, viewing data by eye can provide us with an extraordinary amount of information in an instant. Recent advances in bioinformatic technologies allow us to link graphical tools to data sources with ease, so we can visualize our data sets dynamically. Here, an overview of graphical software tools for comparative genome analysis is given, showing that a range of simple tools can provide us with a powerful view of the differences and similarities between genomes. Copyright (C) 2000 John Wiley & Sons, Ltd. CR ATTWOOD TK, 1995, COLOUR INTERACTIVE E CARROLL L, 1865, ALICES ADVENTURES WO CLAMP M, 1998, JALVIEW ANAL MANIPUL DAVENPORT GF, 1999, GENOME MAP VIEWER DAVENPORT GF, 1999, PROTOCOMAPDB PROTOTY DICKS J, 1997, GENETIC MAPPING DIS, P221 DICKSON JS, 1998, COMP PHYSICAL GENETI DICKSON JS, 1998, PAIRWISE COMP MAP DURBIN R, 1991, ACEDB EDWARDS JH, 1991, ANN HUM GENET, V55, P17 JAREBORG N, 1998, ALFRESCO MCWILLIAM H, 1999, CORBA INTERFACE ACED MOORE G, 1995, CURR BIOL, V5, P737 MUNGALL C, 1997, ANUBIS PECHERER RM, 1999, COMP GENOME MAPPING TC 2 BP 6 EP 15 PG 10 JI Yeast PY 2000 PD APR VL 17 IS 1 GA 323JM PI W SUSSEX RP Dicks J John Innes Ctr Plant Sci Res, Norwich Res Pk, Norwich NR4 7UH, Norfolk, England J9 YEAST PA BAFFINS LANE CHICHESTER, W SUSSEX PO19 1UD, ENGLAND UT ISI:000087562300003 ER PT Journal AU James, CN Brodzik, SR Edmon, H Houze, RA Yuter, SE TI Radar data processing and visualization over complex terrain SO WEATHER AND FORECASTING LA English DT Article NR 20 SN 0882-8156 PU AMER METEOROLOGICAL SOC C1 Embry Briddle Aeronaut Univ, Meteorol Lab, 3200 Willow Creek Rd, Prescott, AZ 86301 USA Embry Briddle Aeronaut Univ, Meteorol Lab, Prescott, AZ 86301 USA Univ Washington, Dept Atmospher Sci, Seattle, WA 98195 USA ID CYCLOGENESIS; LEE; DISPLAY; ALPS AB MountainZebra is a data flow configuration that processes and displays radar data over complex terrain. The system combines three elements: the data stream from an operational radar, 3D topographical information, and the NCAR Zebra data visualization and integration software. MountainZebra operates routinely on a 3D data stream from the National Weather Service Weather Surveillance Radar-1988 Doppler (WSR-88D) at Camano Island, Washington (near Seattle). The WSR-88D data are continuously acquired, archived, formatted, and interpolated for multidimensional display. The three-dimensional topographical information in MountainZebra can be automatically underlaid on any horizontal or vertical display of the radar data. This system allows radar data and other geophysical fields to be analyzed in precise relation to the underlying terrain. Terrain-based visualization facilitates radar data analysis by identifying terrain clutter and shadowing and by identifying orographic precipitation mechanisms. The utility of MountainZebra is illustrated in the investigation of stable orographic enhancement over the windward slopes of the Cascade Mountains of the Pacific Northwest and an orographically enhanced squall line to the lee of the European Alps. CR AEBISCHER U, 1998, J ATMOS SCI, V55, P186 BARNES SL, 1980, B AM METEOROL SOC, V61, P1401 BELL GD, 1988, MON WEA REV, V116, P137 BUZZI A, 1978, Q J R MET SOC, V104, P271 CORBET J, 1994, B AM METEOROL SOC, V75, P783 CRUM TD, 1993, B AM METEOROL SOC, V74, P645 DOICK JJ, 1995, 27 C RAD MET AM MET, P371 DOVIAK RJ, 1993, DOPPLER RADAR WEATHE GUSTAVSSON T, 1998, J APPL METEOROL, V37, P559 HOBBS PV, 1975, J ATMOS SCI, V32, P1542 HOUZE RA, 1993, CLOUD DYNAMICS JOSS J, 1998, 31 NRP ETH JOSS J, 1995, J APPL METEOROL, V34, P2612 LIN CC, 1997, J HOPKINS APL TECH D, V18, P432 MAJEWSKI D, 1991, P ECMWF SEM NUM METH, V2, P147 MCGINLEY J, 1982, MON WEA REV, V110, P1271 MOHR CG, 1979, J APPL METEOROL, V18, P661 RHUE DT, 1995, 11 INT C INT INF PRO, P235 SKAMAROCK WC, 1994, J ATMOS SCI, V51, P2563 SMITH RB, 1979, ADV GEOPHYS, V21, P87 TC 3 BP 327 EP 338 PG 12 JI Weather Forecast. PY 2000 PD JUN VL 15 IS 3 GA 323AZ PI BOSTON RP James CN Embry Briddle Aeronaut Univ, Meteorol Lab, 3200 Willow Creek Rd, Prescott, AZ 86301 USA J9 WEATHER FORECAST PA 45 BEACON ST, BOSTON, MA 02108-3693 USA UT ISI:000087543800005 ER PT Journal AU Ma, C Chou, DC Yen, DC TI Data warehousing, technology assessment and management SO INDUSTRIAL MANAGEMENT & DATA SYSTEMS LA English DT Article NR 19 SN 0263-5577 PU MCB UNIV PRESS LTD C1 Miami Univ, Oxford, OH 45056 USA Miami Univ, Oxford, OH 45056 USA St Cloud State Univ, St Cloud, MN 56301 USA DE administration; data processing; decision-support systems ID SYSTEMS AB Data warehousing is the techno logical trend for the corporate decision support process. This article investigates the current business environment of the data warehouse, including OLAP, data mining, data visualization and other technologies. This article also analyzes the importance of data warehouse management and maintenance and its future developments. CR BARQUIN F, 1997, BUILDING USING MANAG BATRA D, 1995, EUR J INFORM SYST, V4, P185 BROOKS PL, 1997, DBMS, V10, P38 BROWN AJ, 1995, UNIX REV, V13, P39 BURK CF, 1988, INFOMAP COMPLETE GUI CHEN PPS, 1976, ACM T DATABASE SYST, V1, P9 DEVLIN B, 1997, DATA WAREHOUSE ARCHI DOLK DR, 1987, COMMUN ACM, V30, P48 HUFFORD D, 1997, PLANNING DESIGNING D INMON WH, 1992, DATABASE MANAGEM FEB LEVIN EJ, 1996, PLANNING DESIGNING D MATTISON R, 1996, DATA WAREHOUSING STR MCFADDEN FR, 1996, P 29 HAW INT C SYST, V2, P120 RADERMACHER FJ, 1994, DECIS SUPPORT SYST, V12, P257 REEVES L, 1995, DATA MANAGEMENT REV, V5, P12 RUDIN K, 1996, DBMS, V9, PS4 SMALL RD, 1997, SCALABLE DATA MINING WIDOM J, 1995, P 4 INT C INF KNOWL, P25 ZACHMAN JA, 1987, IBM SYST J, V26, P276 TC 0 BP 125 EP 134 PG 10 JI Ind. Manage. Data Syst. PY 2000 VL 100 IS 3-4 GA 323CY PI BRADFORD RP Ma C Miami Univ, Oxford, OH 45056 USA J9 INDUSTRIAL MANAGE DATA SYST PA 60/62 TOLLER LANE, BRADFORD BD8 9BY, W YORKSHIRE, ENGLAND UT ISI:000087548300004 ER PT Book in series AU Andrienko, G Andrienko, N TI Data characterization schema for intelligent support in visual data analysis SO SPATIAL INFORMATION THEORY LA English DT Article NR 11 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 GMD German Natl Res Ctr Informat Technol, Schloss Birlinghoven, D-53754 St Augustin, Germany GMD German Natl Res Ctr Informat Technol, D-53754 St Augustin, Germany DE geographically referenced data; conceptual data characterization; knowledge-based systems; cartographic visualization ID DESIGN AB The project CommonGIS(1) aims at building a system allowing users to view and analyze geographically referenced thematic data. The system is oriented to the general public, i.e. people without special training and expertise in map design. Therefore the system is required to understand data semantics that, hence, must be formally represented. The project involves development of a data characterization schema that defines what knowledge about the data and in what form should be provided to the system for enabling intelligent and user-friendly support in visual data analysis. In this paper we propose a schema developed on the basis of the approach adopted in the system Descartes for automated thematic mapping. The approach involves creation of a domain model containing relevant notions and establishing of a correspondence between data components and the notions. Presence of the domain model is the main difference of the described schema from the ones proposed earlier for the purposes of automated data visualization. CR *URL, UN MOD LANG NOT GUID ANDRIENKO G, 1999, IN PRESS INT J GEOGR, V13 ANDRIENKO G, 1999, P AGILE 99 C ROM APR, P19 ANDRIENKO GL, 1998, P ADV VIS INT 98 NEW, P66 BERTIN J, 1967, SEMIOLOGY GRAPHICS D JUNG W, 1995, P 3 ACM INT WORKSH A, P101 KLIR GJ, 1985, ARCHITECTURE SYSTEMS MACEACHREN AM, 1995, MAPS WORK REPRESENTA MACKINLAY J, 1986, ACM T GRAPHIC, V5, P110 ROTH SF, 1990, P CHI 90, P193 SENAY H, 1994, IEEE COMPUT GRAPH, V14, P36 TC 0 BP 349 EP 365 PG 17 SE LECTURE NOTES IN COMPUTER SCIENCE PY 1999 VL 1661 GA BP96X PI BERLIN RP Andrienko G GMD German Natl Res Ctr Informat Technol, Schloss Birlinghoven, D-53754 St Augustin, Germany J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000086779100023 ER PT Journal AU Tice, RR Agurell, E Anderson, D Burlinson, B Hartmann, A Kobayashi, H Miyamae, Y Rojas, E Ryu, JC Sasaki, YF TI Single cell gel/comet assay: Guidelines for in vitro and in vivo genetic toxicology testing SO ENVIRONMENTAL AND MOLECULAR MUTAGENESIS LA English DT Article NR 63 SN 0893-6692 PU WILEY-LISS C1 Integrated Lab Syst Inc, POB 13501, Res Triangle Pk, NC 27709 USA Integrated Lab Syst Inc, Res Triangle Pk, NC 27709 USA AB Astra, Safety Assessment, Sodertalje, Sweden BIBRA Int, Surrey, England Glaxo Wellcome, Ware, Herts, England Novartis Pharma AG, Basel, Switzerland Shiseido Co Ltd, Safety & Analyt Res Ctr, Yokohama, Kanagawa, Japan Fujisawa Pharmaceut Co Ltd, Toxicol Res Labs, Osaka 532, Japan UNAM, Inst Invest Biomed, Mexico City, DF, Mexico Korea Inst Sci & Technol, Toxicol Lab, Seoul 130650, South Korea DE single cell gel assay; Comet assay; DNA damage; genotoxicity; alkaline electrophoresis ID ELECTROPHORESIS COMET ASSAY; SPRAGUE-DAWLEY RATS; INDUCED DNA- DAMAGE; GEL-ELECTROPHORESIS; MUTAGENICITY TEST; HUMAN- LYMPHOCYTES; INDIVIDUAL CELLS; STRAND BREAKS; GENOTOXICITY; APOPTOSIS AB At the International Workshop on Genotoxicity Test Procedures (IWGTP) held in Washington, DC, March 25-26, 1999, an expert panel met to develop guidelines for the use of the single-cell gel (SCG)/Comet assay in genetic toxicology. The expert panel reached a consensus that the optimal version of the Comet assay for identifying agents with genotoxic activity was the alkaline (pH, 13) version of the assay developed by Singh et al. [1988]. The pH > 13 version is capable of detecting DNA single-strand breaks (SSB), alkali-labile sites (ALS), DNA-DNA/DNAprotein cross-linking, and SSB associated with incomplete excision repair sites. Relative to at her genotoxicity tests, the advantages of the SCG assay include its demonstrated sensitivity for detecting low levels of DNA damage, the requirement for small numbers of cells per sample, its flexibility, its low costs, ifs ease of application, and the short time needed to complete a study. The expert panel decided that no single version of the alkaline (pH > 13) Comet assay was clearly superior. However, critical technical steps within the assay were discussed and guidelines developed for preparing slides with agarose gels, lysing cells to liberate DNA, exposing the liberated DNA to alkali to produce single-stranded DNA and to express ALS as SSB, electrophoresing the DNA using pH > 13 alkaline conditions, alkali neutralization, DNA staining, comet visualization, and data collection. Based on the current state of knowledge, the expert panel developed guidelines for conducting in vitro or in vivo Comet assays. The goal of the expert panel was to identify minimal standards for obtaining reproducible and reliable Comet data deemed suitable for regulatory submission, The expert panel used the current Organization for Economic Go-operation and Development (OECD) guidelines Far in vitro and in vivo genetic toxicological studies as guides during the development of the corresponding in vitro and in vivo SCG assay guidelines. Guideline topics considered included initial considerations, principles of the test method, description of the test method, procedure, results, data analysis and reporting. Special consideration was given by the expert panel to the potential adverse effect of DNA degradation associated with cytotoxicity on the interpretation of Comet assay results. The expert panel also discussed related SCG methodologies that might be useful in the interpretation of positive Comet data, The related methodologies discussed included: (1) the use of different pH conditions during electrophoreses to discriminate between DNA strand breaks and ALS (2) the use of repair enzymes or antibodies to detect specific classes of DNA damage; (3) the use of a neutral diffusion assay to identify apoptotic/necrotic cells; and (4) the use of the acellular SCG assay to evaluate the ability of a test substance to interact directly with DNA. The alkaline (pH > 13) Comet assay guidelines developed by the expert panel represent a work in progress. Additional information is needed before the assay can be critically evaluated for its utility in genetic toxicology. The information needed includes comprehensive data on the different sources of variability (e.g., cell to cell, gel to gel, run to run, culture to culture, animal to animal, experiment to experiment) intrinsic to the alkaline (pH > 3) SCG assay, the generation of a large database based on in vitro and in vivo testing using these guidelines, and the results of appropriately designed multilaboratory international validation studies. (C) 2000 Wiley-Liss, Inc. 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Mol. Mutagen. PY 2000 VL 35 IS 3 GA 300ZF PI NEW YORK RP Tice RR Integrated Lab Syst Inc, POB 13501, Res Triangle Pk, NC 27709 USA J9 ENVIRON MOL MUTAGEN PA DIV JOHN WILEY & SONS INC, 605 THIRD AVE, NEW YORK, NY 10158- 0012 USA UT ISI:000086283100008 ER PT Journal AU Birken, R Versteeg, R TI Use of four-dimensional ground penetrating radar and advanced visualization methods to determine subsurface fluid migration SO JOURNAL OF APPLIED GEOPHYSICS LA English DT Article NR 11 SN 0926-9851 PU ELSEVIER SCIENCE BV C1 Columbia Univ, Lamont Doherty Earth Observ, 61 Route 9W, Palisades, NY 10964 USA Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY 10964 USA DE GPR; modeling; visualization; hydrogeology; monitoring; DNAPLS ID CONTROLLED DNAPL RELEASE AB Four-dimensional (4D) or time-lapse three-dimensional (3D) ground penetrating radar surveys can be used to monitor and image subsurface fluid flow. This information can be used to create a model of hydrogeological properties. The massive amount of data, which is present in and can possibly be generated from 4D GPR data sets, precludes a manual interpretation. Consequently, 4D data sets have to be processed and visualized in a way that extracts models and allows for data visualization in a semi-automatic way. The principles behind such an approach are applied to the Borden data set, which is used to demonstrate how advanced visualization can assist in the interpretation of raw and processed data. In the Borden data set, changes in reflectivity between different time-steps unveil areas of fluid migration in three dimensions. The combination of these reflectivity changes (between different combinations of the 3D subsets of the 4D data set) is used to create a model of hydrogeological properties. While this model does not yield a quantitative description of porosity, permeability or hydraulic conductivity, it is a qualitative proxy for a combination of these properties. (C) 2000 Elsevier Science B.V. All rights reserved. CR ANDERSON EG, 1993, POSTGRAD MED, V94, P23 ANDERSON RN, 1991, OIL GAS J, V89, P60 BERGMANN T, 1996, GEOPHYS RES LETT, V23, P45 BREWSTER ML, 1994, GEOPHYSICS, V59, P1211 BREWSTER ML, 1995, GROUND WATER, V33, P977 LANE JW, 1996, GPR 96 6 INT C GROUN, P185 PANKOW JF, 1995, DENSE CHLORINATED SO ROBERTSSON JOA, 1994, GEOPHYSICS, V59, P1444 SANDER KA, 1994, CHARACTERIZATION DNA SANDER KA, 1995, USGS DIGITAL DATA SE, V25 XU T, 1997, GEOPHYSICS, V62, P403 TC 0 BP 215 EP 226 PG 12 JI J. Appl. Geophys. PY 2000 PD MAR VL 43 IS 2-4 GA 294WG PI AMSTERDAM RP Birken R Columbia Univ, Lamont Doherty Earth Observ, 61 Route 9W, Palisades, NY 10964 USA J9 J APPL GEOPHYS PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000085933000010 ER PT Journal AU Wanstedt, S Carlsten, S Tiren, S TI Borehole radar measurements aid structure geological interpretations SO JOURNAL OF APPLIED GEOPHYSICS LA English DT Article NR 6 SN 0926-9851 PU ELSEVIER SCIENCE BV C1 GEOSIGMA, Box 894, S-75108 Uppsala, Sweden GEOSIGMA, S-75108 Uppsala, Sweden DE borehole investigations; directional borehole radar; tomography; structural geology AB Successful site characterization for a repository of nuclear waste or underground construction in general provides basic data concerning engineering aspects of repository design with impact on both the efficiency of the repository in isolating waste and in constructing the repository. Three-dimensional (3D) visualization of data is an essential step in the development of descriptive hydrologic and rock-mechanical models of fractured rock systems. Modeling of fractures and fracture zones in 3D is usually based on the correlation of fracturing in drillcores and on outcrops. Difficulties with this procedure arise when the vertical or horizontal separation between fractures is large. Directional radar surveys help decrease the uncertainty in correlation. At great depths, such as is the case when investigating potential nuclear waste repositories, the errors present as borehole deviations, and dip determinations of structures as well as the varying characteristics of geological features, may make interpretations virtually impossible. Tomographic radar measurements help improve the 3D modeling because zones with anomalous properties can be traced across the investigated plane or volume. This leads to a further decrease in uncertainty and eventually to better models. The comparison of directional reflection surveys and tomography shows that the accuracy of single-hole surveys is reasonably good. (C) 2000 Elsevier Science B.V. All rights reserved. CR *SKB, 1992, 9220 TR SKB SWED NUC CARLSTEN S, 1996, PATU9652E OLSSON O, 1990, 8711 OECDNEA SWED NU TIREN SA, 1996, 9616 SKI SWED NUCL P TIREN SA, 1998, P 3 ASP INT SEM CHAR, P99 WEST G, 1981, INT J ROCK MECH MIN, V18, P345 TC 1 BP 227 EP 237 PG 11 JI J. Appl. Geophys. PY 2000 PD MAR VL 43 IS 2-4 GA 294WG PI AMSTERDAM RP Wanstedt S GEOSIGMA, Box 894, S-75108 Uppsala, Sweden J9 J APPL GEOPHYS PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000085933000011 ER PT Journal AU Williams, A Kolovanov, E TI A chromatography data system with integrated molecular structure management SO AMERICAN LABORATORY LA English DT Article NR 0 SN 0044-7749 PU INT SCIENTIFIC COMMUN INC C1 Adv Chem Dev, 133 Richmond St W,Ste 605, Toronto, ON M5H 2L3, Canada Adv Chem Dev, Toronto, ON M5H 2L3, Canada ACD Inc, Phys Property Predict & Chromatog, Moscow, Russia AB Many of the currently available chromatography data systems focus primarily on data processing; that is, collecting data, visualization, archiving, and so forth. Accordingly, abundant amounts of chromatographic data are collected annually. Structural data on the compounds separated are carried in associated analytical laboratories. However, often there is very little effort made to link the chromatography data with the structural data-a short-coming that is addressed in this article. TC 1 BP 22 EP + PG 5 JI Am. Lab. PY 2000 PD MAR VL 32 IS 6 GA 295DQ PI SHELTON RP Williams A Adv Chem Dev, 133 Richmond St W,Ste 605, Toronto, ON M5H 2L3, Canada J9 AMER LAB PA PO BOX 870, 30 CONTROLS DRIVE, SHELTON, CT 06484-0870 USA UT ISI:000085951800002 ER PT Journal AU Takahashi, H Hosokawa, Y Furukawa, K Yoshimura, H TI Visualization of an oxygen-deficient bottom water circulation in Osaka Bay, Japan SO ESTUARINE COASTAL AND SHELF SCIENCE LA English DT Article NR 5 SN 0272-7714 PU ACADEMIC PRESS LTD C1 MOT, Port & Harbour Res Inst, 3-1-1 Nagase, Yokosuka, Kanagawa 2390826, Japan MOT, Port & Harbour Res Inst, Yokosuka, Kanagawa 2390826, Japan DE estuarine circulation; water quality distribution; dissolved oxygen; stratification; environmental data visualization; 3-D contour surface AB A visualization system that can analyse integrated images of time and spatially dependent data has been developed. The system was used to analyse coastal environmental monitoring data sets obtained from Osaka Bay, Japan. The visualization of water temperature, salinity, and dissolved oxygen (DO) in the spring, summer and autumn revealed an oxygen deficient water circulation in the inner part of the bay. The circulation had a strong correlation with vertical stratification. In addition, the speed of the oxygen deficient water mass was c. 1.2 cm s (- 1), and the mass circulated counter-clockwise in the inner part of the bay. (C) 2000 Academic Press. CR CULBERSON SD, 1996, ECOL MODEL, V89, P231 NAKAMURA Y, 1988, JSCE, V35, P802 NAKATSUJI K, 1994, FLUX CHANGES ESTUARI, P79 TANIMOTO T, 1997, J OCEANOGRAPHY, V53, P365 YAMANE N, 1998, JSCE, V45, P961 TC 0 BP 81 EP 84 PG 4 JI Estuar. Coast. Shelf Sci. PY 2000 PD JAN VL 50 IS 1 GA 292DM PI LONDON RP Takahashi H MOT, Port & Harbour Res Inst, 3-1-1 Nagase, Yokosuka, Kanagawa 2390826, Japan J9 ESTUAR COAST SHELF SCI PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND UT ISI:000085778900012 ER PT Journal AU Brown, TJ Mielke, PW TI Statistical mining and data visualization in atmospheric sciences SO DATA MINING AND KNOWLEDGE DISCOVERY LA English DT Editorial Material NR 0 SN 1384-5810 PU KLUWER ACADEMIC PUBL C1 Desert Res Inst, Reno, NV 89506 USA Desert Res Inst, Reno, NV 89506 USA Univ Nevada, Atmospher Sci Program, Reno, NV 89557 USA Colorado State Univ, Ft Collins, CO 80523 USA TC 0 BP 5 EP 6 PG 2 JI Data Min. Knowl. Discov. PY 2000 PD APR VL 4 IS 1 GA 289HY PI DORDRECHT RP Brown TJ Desert Res Inst, Reno, NV 89506 USA J9 DATA MIN KNOWL DISCOV PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS UT ISI:000085615500001 ER PT Journal AU Ault, JS Luo, JG Smith, SG Serafy, JE Wang, JD Humston, R Diaz, GA TI A spatial dynamic multistock production model SO CANADIAN JOURNAL OF FISHERIES AND AQUATIC SCIENCES LA English DT Article NR 89 SN 0706-652X PU NATL RESEARCH COUNCIL CANADA C1 Univ Miami, Rosenstiel Sch Marine & Atmospher Sci, 4600 Rickenbacker Causeway, Miami, FL 33149 USA Univ Miami, Rosenstiel Sch Marine & Atmospher Sci, Miami, FL 33149 USA ID EARLY LIFE-HISTORY; SPOTTED SEA-TROUT; CYNOSCION-NEBULOSUS; FLORIDA BAY; EXPLICIT MODELS; GRAY SNAPPER; GROWTH; SEATROUT; TEMPERATURE; SELECTION AB We developed a generalized spatial dynamic age-structured multistock production model by linking bioenergetic principles of physiology, population ecology, and community trophodynamics to a two-dimensional finite-element hydrodynamic circulation model. Animal movement is based on a search of an environmental-habitat feature vector that maximizes cohort production dynamics. We implemented a numerical version of the model and used scientific data visualization to display real- time results. As a proxy for larger regional-scale dynamics, we applied the model to study the space-time behavior of recruitment and predator-prey production dynamics for cohorts of spotted seatrout (Cynoscion nebulosus) and pink shrimp (Penaeus duorarum) in the tropical waters of Biscayne Bay, Florida. 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J. Fish. Aquat. Sci. PY 1999 VL 56 SU 1 GA 288YG PI OTTAWA RP Ault JS Univ Miami, Rosenstiel Sch Marine & Atmospher Sci, 4600 Rickenbacker Causeway, Miami, FL 33149 USA J9 CAN J FISHERIES AQUAT SCI PA RESEARCH JOURNALS, MONTREAL RD, OTTAWA, ONTARIO K1A 0R6, CANADA UT ISI:000085591600002 ER PT Journal AU Dransch, D TI The use of different media in visualizing spatial data SO COMPUTERS & GEOSCIENCES LA English DT Article NR 16 SN 0098-3004 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Univ Rostock, Inst Geodesy & Geoinformat, D-18059 Rostock, Germany Univ Rostock, Inst Geodesy & Geoinformat, D-18059 Rostock, Germany DE multimedia; visualization; media function; communication; cognition AB Multimedia technology changes the visualization of spatial data. The map, the traditional presentation of spatial data, is complemented by other media like pictures, animation, sound and video. Each of these additional media has particular abilities to communicate information. For instance some media are suitable to give a vivid picture about a phenomenon and others, on the contrary, are efficient to mediate the idea of spatial concepts. The selection and combination of different media for data visualization in cartography and geosciences have to be guided by two different aspects: by the particular function a medium has to fulfil in a multimedia presentation and by the application context. The functions of media can be derived from human cognition process and from communication purpose. Cognition process explains how media may support perception and cognition. Communication purpose determines what type of media are suitable. The application context of a multimedia presentation is either visual thinking, the graphical exploration or verification of spatial data by an expert or visual communication, the demonstration of spatial data for non-experts. In the first case media have to assist the scientist in finding questions from unstructured data sets and in verifying a derived hypothesis; in the second case media have to present information in a way that people without great knowledge of a subject can perceive and understand that subject. The media design for visual thinking and visual communication have to regard the media functions which were derived from cognition process and communication purpose. (C) 2000 Elsevier Science Ltd. All rights reserved. CR CARTWRIGHT WE, 1995, P 17 INT CART C BAC, P1116 DIBIASE D, 1990, EARTH MINERAL SCI, V59, P13 DRANSCH D, 1997, COMPUTER ANIMATION K, P145 DREHER A, 1996, MEDIENPSYCHOLOGISCHE FREITAG U, 1993, REPORT ICA WORKING G, P9 HASEBROOK J, 1995, MULTIMEDIA PSYCHOLOG, P330 HEIDMANN F, 1996, BEITRAGE KARTOGRAPHI, P133 KOSSLYN SM, 1980, IMAGES MIND, P500 KRYGIER JB, 1994, VISUALIZATION MODERN, P149 MANDL H, 1989, KNOWLEDGE ACQUISITIO NEISSER U, 1974, KOGNITIVE PSYCHOLOGI, P427 ORMELING F, 1993, P 16 INT CART C COL, P265 PAIVIO A, 1969, PSYCHOL REV, V76, P241 PAPAY G, 1973, FUNKTIONEN KARTOGRAP, V117, P234 WEIDENMANN B, 1995, INFORMATION LERNEN M, P65 WEIDENMANN B, 1995, INFORMATION LERNEN M, P107 TC 0 BP 5 EP 9 PG 5 JI Comput. Geosci. PY 2000 PD FEB VL 26 IS 1 GA 287FN PI OXFORD RP Dransch D Univ Rostock, Inst Geodesy & Geoinformat, D-18059 Rostock, Germany J9 COMPUT GEOSCI PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000085496000003 ER PT Journal AU Klock, H Buhmann, JM TI Data visualization by multidimensional scaling: a deterministic annealing approach SO PATTERN RECOGNITION LA English DT Article NR 42 SN 0031-3203 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Rhein Friedrich Wilhelms Univ, Inst Informat 3, Romerstr 164, D-53117 Bonn, Germany Rhein Friedrich Wilhelms Univ, Inst Informat 3, D-53117 Bonn, Germany DE multidimensional scaling; visualization; proximity data; Sammon mapping; maximum entropy; deterministic annealing; optimization ID OPTIMIZATION; RELAXATION; PROJECTION; ALGORITHM AB Multidimensional scaling addresses the problem how proximity data can be faithfully visualized as points in a low- dimensional Euclidean space. The quality of a data embedding is measured by a stress function which compares proximity values with Euclidean distances of the respective points. The corresponding minimization problem is non-convex and sensitive to local minima. We present a novel deterministic annealing algorithm for the frequently used objective SSTRESS and for Sammon mapping, derived in the framework of maximum entropy estimation. Experimental results demonstrate the superiority of our optimization technique compared to conventional gradient descent methods. (C) 2000 Published by Elsevier Science Ltd. All rights reserved. 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PY 2000 PD APR VL 33 IS 4 GA 285XH PI OXFORD RP Buhmann JM Rhein Friedrich Wilhelms Univ, Inst Informat 3, Romerstr 164, D-53117 Bonn, Germany J9 PATT RECOG PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000085413300010 ER PT Journal AU Meyer, RD Cook, D TI Visualization of data SO CURRENT OPINION IN BIOTECHNOLOGY LA English DT Review NR 61 SN 0958-1669 PU CURRENT BIOLOGY LTD C1 Pfizer Inc, Cent Res, Math & Stat Sci, Eastern Point Rd, Groton, CT 06340 USA Pfizer Inc, Cent Res, Math & Stat Sci, Groton, CT 06340 USA Iowa State Univ, Dept Stat, Ames, IA 50011 USA ID SYSTEM AB Data visualization has developed in several directions: theoretical; methodological; and in new application areas. Advances include the development of a grammar of graphics, deeper understanding of human perception and implications for graphical layout, and better approaches to visualizing multidimensional data and large data sets. 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Opin. Biotechnol. PY 2000 PD FEB VL 11 IS 1 GA 285TR PI LONDON RP Meyer RD Pfizer Inc, Cent Res, Math & Stat Sci, Eastern Point Rd, Groton, CT 06340 USA J9 CURR OPIN BIOTECHNOL PA 84 THEOBALDS RD, LONDON WC1X 8RR, ENGLAND UT ISI:000085404600015 ER PT Journal AU Fischer, S Crabtree, J Brunk, B Gibson, M Overton, GC TI bioWidgets: data interaction components for genomics SO BIOINFORMATICS LA English DT Article NR 29 SN 1367-4803 PU OXFORD UNIV PRESS C1 Univ Penn, Ctr Bioinformat, Philadelphia, PA 19104 USA Univ Penn, Ctr Bioinformat, Philadelphia, PA 19104 USA ID ALIGNMENT; SEARCH AB Motivation: The presentation of genomics data in a perspicuous visual format is critical for its rapid interpretation and validation. Relatively few public database developers have the resources to implement sophisticated front-end user interfaces themselves. Accordingly, these developers would benefit from a reusable toolkit of user interface and data visualization components. Results: We have designed the bioWidget toolkit as a set of JavaBean(TM) components. It includes a wide array of user interface components and defines an architecture for assembling applications. The toolkit is founded on established software engineering design patterns and principles, including componentry Model-View-Controller, factored models and schema neutrality. As a proof of concept, we have used the bioWidget toolkit to create three extendible applications: AnnotView, BlastView and AlignView. 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To achieve high sensitivity and good localization, two problems have to be overcome. First, the strong high frequency attenuation in long XLPE cables requires that the sensors be located along the cable, preferably directly at the accessories. Secondly, the detection system must be able to distinguish internal PD from other pulses. This paper describes a solution based on directional coupling sensors and a data visualization system, which displays phase- amplitude diagrams for individual PD sources which are identified by the direction of pulse propagation. It has been applied to on-site measurements, type and routine testing of Hv cable joints and stress cones. Due to the reliable discrimination between internal PD from the accessory measured and from other pulses, testing can be done in unshielded rooms even using terminations with internal PD and corona. The method works independently well on line voltage, resonance sources, oscillating voltages and 0.1 Hz. cosine-square voltage. It has been used to verify the cable accessories installed in the 6.3 km long 380 kV cable system in Berlin, Germany. 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Dielectr. Electr. Insul. PY 1999 PD DEC VL 6 IS 6 GA 276FX PI NEW YORK RP Tech Univ Berlin, D-1000 Berlin, Germany J9 IEEE TRANS DIELECT ELECTR IN PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000084865900007 ER PT Journal AU Denoeux, T Masson, M TI Multidimensional scaling of interval-valued dissimilarity data SO PATTERN RECOGNITION LETTERS LA English DT Article NR 10 SN 0167-8655 PU ELSEVIER SCIENCE BV C1 Univ Technol Compiegne, UMR CNRS 6599 Heudiasyc, BP 20529, F- 60205 Compiegne, France Univ Technol Compiegne, UMR CNRS 6599 Heudiasyc, F-60205 Compiegne, France DE multidimensional scaling; interval-valued data; exploratory data analysis; data visualization AB Multidimensional scaling is a well-known technique for representing measurements of dissimilarity among objects as points in a p-dimensional space. In this paper, this method is extended to the case where dissimilarities are only known to lie within certain intervals. Each object is then no longer represented as point but as a legion of R-P, in such a way that the minimum and maximum distances between two regions approximate the lower and upper bounds of the dissimilarity interval between the two objects. Experiments with real data demonstrate the ability of this method to represent both the structure and the precision of dissimilarity measurements. (C) 2000 Elsevier Science B.V. All rights reserved. CR BORG I, 1997, MODERN MULTIDIMENSIO CAZES P, 1997, REV STAT APPL, V14, P5 COX TF, 1994, MULTIDIMENSIONAL SCA DETERDING DH, 1989, THESIS U CAMBRIDGE GOWDA KC, 1992, IEEE T SYST MAN CYB, V22, P368 ICHINO M, 1994, IEEE T SYST MAN CYB, V24, P698 KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P115 MURPHY PM, 1994, UCI REPOSITORY MACHI NAGABHUSHAN P, 1995, PATTERN RECOGN LETT, V16, P219 RIPLEY BD, 1996, PATTERN RECOGNITION TC 1 BP 83 EP 92 PG 10 JI Pattern Recognit. Lett. PY 2000 PD JAN VL 21 IS 1 GA 273UM PI AMSTERDAM RP Denoeux T Univ Technol Compiegne, UMR CNRS 6599 Heudiasyc, BP 20529, F-60205 Compiegne, France J9 PATTERN RECOGNITION LETT PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000084726100008 ER PT Journal AU Falissard, B TI Focused principal component analysis: Looking at a correlation matrix with a particular interest in a given variable SO JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS LA English DT Article NR 6 SN 1061-8600 PU AMER STATISTICAL ASSOC C1 Fac Med Paris Sud, INSERM, U472, 16 Ave Paul Vaillant Couturier, F-94807 Villejuif, France Fac Med Paris Sud, INSERM, U472, F-94807 Villejuif, France DE data visualization; exploratory data analysis; multivariate analysis; multidimensional graphical displays AB This article proposes a simple graphical display that is similar to principal component displays in that approximations to correlation structures are visualized. The proposed method differs from principal components in its focus on a particular variable and the exact representation of the correlations between this variable and all others. The method is therefore applicable to predictor-response data. CR ALLISON T, 1976, SCIENCE, V194, P732 GELADI P, 1986, ANAL CHIM ACTA, V185, P1 HAMILTON M, 1960, J NEUROL NEUROSUR PS, V23, P56 MALLET L, 1998, PSYCHIAT RES-NEUROIM, V82, P83 MARTINOT JL, 1990, AM J PSYCHIAT, V147, P1313 SABATIER R, 1987, STAT ANAL DONNEES, V12, P75 TC 0 BP 906 EP 912 PG 7 JI J. Comput. Graph. Stat. PY 1999 PD DEC VL 8 IS 4 GA 270ZA PI ALEXANDRIA RP Falissard B Fac Med Paris Sud, INSERM, U472, 16 Ave Paul Vaillant Couturier, F-94807 Villejuif, France J9 J COMPUT GRAPH STAT PA 1429 DUKE ST, ALEXANDRIA, VA 22314 USA UT ISI:000084566000014 ER PT Journal AU Spence, I Kutlesa, N Rose, DL TI Using color to code quantity in spatial displays SO JOURNAL OF EXPERIMENTAL PSYCHOLOGY-APPLIED LA English DT Article NR 50 SN 1076-898X PU AMER PSYCHOLOGICAL ASSOC C1 Univ Toronto, Dept Psychol, Toronto, ON M5S 3G3, Canada Univ Toronto, Dept Psychol, Toronto, ON M5S 3G3, Canada Univ Calgary, Dept Psychol, Calgary, AB T2N 1N4, Canada Univ Illinois, Dept Psychol, Urbana, IL 61801 USA ID MAPS; PRINCIPLES AB Participants made simple and complex judgments in 2 experiments that examined the use of color to code quantity in spatial displays. The coding assignments were chosen to evaluate the principle of perceptual linearity in color space. In Experiment 1, participants compared all possible pairs of colors used to represent magnitudes. Comparisons were made most rapidly with a scale that varied only brightness (B) and most accurately with a scale that covaried hue (H) with saturation (S) and brightness (H+S+B scale). In Experiment 2, clusters were identified fastest with the H+S+B scale, followed by brightness and bipolar scales, whereas a nonlinear, hue-only scale was slowest and produced the least accurate judgments. Coding assignments close to perceptual linearity were best for both simple and complex judgments in data visualization. However, hue conferred an advantage if the task involved segregation or classification. CR 1986, PSEUDOISOCHROMATIC P *MIN TRAV PUBL, 1884, ALB STAT GRAPH 1883 ALFANO B, 1992, J COMPUT ASSIST TOMO, V16, P634 ANTES JR, 1990, CARTOGR GEOGR INFORM, V17, P271 BENBASAT I, 1986, HUMAN COMPUTER INTER, V2, P65 BERLIN B, 1969, BAISC COLOR TERMS TH BERTIN J, 1983, SEMIOLOGY GRAPHICS D BOYNTON RM, 1989, P HUM VIS VIS PROC D, V1077, P322 BREWER CA, 1996, CARTOGR J, V33, P79 BREWER CA, 1997, CARTOGRAPHY GEOGRAPH, V24, P203 BREWER CA, 1994, VISUALIZATION MODERN BROWN HK, 1992, MAGN RESON IMAGING, V10, P143 CARSWELL CM, 1995, NCHS WORKING PAPER S, V18, P201 CHRIST RE, 1983, HUM FACTORS, V25, P71 CLEVELAND WS, 1985, ELEMENTS GRAPHING DA CUFF DJ, 1974, CANADIAN CARTOGRAPHE, V11, P54 CUFF DJ, 1972, CANADIAN CARTOGRAPHE, V9, P134 CUFF DJ, 1973, CARTOGR J, V10, P17 EGETH H, 1969, PERCEPT PSYCHOPHYS, V5, P341 FAIRCHILD MD, 1998, COLOR APPEARANCE MOD FUNKHOUSER HG, 1937, OSIRIS, V3, P269 GREMILLION LL, 1981, P 2 INT C INF SYST C, P121 HASTIE R, 1995, NCHS WORKING PAPER S, V18, P149 HERRMANN D, 1994, NATL CTR HLTH STAT W, V11, P1 HOADLEY ED, 1990, COMMUN ACM, V33, P120 INDOW T, 1988, PSYCHOL REV, V95, P456 LAMBERSKI RJ, 1983, ECTJ-EDUC COMMUN TEC, V31, P9 LEVKOWITZ H, 1992, IEEE COMPUT GRAPH, V12, P72 LEWANDOWSKY S, 1993, APPL COGNITIVE PSYCH, V7, P533 LEWANDOWSKY S, 1989, J AM STAT ASSOC, V84, P682 LEWANDOWSKY S, 1995, NCHS WORKING PAPER S, V18, P89 MCCARTY HH, 1961, IOWA STUDIES GEOGRAP, V3 MERSEY JE, 1990, CARTOGRAPHICA, V27, P1 MERSEY JE, 1980, THESIS U WISCONSIN M MERWIN DH, 1993, P HUM FACT ERG SOC 3, P1330 MILLER JW, 1974, J GEOGR, V73, P41 MONMONIER MS, 1993, MAPPING IT OUT EXPOS MUNSELL AH, 1976, MUNSELL BOOK COLOR PETCHENIK BB, 1983, GRAPHIC COMMUNICATIO, P37 PICKLE LW, 1995, NCHS WORKING PAPER S, V18, P183 PLAYFAIR W, 1786, COMMERCIAL POLITICAL ROBERTSON PK, 1988, IEEE COMPUT GRAPH, V8, P50 ROBINSON A, 1952, LOOK MAPS ROBINSON AH, 1967, INT YB CARTOGRAPHY, V7, P50 STURGES J, 1995, COLOR RES APPL, V20, P364 TRAVIS D, 1991, EFFECTIVE COLOR DISP VOGEL D, 1986, MISRC WORKING PAPER, V8611 WARE C, 1988, IEEE COMPUT GRAPH, V8, P41 WOLFRAM S, 1991, MATH SYSTEM DOING MA WYSZECKI G, 1982, COLOR SCI CONCEPTS M TC 1 BP 393 EP 412 PG 20 JI J. Exp. Psychol.-Appl. PY 1999 PD DEC VL 5 IS 4 GA 264VX PI WASHINGTON RP Spence I Univ Toronto, Dept Psychol, Toronto, ON M5S 3G3, Canada J9 J EXP PSYCHOL-APPLIED PA 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA UT ISI:000084204600004 ER PT Journal AU Wang, WC Wu, EH Max, N TI A selective rendering method for data visualization SO JOURNAL OF VISUALIZATION AND COMPUTER ANIMATION LA English DT Article NR 27 SN 1049-8907 PU JOHN WILEY & SONS LTD C1 Acad Sinica, Inst Software, Comp Sci Lab, POB 8718, Beijing 100080, Peoples R China Acad Sinica, Inst Software, Comp Sci Lab, Beijing 100080, Peoples R China DE refinement; volume rendering; scientific visualization; interaction ID RAY-CASTING-ALGORITHM; OBJECTS AB Selective visualization is a solution for visualizing data of large size and dimensionality. In this paper a neu, method is proposed for effectively rendering certain chosen parts among the fall set of data in terms of a colour buffer, referred to as the virtual plane, for storing intermediate results. By this method, scientists may concentrate their attention on the contents of data in which they are interested. Besides, the method could be easily integrated with all the current divert volume rendering techniques, especially progressive refinement methods and selective methods. Copyright (C) 1999 John Wiley & Sons, Ltd. CR FRUHAUF M, 1991, COMPUT GRAPH, V15, P101 HALEY MB, 1996, P EUROGRAPHICS 96, P45 IHM I, 1995, P VISUALIZATION 95, P69 KAJIYA JT, 1984, COMPUT GRAPHICS, V18, P165 KE HR, 1993, COMPUT GRAPH, V17, P277 LACROUTE P, 1994, ANN C SERIES ACM SIG, P451 LAUE D, 1991, COMPUTER GRAPHICS, V25, P285 LORENSEN WE, 1987, COMPUT GRAPHICS, V21, P4 MAX N, 1990, COMPUT GRAPH, V24, P27 PORTER T, 1984, COMPUT GRAPHICS, V18, P253 REYNOLDS RA, 1987, COMPUT VISION GRAPH, V38, P275 SABELLA P, 1988, COMPUT GRAPH, V22, P51 SAKAS G, 1990, P EUROGRAPHICS 90, P519 SHIRLEY P, 1990, COMPUT GRAPHICS, V24, P63 SILVA CT, 1997, IEEE T VIS COMPUT GR, V3, P142 SILVER D, 1995, IEEE COMPUT GRAPH, V15, P54 SOBIERAJSKI L, 1993, VISUAL COMPUT, V10, P116 UDUPA JK, 1993, IEEE COMPUT GRAPH, V13, P58 UDUPA JK, 1991, IEEE COMPUT GRAPH, V11, P53 WALSUM TV, 1994, COMPUT GRAPH FORUM, V13, P339 WANG FI, 1989, J NEUROIMMUNOL, V21, P3 WANG W, 1994, 5 EUR WORKSH VISC RO WESTOVER L, 1990, COMP GRAPH, V24, P367 WILHELMS J, 1992, ACM T GRAPHIC, V11, P201 WILHELMS J, 1991, COMPUT GRAPHICS, V25, P275 WILHELMS J, 1990, COMPUT GRAPHICS, V24, P57 YAGEL R, 1992, P EUROGRAPHICS 92, P153 TC 0 BP 123 EP 131 PG 9 JI J. Vis. Comput. Animat. PY 1999 PD JUL-SEP VL 10 IS 3 GA 261WQ PI W SUSSEX RP Wang WC Acad Sinica, Inst Software, Comp Sci Lab, POB 8718, Beijing 100080, Peoples R China J9 J VISUAL COMPUT ANIMAT PA BAFFINS LANE CHICHESTER, W SUSSEX PO19 1UD, ENGLAND UT ISI:000084033800002 ER PT Book in series AU Bobrowski, L Sowinski, T TI Ranked rules and data visualization SO PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY LA English DT Article NR 12 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Tech Univ Bialystok, Inst Comp Sci, Bialystok, Poland Tech Univ Bialystok, Inst Comp Sci, Bialystok, Poland PAS, Inst Biocybernet & Biomed Engn, Warsaw, Poland DE extraction of rules from data; ranked decision rules; visualization of multivariate data; diagnostic maps AB The design of non-linear visualizing transformations of data sets that allow for a good separation of classes (categories) on the diagnostic maps is considered. The proposed transformations are based on a model of the ranked family of decision rules. Such family of rules could be generated by separate and conquer algorithms. CR BISHOP CM, 1995, NEURAL NETWORKS PATT BOBROWSKI L, 1992, BIOCYBERNETICS BIOME, V12, P61 BOBROWSKI L, 1998, IN PRESS BIOCYBERNET BOBROWSKI L, 1996, P ICPR96 AUSTR, P224 BOBROWSKI L, 1991, PATTERN RECOGN, V24, P863 BOBROWSKI L, 1992, PRACE IBIB, P31 DUDA OR, 1973, PATTERN CLASSIFICATI FAYYAD UM, 1996, ADV KNOWLEDGE DISCOV FUHRKRANZ J, OEFAJTR9625 AUSTR RE MIZARD M, 1989, J PHYS A, V22, P2191 RIPLEY BD, 1996, PATTERN RECONGITION WASYLUK H, MEDINFO 95 P TC 0 BP 47 EP 55 PG 9 SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE PY 1998 VL 1510 GA BN94W PI BERLIN RP Bobrowski L Tech Univ Bialystok, Inst Comp Sci, Bialystok, Poland J9 LECT NOTE ARTIF INTELL PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000083636500006 ER PT Book in series AU Gibert, K Aluja, T Cortes, U TI Knowledge discovery with clustering based on rules. Interpreting results SO PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY LA English DT Article NR 9 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Univ Politecn Catalunya, Dept Stat & Operat Res, Pau Gargallo 5, E-08028 Barcelona, Spain Univ Politecn Catalunya, Dept Stat & Operat Res, E-08028 Barcelona, Spain Univ Politecn Catalunya, Dept Software, E-08028 Barcelona, Spain DE combining many methods in one system; statistical tests in KDD applications; medicine : diagnosis and prognosis; from concept learning to concept discovery; prior domain knowledge and use of discovered; knowledge AB It is clear that nowadays analysis of complex systems is an important handicap in Statistics, Artificial Intelligence, Information Systems, Data visualization, and other fields. Describing the structure or obtaining knowledge of complex systems is known as a difficult task. The combination of Data Analysis techniques (including clustering),Inductive Learning (knowledge-based systems), Management of Data Bases and Multidimensional Graphical Representation must produce benefits on this field. Clustering based on rules ( CBR) is a methodology developed with the aim of finding the structure of complex domains, which performs better than traditional clustering algorithms or knowledge based systems approaches. In our proposal, a combination of clustering and inductive learning is focussed to the problem of finding and interpreting special patterns (or concepts) from large data bases, in order to extract useful knowledge to represent real-world domains. This methodology and its behaviour as a Knowledge Discovery has been, in fact, presented in previous papers ([3], [5], [2]...). The aim of this paper is to emphasize the reporting phase. Some tools oriented to the interpretation of the dusters are presented; automatic rules generation is presented and applied to a real research. Actually, in a KD system, data preparation and interpretation of the results is as important as the analysis itself. In this paper, missing data treatment is analysed; a statistical test, based on non parametric techniques, for comparing several classifications is presented. Also, a method for finding characteristic values of the classes is presented; this is based on the prototype of each class. Finally, these characterizations allow automatic generation of decision rules, as a predictive tool for future items. CR FAYYAD U, 1996, ADV KD DM GIBERT K, 1998, IN PRESS COMPUTACION GIBERT K, 1994, LNS, V89, P351 GIBERT K, 1997, MATHWARE, V10 GIBERT K, P APPL STOCH MOD DAT, P181 GOWER JC, BIOMETRICS, P257 LEBART L, TRAITEMENT STAT DONN NAKHAEIZADEH G, IFCS 96, P17 SONICKI Z, LIJECNICKI VJESNIK, V115, P306 TC 1 BP 83 EP 92 PG 10 SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE PY 1998 VL 1510 GA BN94W PI BERLIN RP Gibert K Univ Politecn Catalunya, Dept Stat & Operat Res, Pau Gargallo 5, E-08028 Barcelona, Spain J9 LECT NOTE ARTIF INTELL PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000083636500010 ER PT Book in series AU Hamilton, HJ Hilderman, RJ Li, LC Randall, DJ TI Generalization lattices SO PRINCIPLES OF DATA MINING AND KNOWLEDGE DISCOVERY LA English DT Article NR 12 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada AB Generalization lattices encode domain knowledge relevant to generalization. They provide a convenient framework for data visualization during user-guided exploration and for automated guidance during independent exploration. To reduce the size of a generalization lattice for an individual attribute, we define six types of pruning. Then we consider the generalization space defined by the cross product of lattices for several attributes. To increase the relevance of the data exploration results, we define five additional types of pruning, hn interactive, web-based system for visualizing the generalization space allows the user to interactively guide the data exploration process. CR BOURNAUD I, 1997, P 3 INT C CONC STRUC, P446 CAI Y, 1991, KNOWLEDGE DISCOVERY, P213 CHAUDHURI S, 1997, OLAP DATA WAREHOUSIN FELDMAN R, 1995, P 1 INT C KNOWL DISC, P112 GODIN R, 1995, COMPUT INTELL, V11, P246 HAMILTON HJ, 1996, PROC INT C TOOLS ART, P246 HARINARAYAN V, 1996, P ACM SIGMOD INT C M, P205 HILDERMAN RJ, 1997, P 1 EUR C PRINC DAT, P25 MINEAU GW, 1995, IEEE T KNOWL DATA EN, V7, P824 PANG W, 1996, P 9 ANN FLOR AI RES, P390 RANDALL DJ, 1998, 5 INT WORKSH TEMP RE, P177 SOWA JF, 1984, CONCEPTUAL STRUCTURE TC 0 BP 328 EP 336 PG 9 SE LECTURE NOTES IN ARTIFICIAL INTELLIGENCE PY 1998 VL 1510 GA BN94W PI BERLIN RP Hamilton HJ Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada J9 LECT NOTE ARTIF INTELL PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000083636500037 ER PT Journal AU Vellido, A Lisboa, PJG Meehan, K TI Segmentation of the on-line shopping market using neural networks SO EXPERT SYSTEMS WITH APPLICATIONS LA English DT Article NR 45 SN 0957-4174 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Liverpool John Moores Univ, Sch Comp & Math Sci, Byrom St, Liverpool L3 3AF, Merseyside, England Liverpool John Moores Univ, Sch Comp & Math Sci, Liverpool L3 3AF, Merseyside, England Liverpool John Moores Univ, Sch Business, Liverpool L3 5UZ, Merseyside, England DE neural networks; self-organizing map; market segmentation; electronic commerce; on-line shopping ID ORGANIZING FEATURE MAP; PERFORMANCE AB The characterization and analysis of on-line customers' needs and expectations, regarding the Internet as a new marketing channel, is considered a prerequisite to the realization of the expected growth of the consumer-oriented electronic commerce market. The aim of the present study is twofold: to carry out an exploratory segmentation of this market that can throw some light upon its structure, and to characterize the on-line shopping adoption process. The Self-Organizing Map (SOM), an unsupervised neural network model devised by Kohonen (Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59-69; Kohonen, T., (1995). Self-organizing maps. Berlin: Springer) will be used as part of a tandem approach to segmentation, which involves the factor analysis of the observable variables in the data to be analyzed, prior to clustering. The SOM is shown to be a powerful data visualization tool, able to assist the data analysis, providing supervised methods with useful explanatory capabilities. It is also applied, in a completely unsupervised mode, to discover the clusters or segments that naturally occur in the data. The SOM is proposed as a flexible clustering model able to accommodate both Finer Segmentation and Normative Segmentation approaches. Within the latter, a cluster-partition is proposed and analysed, and high-level customer profiles, of potential interest to on-line marketers, are derived and described in marketing terms. (C) 1999 Elsevier Science Ltd. All rights reserved. CR ARABIE P, 1994, ADV METHODS MARKETIN, P160 BACK B, 1997, PAP PUU-PAP TIM, V79, P42 BALAKRISHNAN PV, 1996, EUR J OPER RES, V93, P346 BISHOP CM, 1998, NEURAL COMPUT, V10, P215 BISHOP CM, 1995, NEURAL NETWORKS PATT BRANNBACK M, 1997, EUROPEAN MANAGEMENT, V15, P698 BROCKETT PL, 1998, J RISK INSUR, V65, P245 CHANG S, 1998, J SEGMENTATION MARKE, V2, P19 CHEN SK, 1995, OMEGA-INT J MANAGE S, V23, P235 DASGUPTA CG, 1994, INT J FORECASTING, V10, P235 DAVIES F, 1996, MARKETING INTELLIGEN, V14, P26 DEBODT E, 1998, J COMPUTATIONAL INTE, V6, P5 FIRAT AF, 1997, EUR J MARKETING, V31, P183 FISH KE, 1995, IND MARKET MANAG, V24, P431 GORDON ME, 1997, INT MARKET REV, V14, P362 GREEN PE, 1995, J MARKET RES SOC, V37, P221 GROVER R, 1989, J MARKETING RES, V26, P230 HA SH, 1998, EXPERT SYST APPL, V15, P1 HOFFMAN DL, 1999, INFORMATION SOC, V15 KARA A, 1997, EUR J MARKETING, V31, P873 KEHOE C, 1998, 9 GVUS WWW USER SURV KIVILUOTO K, 1998, NEUROCOMPUTING, V21, P203 KIVILUOTO K, 1998, P INT JOINT C NEUR N, P2268 KIVILUOTO K, 1998, P INT JONT C NEUR NE, P189 KOHONEN T, 1982, BIOL CYBERN, V43, P59 KOHONEN T, 1995, SELF ORG MAPS KRAAIJVELD MA, 1995, IEEE T NEURAL NETWOR, V6, P548 LEWIS OM, 1997, NEURAL COMPUT APPL, V5, P224 MACKAY DJC, 1995, NETWORK-COMP NEURAL, V6, P469 MARTINDELBRIO B, 1993, NEURAL COMPUT APPL, V1, P193 MAZANEC JA, 1992, J TRAVEL TOURISM MAR, V1, P39 MCDONALD WJ, 1996, ENHANCING KNOWLEDGE, P338 MURTAGH F, 1995, PATTERN RECOGN LETT, V16, P399 OBRIEN S, 1988, J MARKET RES SOC, V30, P289 OCONNOR GC, 1997, DECIS SUPPORT SYST, V21, P171 PEPERMANS R, 1996, J ECON PSYCHOL, V17, P731 PETERSOHN H, 1998, INT J UNCERTAIN FUZZ, V6, P139 RIPLEY B, 1996, PATTERN RECOGNITION SCHAFFER CM, 1998, J MARKET RES SOC, V40, P155 SERRANOCINCA C, 1996, DECIS SUPPORT SYST, V17, P227 SETIONO R, 1998, INFORM MANAGE, V34, P91 ULTSCH A, 1993, INFORMATION CLASSIFI, P307 VELLIDO A, 1999, EXPERT SYST APPL, V17, P51 WEINSTEIN A, 1987, MARKET SEGMENTATION ZHANG X, 1993, P INT JOINT C NEUR N, P2448 TC 3 BP 303 EP 314 PG 12 JI Expert Syst. Appl. PY 1999 PD NOV VL 17 IS 4 GA 256WZ PI OXFORD RP Vellido A Liverpool John Moores Univ, Sch Comp & Math Sci, Byrom St, Liverpool L3 3AF, Merseyside, England J9 EXPERT SYST APPL PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000083750600006 ER PT Journal AU Kolchanov, NA Ponomarenko, MP Frolov, AS Ananko, EA Kolpakov, FA Ignatieva, EV Podkolodnaya, OA Goryachkovskaya, TN Stepanenko, IL Merkulova, TI Babenko, VV Ponomarenko, YV Kochetov, AV Podkolodny, NL Vorobiev, DV Lavryushev, SV Grigorovich, DA Kondrakhin, YV Milanesi, L Wingender, E Solovyev, V Overton, GC TI Integrated databases and computer systems for studying eukaryotic gene expression SO BIOINFORMATICS LA English DT Article NR 40 SN 1367-4803 PU OXFORD UNIV PRESS C1 Russian Acad Sci, Inst Cytol & Genet, Siberian Branch, Prosp Lavrentieva 10, Novosibirsk 630090, Russia Russian Acad Sci, Inst Cytol & Genet, Siberian Branch, Novosibirsk 630090, Russia Inst Computat Math & Math Geophys, Novosibirsk 630090, Russia CNR, Ist Tecnol Biomed Avanzate, I-20131 Milan, Italy Gesell Biotechnol Forsch GmbH, D-3300 Braunschweig, Germany Sanger Ctr, Cambridge, England Univ Penn, Philadelphia, PA 19104 USA ID MESSENGER-RNAS; DNA; ELEMENTS; PROTEIN; PHOSPHORYLATION; INITIATION; SEQUENCES; RECEPTOR; BINDING; SIGNALS AB Motivation: The goal of the work was to develop a WWW-oriented computer system providing a maximal integration of informational and software resources on the regulation of gene expression and navigation through them. Rapid grow th of the variety and volume of information accumulated in the databases on regulation of gene expression necessarily requires the development of computer systems for automated discovery of the knowledge that can be further used for analysis of regulatory genomic sequences. Results: The GeneExpress system developed includes the following major informational and software modules: (1) Transcription Regulation (TRRD) module, which contains the databases on transcription regulatory regions of eukaryotic genes and TRRD Viewer for data visualization; (2) Site Activity Prediction (ACTIVITY), the module for analysis of functional site activity and its prediction; (3) Site Recognition module, which comprises (a) B-DNA-VIDEO system for detecting the conformational and physicochemical properties of DNA sires significant for their recognition (b) Consensus and Weight Matrices (ConsFrec) and (c) Transcription Factor Binding Sires Recognition (TFBSR) systems for detecting conservative contextual regions of functional sites and their recognition; (4) Gene Networks (GeneNet), which contains an object-oriented database accumulating the data on gene networks and signal transduction pathways, and the Java-based Viewer for exploration and visualization of the GeneNet information; (5) mRNA Translation (Leader mRNA), designed to analyze structural and contextual properties of mRNA 5'-untranslated regions (5'- UTRs) and predict their translation efficiency; (6) other program modules designed to study the structure-function organization of regulatory genomic sequences and regulatory proteins. Availability: GeneExpress is available at http://wwwmgs. bionet.nsc.ru/systems/GeneExpress/ and the links to the mirror sire(s) can be found at http://wwwmgs.bionet.nsc.ru/mgs/links/mirrors.html Contact: kol@bionet.nsc.ru. 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The presence of the blades in the inviscid axisymmetric flow is modelled in the classical way through a distributed blade force to produce the desired turning, a blockage factor that accounts for the reduced area due to blade thickness, and a distributed frictional force representing the entropy increase due to viscous stresses and heat conduction. The exact blade geometry is not required. All features of the three-dimensional code concerning the physical fluid model, boundary conditions, spatial and time discretization, convergence acceleration techniques and data visualization are available to the throughflow module. This includes the capability to treat the entire range of relevant Mach numbers, from strictly incompressible (through a preconditioning technique) to supersonic, as well as any number of blade rows in any configuration, including, for example, bypass engines. Selected elements comprising the throughflow model are discussed, with special emphasis on the blade force and its discretization. The properties of analysis and design mode with respect to shocks and the associated losses are investigated. The methodology is demonstrated on a transonic compressor rotor and a four-stage low-speed turbine. 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Inst. Mech. Eng. Part A-J. Power Energy PY 1999 VL 213 IS A4 GA 240GH PI BURY ST EDMUNDS RP Sturmayr A Free Univ Brussels, Dept Fluid Mech, Pleinlaan 2, B-1050 Brussels, Belgium J9 PROC INST MECH ENG A-J POWER PA NORTHGATE AVENUE,, BURY ST EDMUNDS IP32 6BW, SUFFOLK, ENGLAND UT ISI:000082817200005 ER PT Journal AU Assa, J Cohen-Or, D Milo, T TI RMAP: a system for visualizing data in multidimensional relevance space SO VISUAL COMPUTER LA English DT Article NR 40 SN 0178-2789 PU SPRINGER VERLAG C1 Tel Aviv Univ, Sch Math Sci, Dept Comp Sci, IL-69978 Tel Aviv, Israel Tel Aviv Univ, Sch Math Sci, Dept Comp Sci, IL-69978 Tel Aviv, Israel DE user information; data visualization; relevance map; World Wide Web; search engines AB We describe a prototype system, RMAP, for visualizing information distribution in a multidimensional relevance space. The information displayed consists of many objects, a set of features likely to interest the user, and some function that measures the relevance level of every object to the various features. The goal is to provide the user with a comprehensible visualization of that information, where the exact relevance measures of the objects are not significant. We flatten the multidimensionality of the feature space into a 2D relevance map, capturing the inter-relations among the features. The prototype, extract information from the World Wide Web from query engines, automatically categorizes and clusters the information and allow the user to visualize. 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PY 1999 VL 15 IS 5 GA 233LK PI NEW YORK RP Assa J Tel Aviv Univ, Sch Math Sci, Dept Comp Sci, IL-69978 Tel Aviv, Israel J9 VISUAL COMPUT PA 175 FIFTH AVE, NEW YORK, NY 10010 USA UT ISI:000082428200001 ER PT Journal AU Nakano, A Kalia, RK Vashishta, P TI Scalable molecular-dynamics, visualization, and data-management algorithms for materials simulations SO COMPUTING IN SCIENCE & ENGINEERING LA English DT Article NR 34 SN 1521-9615 PU IEEE COMPUTER SOC C1 Louisiana State Univ, Dept Comp Sci, Coates Hall, Baton Rouge, LA 70803 USA Louisiana State Univ, Dept Comp Sci, Baton Rouge, LA 70803 USA Louisiana State Univ, Concurrent Comp Lab Mat Simulat, Dept Phys & Astron, Baton Rouge, LA 70803 USA ID PARALLEL COMPUTERS; INTERFACES; FRACTURE; SILICON; SYSTEMS; SOLIDS; CRACK; FILMS AB Highly efficient algorithms for massively parallel computers, interactive virtual environments for analyzing and steering simulations in real time, and data compression and mining schemes for input/output and knowledge discovery have led to rapid progress in large-scale molecular-dynamics simulations involving millions of atoms. Consequently, dynamic fracture of materials with realistic microstructures can now be modeled atom-by-atom. 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PY 1999 PD SEP-OCT VL 1 IS 5 GA 231WR PI LOS ALAMITOS RP Nakano A Louisiana State Univ, Dept Comp Sci, Coates Hall, Baton Rouge, LA 70803 USA J9 COMPUT SCI ENG PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000082334800012 ER PT Journal AU Hans, P Grant, AJ Laitt, RD Ramsden, RT Kassner, A Jackson, A TI Comparison of three-dimensional visualization techniques for depicting the scala vestibuli and scala tympani of the cochlea by using high-resolution MR imaging SO AMERICAN JOURNAL OF NEURORADIOLOGY LA English DT Article NR 27 SN 0195-6108 PU AMER SOC NEURORADIOLOGY C1 Stopford Med Sch, Dept Diagnost Radiol, Oxford Rd, Manchester M13 9PT, Lancs, England Stopford Med Sch, Dept Diagnost Radiol, Manchester M13 9PT, Lancs, England Univ Manchester, Manchester Visualisat Ctr, Manchester Comp, Manchester, Lancs, England Cent Manchester Healthcare Trust, Dept Neuroradiol, Manchester, Lancs, England Cent Manchester Healthcare Trust, Dept Otolaryngol, Manchester, Lancs, England Philips Med Syst, London, England ID SPIN-ECHO MR; 3D CISS SEQUENCES; INNER-EAR; TRAIN-LENGTH; SURFACE-COIL; MP-RAGE; IMPLANTATION; HEARING; BRAIN AB BACKGROUND AND PURPOSE: Cochlear implantation requires introduction of a stimulating electrode array into the scala vestibuli or scala tympani, Although these structures can be separately identified on many high-resolution scans, it is often difficult to ascertain whether these channels are patent throughout their length, The aim of this study was to determine whether an optimized combination of an imaging protocol and a visualization technique allows routine 3D rendering of the scala vestibuli and scala tympani. METHODS: A submillimeter T2 fast spin-echo imaging sequence was designed to optimize the performance of 3D visualization methods. The spatial resolution was determined experimentally using primary images and 3D surface and volume renderings from eight healthy subjects, These data were used to develop the imaging sequence and to compare the quality and signal-to-noise dependency of four data visualization algorithms: maximum intensity projection, ray casting with transparent voxels, ray casting with opaque voxels, and isosurface rendering. The ability of these methods to produce 3D renderings of the scala tympani and scala vestibuli was also examined. The imaging technique was used in five patients with sensorineural deafness. RESULTS: Visualization techniques produced optimal results in combination with an isotropic volume imaging sequence. Clinicians preferred the isosurface-rendered images to other 3D visualizations. Both isosurface and ray casting displayed the scala vestibuli and scala tympani throughout their length, Abnormalities were shown in three patients, and in one of these, a focal occlusion of the scala tympani was confirmed at surgery. CONCLUSION: Three-dimensional images of the scala vestibuli and scala tympani can be routinely produced. The combination of an MR sequence optimized for use with isosurface rendering or ray-casting algorithms can produce 3D images with greater spatial resolution and anatomic detail than has been possible previously. 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PY 1999 PD AUG VL 20 IS 7 GA 232CY PI OAK BROOK RP Hans P Stopford Med Sch, Dept Diagnost Radiol, Oxford Rd, Manchester M13 9PT, Lancs, England J9 AMER J NEURORADIOL PA 2210 MIDWEST RD, OAK BROOK, IL 60521 USA UT ISI:000082349200005 ER PT Book in series AU Blackwelll, M Nikou, C DiGioia, AM Kanade, T TI An image overlay system for medical data visualization SO MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI'98 LA English DT Article NR 12 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Carnegie Mellon Univ, Ctr Med Robot & Comp Assisted Surg, Pittsburgh, PA 15213 USA Carnegie Mellon Univ, Ctr Med Robot & Comp Assisted Surg, Pittsburgh, PA 15213 USA UPMC, Shadyside Hosp, Ctr Orthopaed Res, Pittsburgh, PA USA CR BAUER W, 1995, INTERACTIVE TECHNOLO BLACKWELL M, 1995, P MED ROB COMP ASS S BUCHOLZ RD, 1997, CVRMED MRCAS 97, P459 DEERING M, 1992, COMPUTER GRAPHICS, V26 DRASCIC D, 1991, SPIE STEREOSCOPIC 2, V1457 GRIMSON WEL, 1996, T MED IMAGING ISEKI H, 1996, VSMM 96 OTOOLE RV, 1995, ST HEAL T, V18, P271 SCHMANDT C, 1983, COMPUT GRAPHICS, V17, P253 SIMON D, 1997, CVRMED MRCAS 97, P583 SIMON DA, 1995, J IMAGE GUID SURG, V1, P17 TONETTI J, 1997, CAR97 TC 0 BP 232 EP 240 PG 9 SE LECTURE NOTES IN COMPUTER SCIENCE PY 1998 VL 1496 GA BN52N PI BERLIN RP Blackwelll M Carnegie Mellon Univ, Ctr Med Robot & Comp Assisted Surg, Pittsburgh, PA 15213 USA J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000082115900025 ER PT Journal AU Lawrence, RD Almasi, GS Rushmeier, HE TI A scalable parallel algorithm for self-organizing maps with applications to sparse data mining problems SO DATA MINING AND KNOWLEDGE DISCOVERY LA English DT Article NR 24 SN 1384-5810 PU KLUWER ACADEMIC PUBL C1 IBM Corp, Thomas J Watson Res Ctr, POB 218, Yorktown Heights, NY 10598 USA IBM Corp, Thomas J Watson Res Ctr, Yorktown Heights, NY 10598 USA DE parallel processing; parallel IO; scalable data mining; clustering; Kohonen self-organizing maps; data visualization ID NEURAL NETWORKS; COMPUTERS AB We describe a scalable parallel implementation of the self organizing map (SOM) suitable for data-mining applications involving clustering or segmentation against large data sets such as those encountered in the analysis of customer spending patterns. The parallel algorithm is based on the batch SOM formulation in which the neural weights are updated at the end of each pass over the training data. The underlying serial algorithm is enhanced to take advantage of the sparseness often encountered in these data sets. Analysis of a realistic test problem shows that the batch SOM algorithm captures key features observed using the conventional on-line algorithm, with comparable convergence rates. Performance measurements on an SP2 parallel computer are given for two retail data sets and a publicly available set of census data.These results demonstrate essentially linear speedup for the parallel batch SOM algorithm, using both a memory-contained sparse formulation as well as a separate implementation in which the mining data is accessed directly from a parallel file system. We also present visualizations of the census data to illustrate the value of the clustering information obtained via the parallel SOM method. 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Knowl. Discov. PY 1999 PD JUN VL 3 IS 2 GA 227LZ PI DORDRECHT RP Lawrence RD IBM Corp, Thomas J Watson Res Ctr, POB 218, Yorktown Heights, NY 10598 USA J9 DATA MIN KNOWL DISCOV PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS UT ISI:000082083000002 ER PT Journal AU Mikelbank, BA Jackson, RW TI Equity vs. efficiency: Public capital investment in Ohio, 1988- 1992 SO PROFESSIONAL GEOGRAPHER LA English DT Article NR 33 SN 0033-0124 PU BLACKWELL PUBLISHERS C1 Ohio State Univ, Dept Geog, 1036 Derby Hall,154 N Oval Mall, Columbus, OH 43210 USA Ohio State Univ, Dept Geog, Columbus, OH 43210 USA DE infrastructure; socioeconomic welfare; data visualization; investment policy ID INFRASTRUCTURE; PRODUCTIVITY; OUTPUT; STATES AB Recent research on the role of public economy primarily on assessing its economic,and sometimes spatial economic, impacts. Access to more detailed and disaggregate data than typically used in these analyses allows us to take a fresh perspective on the often conflicting goals of interregional equity and aggregate efficiency. Using the state of Ohio as a case study, and classic definitions of equity and efficiency, we assess the correspondence between distributions of infrastructure investment and the social/economic distress they are intended to alleviate. Traditional map and statistical analysis combined with a graphical device we call the variegated distribution plot reveals that, in both rates and levels, investment is highest in areas of greatest distress. Both patterns are consistent with equity-driven investment distributions. 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PY 1999 PD MAY VL 51 IS 2 GA 216MP PI MALDEN RP Mikelbank BA Ohio State Univ, Dept Geog, 1036 Derby Hall,154 N Oval Mall, Columbus, OH 43210 USA J9 PROF GEOGR PA 350 MAIN STREET, STE 6, MALDEN, MA 02148 USA UT ISI:000081446700004 ER PT Journal AU Kahn, MG TI Clinical research databases and clinical decision making in chronic disease SO HORMONE RESEARCH LA English DT Article NR 39 SN 0301-0163 PU KARGER C1 100 Technol Dr, Broomfield, CO 80021 USA Roder Syst Inc, Broomfield, CO USA DE clinical databases; chronic diseases; data visualization ID RANDOMIZED DATABASE; SYSTEM; METHODOLOGIES; COMPLEMENT; EFFICACY; REPLACE; TRIALS; SAFETY; TIME AB Chronic diseases are the major source of morbidity, mortality, and resource utilization. Large-scale longitudinal databases are rapidly proliferating in both single- and multi- institutional settings, providing clinical data on a broad range of patients who receive 'real world' management. Although bias and changing medical management may limit the types of questions that can be addressed using the data contained in longitudinal clinical databases, many initial hypotheses can be generated from the data. Because chronic diseases persist over long periods of time, understanding the impact of temporal relationships, and of concurrent clinical events and contexts is critical to meaningful interpretation of clinical data. Adapting techniques initially developed for the physical sciences and for statistical process control can produce visual displays of clinical data that capture complex temporal and contextual information; With these tools, investigators can quickly explore vast quantities of clinical data, and discover new temporal relationships and emerging trends. 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Res. PY 1999 VL 51 SU 1 GA 215NN PI BASEL RP Kahn MG 100 Technol Dr, Broomfield, CO 80021 USA J9 HORMONE RES PA ALLSCHWILERSTRASSE 10, CH-4009 BASEL, SWITZERLAND UT ISI:000081387900010 ER PT Journal AU Chou, SY Lin, SW Yeh, CS TI Cluster identification with parallel coordinates SO PATTERN RECOGNITION LETTERS LA English DT Article NR 21 SN 0167-8655 PU ELSEVIER SCIENCE BV C1 Natl Taiwan Univ Sci & Technol, Dept Ind Management, 43 Keelung Rd,Sect 4, Taipei, Taiwan Natl Taiwan Univ Sci & Technol, Dept Ind Management, Taipei, Taiwan DE parallel coordinates; multi-dimensional data visualization; duality; clustering; WWW application ID MULTIDIMENSIONAL LINES; VISUALIZATION AB In this paper, the visualization of multi-dimensional data with the parallel coordinate representation is studied. A particular problem of interest is the identification of lines to which clusters of points are close in an n-dimensional space. By using the duality between the Cartesian and the parallel coordinate systems, a scan-line approach in the parallel coordinate system is established and implemented to aid the identification of such lines. (C) 1999 Elsevier Science B.V. All rights reserved. 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PY 1999 PD JUN VL 20 IS 6 GA 211EP PI AMSTERDAM RP Chou SY Natl Taiwan Univ Sci & Technol, Dept Ind Management, 43 Keelung Rd,Sect 4, Taipei, Taiwan J9 PATTERN RECOGNITION LETT PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000081147200002 ER PT Journal AU Berry, MW TI Massive data visualization - Guest editors' introduction SO COMPUTING IN SCIENCE & ENGINEERING LA English DT Editorial Material NR 0 SN 1521-9615 PU IEEE COMPUTER SOC C1 Univ Tennessee, Dept Comp Sci, Ayres Hall Rm 107, Knoxville, TN 37996 USA Univ Tennessee, Dept Comp Sci, Knoxville, TN 37996 USA TC 4 BP 16 EP 17 PG 2 JI Comput. Sci. Eng. PY 1999 PD JUL-AUG VL 1 IS 4 GA 211DZ PI LOS ALAMITOS RP Berry MW Univ Tennessee, Dept Comp Sci, Ayres Hall Rm 107, Knoxville, TN 37996 USA J9 COMPUT SCI ENG PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000081145800006 ER PT Journal AU Bauer, HU Herrmann, M Villmann, T TI Neural maps and topographic vector quantization SO NEURAL NETWORKS LA English DT Article NR 46 SN 0893-6080 PU PERGAMON-ELSEVIER SCIENCE LTD C1 MPI Stromungsforsch, Bunsenstr 10, D-37073 Gottingen, Germany MPI Stromungsforsch, D-37073 Gottingen, Germany Univ Leipzig, Klin Psychosomat Med, D-04107 Leipzig, Germany DE self-organization; neural maps; topology preservation; vector quantization ID ORGANIZING FEATURE MAPS; PRESERVATION; NETWORKS AB Neural maps combine the representation of data by codebook vectors, like a vector quantizer, with the property of topography, like a continuous function. While the quantization error is simple to compute and to compare between different maps, topography of a map is difficult to define and to quantify. Yet, topography of a neural map is an advantageous property, e.g. in the presence of noise in a transmission channel, in data visualization, and in numerous other applications. In this article we review some conceptual aspects of definitions of topography, and some recently proposed measures to quantify topography. We apply the measures first to neural maps trained on synthetic data sets, and check the measures for properties like reproducibility, scalability, systematic dependence of the value of the measure on the topology of the map, etc. We then test the measures on maps generated for four real-world data sets, a chaotic time series, speech data, and two sets of image data. The measures are found to do an imperfect, but an adequate job in selecting a topographically optimal output space dimension, while they consistently single out particular maps as non-topographic. (C) 1999 Elsevier Science Ltd. All rights reserved. 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PY 1999 PD JUN VL 12 IS 4-5 GA 208FL PI OXFORD RP Herrmann M MPI Stromungsforsch, Bunsenstr 10, D-37073 Gottingen, Germany J9 NEURAL NETWORKS PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000080980100009 ER PT Journal AU Fox, P TI IDL data visualization broadly upgraded SO IEEE SPECTRUM LA English DT Software Review NR 0 SN 0018-9235 PU IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC C1 Natl Ctr Atmospher Res, High Altitude Observ, Pob 3000, Boulder, CO 80307 USA Natl Ctr Atmospher Res, High Altitude Observ, Boulder, CO 80307 USA TC 0 BP 93 EP 94 PG 2 JI IEEE Spectr. PY 1999 PD JUN VL 36 IS 6 GA 201CD PI NEW YORK RP Fox P Natl Ctr Atmospher Res, High Altitude Observ, Pob 3000, Boulder, CO 80307 USA J9 IEEE SPECTRUM PA 345 E 47TH ST, NEW YORK, NY 10017-2394 USA UT ISI:000080576300017 ER PT Journal AU Sahu, R Panthaki, MJ Gerstle, WH TI An object-oriented framework for multidisciplinary, multi- physics, computational mechanics SO ENGINEERING WITH COMPUTERS LA English DT Article NR 32 SN 0177-0667 PU SPRINGER VERLAG C1 Univ New Mexico, Albuquerque High Performance Comp Ctr, 1601 Cent Ave NE, Albuquerque, NM 87131 USA Univ New Mexico, Albuquerque High Performance Comp Ctr, Albuquerque, NM 87131 USA DE analysis management; computational mechanics; computational mechanics language; geometric modeling; model management; object-oriented software framework ID DESIGN AB This paper presents the design and development of an object- oriented framework for computational mechanics. The framework has been designed to address some of the major deficiencies in existing computational mechanics software packages. The framework addresses the deficiencies of existing computational mechanics software packages by (a) having a sound design using the stare of the art in software engineering, and (b) providing model manipulation features that are common to a large set of computational mechanics problems. The framework provides features that are essential to a large set of computational mechanics problems. The domain-specific features provided by the framework are a geometry sub-system specifically designed for computational mechanics, an interpreted Computational Mechanics Language (CML), a structure for management of analysis projects, a comprehensive data model, model development, model query and analysis management. The domain independent features provided by the framework are a drawing sub-system for data visualization, a database server; a quantity sub-system, a simple GUI and an online help server It is demonstrated that the framework, can be used to develop applications that can: (a) extend or modify important parts of the framework to suit their own needs; (b) use CML for rapid prototyping and extending the functionality of the framework; (c) significantly ease the task of conducting parametric studies; (d) significantly ease the task of modeling evolutionary problems; (e) be easily interfaced with existing analysis programs; and (f) be used to carry out basic computational, mechanics research. It is hope that the the framework will substantially ease the task of ct-earing families of software applications that apply existing and upcoming theories of computational mechanics to solve bath academic and real world interdisciplinary simulation problems. 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Comput. PY 1999 VL 15 IS 1 GA 197HW PI NEW YORK RP Sahu R Univ New Mexico, Albuquerque High Performance Comp Ctr, 1601 Cent Ave NE, Albuquerque, NM 87131 USA J9 ENG COMPUT PA 175 FIFTH AVE, NEW YORK, NY 10010 USA UT ISI:000080360200008 ER PT Journal AU Cheng, G Fox, GC Lin, TH Haupt, T TI A computing framework for integrating interactive visualization in HPCC applications SO CONCURRENCY-PRACTICE AND EXPERIENCE LA English DT Article NR 31 SN 1040-3108 PU JOHN WILEY & SONS LTD C1 Syracuse Univ, NE Parallel Architectures Ctr, Syracuse, NY 13244 USA Syracuse Univ, NE Parallel Architectures Ctr, Syracuse, NY 13244 USA AB Network-based concurrent computing and interactive data visualization are two important components in industry applications of high-performance computing and communication. We propose an execution framework to build interactive remote visualization systems for real-world applications on heterogeneous parallel and distributed computers. Using a dataflow model of a commercial visualization software AVS in three case studies, we demonstrate a simple, effective, and modular approach to couple parallel simulation modules into an interactive remote visualization environment. The applications described in this paper are drawn from our industrial projects in financial modeling, computational electromagnetics and computational chemistry. Copyright (C) 1999 John Wiley & Sons, Ltd. CR 1992, P PAS WORKSH SYST SO 1993, P WORKSH C GRAND CHA *ADV VIS SYST INC, 1992, AVS 4 0 DEV GUID US *SIL GRAPH INC, 1992, IR EXPL US GUID CHENG G, 1996, CONCURRENCY-PRACT EX, V8, P667 CHENG G, 1993, P 2 AVS C AVS 93 LAK CHENG G, 1994, P 3 ANN INT AVS C AV CHENG G, 1993, P 6 SIAM C PAR PROC CHENG G, 1993, P SUP 93 PORTL OR NO COX JC, 1979, J FINANC ECON, V7, P229 FINUCANE T, 1992, UNPUB J FINANCE FLANERY R, 1995, P 4 ANN INT AVS C AV FOX GC, 1993, SCCS531 FOX GC, 1993, SCCS575 GRANT AJ, 1993, P AVS UK US GROUP C HARRINGTON RF, 1961, TIME HARMONIC ELECTR HAUPT T, 1995, SCCS744 KRASKE W, 1995, P 4 ANN INT AVS C AV KROGH MF, 1993, P 2 AVS C AVS 93 LAK LARKIN S, 1996, LECT NOTES COMPUTER, V1067 LU Y, 1993, P 6 SIAM C PAR PROC MILLS K, 1992, P 4 S FRONT MASS PAR MILLS K, 1992, P 5 AUSTR SUP C 6 7 OBERBRUNNER G, MP EXPRESS PARALLEL OBERBRUNNER G, 1992, S HIGH PERF DISTR CO, V1, P78 SEILER FJ, 1990, MOPAC MANUAL STEWART JJP, 1982, J COMPUT CHEM, P227 SZABO A, 1989, MODERN QUANTUM CHEM UPSON C, 1989, IEEE COMPUTER GR JUL VAZIRI A, 1994, P 3 ANN INT AVS C AV WOYS K, 1995, P 4 ANN INT AVS C AV TC 2 BP 71 EP 92 PG 22 JI Concurrency-Pract. Exp. PY 1999 PD FEB VL 11 IS 2 GA 197YW PI W SUSSEX RP Cheng G Syracuse Univ, NE Parallel Architectures Ctr, Syracuse, NY 13244 USA J9 CONCURRENCY-PRACT EXPER PA BAFFINS LANE CHICHESTER, W SUSSEX PO19 1UD, ENGLAND UT ISI:000080395200002 ER PT Journal AU Tiren, SA Askling, P Wanstedt, S TI Geologic site characterization for deep nuclear waste disposal in fractured rock based on 3D data visualization SO ENGINEERING GEOLOGY LA English DT Article NR 44 SN 0013-7952 PU ELSEVIER SCIENCE BV C1 GEOSIGMA AB, Box 894, S-75108 Uppsala, Sweden GEOSIGMA AB, S-75108 Uppsala, Sweden DE fractured rock systems; geological properties; nuclear waste repository; remote sensing; 3D modelling; visualization ID SWEDEN AB Three-dimensional (3D) visualization of data is an essential step in the development of descriptive hydrologic and rock- mechanical models of fractured rock systems. Data includes geological properties, rock mechanical properties and hydraulic responses. The 3D visualization approach is applied to characterizing a hypothetical site for a high-level nuclear waste repository located at 500 m depth in granitoids of the trans-Scandinavian igneous belt (1.6-1.8 Ga old). The study is based on site-specific data at the Aspo Hard Rock Laboratory (HRL) on the southeastern coast of Sweden. The data are extensive and spatially complex and are based on surface information, as well as a large number of boreholes penetrating up to 1000 m. At this site, fractures and fracture zones control both groundwater flow and mechanical stability. Structures are found within a 2x2x1 km deep model block representing the site. 3D locations of fractures and fracture zones are hypothesized based on surface and subsurface geological and geophysical information, including borehole radar. This results in a 3D geological model structure of the site. (C) 1999 Elsevier Science B.V. All rights reserved. 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Geol. PY 1999 PD APR VL 52 IS 3-4 GA 192YC PI AMSTERDAM RP Tiren SA GEOSIGMA AB, Box 894, S-75108 Uppsala, Sweden J9 ENG GEOL PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000080106500013 ER PT Journal AU Scharl, A TI Reference modeling as the missing link between academic research and industry practice SO JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH LA English DT Article NR 26 SN 0022-4456 PU NATL INST SCIENCE COMMUNICATION C1 Vienna Univ Econ & Business Adm, Dept Management Informat Syst, Augasse 2-6, A-1090 Vienna, Austria Vienna Univ Econ & Business Adm, Dept Management Informat Syst, A-1090 Vienna, Austria ID HYPERMEDIA DESIGN; HYPERTEXT AB The influence of visual representation on the decision behavior of managers is frequently underestimated. With the IS-induced transformation of commercial transactions and market allocation mechanisms people have to deal with the problem of filtering and processing information in order to make the right decisions in rather short duration. As a consequence, the visualization of data remains a crucial factor in the analysis and design process of every (Web-based) mass information system. For this reason, a transaction-oriented derivative of the extended World Wide Web Design Technique (eW3DT) is presented. Focusing on consumer-to-business transactions, the document-oriented modeling framework is intended to remove existing communication barriers within the boundaries of individual organizations as well as to support cooperative efforts between such entities to develop and maintain Web-based mass IS. The divergent background, perspective, and policies of academic research, IS departments, and management are addressed, in particular. CR BANKES S, 1992, INFORMATION SOC, V8, P1 BECKER J, 1995, WIRTSCHAFTSINF, V37, P435 BICHLER M, 1996, P 4 EUR C INF SYST, P1093 BICHLER M, 1996, P 5 WORKSH EN TECHN, P328 BIEBER M, 1998, P 30 ANN HAW INT C S, P309 GARZA JC, 1996, GENOME RES, V6, P211 GARZOTTO F, 1993, ACM T INFORM SYST, V11, P1 GARZOTTO F, 1995, COMMUN ACM, V38, P74 HALASZ F, 1994, COMMUN ACM, V37, P30 HANSEN HR, 1995, INF MANAGE, V31, P125 HANSEN HR, 1996, P 4 EUR C INF SYST E, P201 HARS A, 1994, REFERENZDATENMODELLE, P6 ISAKOWITZ T, 1995, COMMUN ACM, V38, P34 JASPER JE, DISCOURSE ANAL USER LOHSE GL, 1994, COMMUN ACM, V37, P36 MEYER JA, 1995, OBW, V45, P366 MEYER JA, 1997, WERBEFORSCH PRAXIS, V42, P10 MEYER JA, 1996, ZFBF, V48, P738 MINCH RP, 1996, J ORG COMP, V6, P295 NANARD J, 1995, COMMUN ACM, V38, P49 SCHARL A, 1998, P ANN HICSS, P476 SCHARL A, 1997, REFERENZMODELLIERUNG, P52 SCHWABE D, 1995, COMMUN ACM, V38, P45 SCHWABE D, 1996, P 7 ACM C HYP ASS CO, P116 TAKAHASHI K, 1997, P 6 INT WWW C STANF, P377 WALDEN P, 1996, P 4 EUR C INT TECHN, P1353 TC 1 BP 211 EP 220 PG 10 JI J. Sci. Ind. Res. PY 1999 PD MAR-APR VL 58 IS 3-4 GA 185UR PI NEW DELHI RP Scharl A Vienna Univ Econ & Business Adm, Dept Management Informat Syst, Augasse 2-6, A-1090 Vienna, Austria J9 J SCI IND RES INDIA PA DR K S KRISHNAN MARG, NEW DELHI 110 012, INDIA UT ISI:000079689700008 ER PT Journal AU Dzwinel, W Blasiak, J TI Method of particles in visual clustering of multi-dimensional and large data sets SO FUTURE GENERATION COMPUTER SYSTEMS LA English DT Article NR 23 SN 0167-739X PU ELSEVIER SCIENCE BV C1 AGH Univ Min & Met, Inst Comp Sci, Al Mickiewicza 30, PL-30059 Krakow, Poland AGH Univ Min & Met, Inst Comp Sci, PL-30059 Krakow, Poland DE visual clustering; multi-dimensional data sets; feature extraction; method of particles; parallel implementation ID EXPLORATORY PATTERN-ANALYSIS; MULTIDIMENSIONAL DATA; MAPPING TECHNIQUES; RECOGNITION; SEARCH; ALGORITHM AB A method dedicated for visual clustering of N-dimensional data sets is presented. It is based on the classical feature extraction technique - the Sammon's mapping. This technique empowered by a particle approach used in the Sammon's criterion minimization makes the method more reliable, general and efficient. To show its reliability, the results of tests are presented, which were made to exemplify the algorithm 'immunity' from data errors. The general character of the method is emphasized and its role in multicriterial analysis discussed. Due to inherent parallelism of the methods, which are based on the particle approach, the visual clustering technique can be implemented easily in parallel environment. It is shown that parallel realization of the mapping algorithm enables the visualization of data sets consisting of more than 10(4) multi-dimensional data points. The method was tested in the PVM, MPI and data parallel environments on an HP/Convex SPP/1600. In this paper, the authors compare the parallel algorithm performance for these three interfaces. The approach to visual clustering, presented in the paper, can be used in visualization and analysis of large multi-dimensional data sets. (C) 1999 Elsevier Science B.V. All rights reserved. CR ALADJEM M, 1991, PATTERN RECOGN, V24, P543 ALSULTAN KS, 1995, PATTERN RECOGN, V28, P1443 ANDENBERG MR, 1973, CLUSTER ANAL APPL BLASIAK J, 1998, HPCN 98 HIGH PERF CO BRODE S, 1986, COMPUT PHYS COMMUN, V42, P51 DZWINEL J, 1996, P 2 INT C APPL FUZZ DZWINEL W, 1995, ANN NUCL ENERGY, V22, P543 DZWINEL W, 1997, FUTURE GENER COMP SY, V12, P371 DZWINEL W, 1997, LECT NOTES COMPUT SC, V1225, P223 DZWINEL W, 1995, LECT NOTES COMPUT SC, V919, P508 DZWINEL W, 1994, PATTERN RECOGN, V27, P949 GOWDA KC, 1978, PATTERN RECOGN, V10, P105 ISMAIL MA, 1989, PATTERN RECOGN, V22, P75 JAIN D, 1988, ALGORITHMS CLUSTERIN KLEIN RW, 1989, PATTERN RECOGN, V22, P213 NIEMANN H, 1980, PATTERN RECOGN, V12, P83 OLSON CF, 1995, PARALLEL COMPUT, V21, P1313 PEPYOLYSHEV YN, 1991, ANN NUCL ENERGY, V18, P117 SELIM SZ, 1991, PATTERN RECOGN, V24, P1003 SIEDLECKI W, 1988, PATTERN RECOGN, V21, P411 SIEDLECKI W, 1988, PATTERN RECOGN, V21, P431 TOU J, 1974, PATTERN RECOGNITION ZHANG QW, 1991, PATTERN RECOGN, V24, P835 TC 0 BP 365 EP 379 PG 15 JI Futur. Gener. Comp. Syst. PY 1999 PD APR VL 15 IS 3 GA 185DD PI AMSTERDAM RP Dzwinel W AGH Univ Min & Met, Inst Comp Sci, Al Mickiewicza 30, PL-30059 Krakow, Poland J9 FUTURE GENER COMPUT SYST PA PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS UT ISI:000079652100007 ER PT Journal AU Nelson, L Cook, D Cruz-Neira, C TI XGobi vs the C2: Results of an experiment comparing data visualization in a 3-D immersive virtual reality environment with a 2-D workstation display SO COMPUTATIONAL STATISTICS LA English DT Article NR 17 SN 0943-4062 PU PHYSICA VERLAG GMBH C1 Iowa State Univ, Iowa Ctr Emerging Mfg Technol, Ames, IA 50011 USA Iowa State Univ, Iowa Ctr Emerging Mfg Technol, Ames, IA 50011 USA Iowa State Univ, Dept Stat, Ames, IA 50011 USA DE C2; XGobi; grand tour; linked brushing; dynamic graphics; immersive environment; exploratory data analysis; data mining; cluster analysis; dimension reduction AB Virtual environments are an emerging technology, which make use of 3-D display devices. Currently very little research into using this new technology for statistical graphics has been done. In association with the Iowa Center for Emerging Manufacturing Technology we have been building a statistical graphics application in the highly immersive C2 environment,called VR-Gobi. This paper describes an experiment conducted to determine if the new technology provides an improved exploratory data analysis environment over traditional workstation graphics environments, such as XGobi. A good analogy comparing the difference between the two environments is that one is like a desk and the other like a room. The visualization tasks we compare between the two environments are detecting clusters, dimensionality and radial sparseness in high-dimensional data, and we also compare the ease of interaction between the computer and human user in the two environments. CR ASIMOV D, 1985, SIAM J SCI STAT COMP, V6, P128 BUJA A, 1997, DYNAMIC PROJECTIONS BUJA A, 1986, P 17 S INT COMP SCI, P63 CARR DB, 1996, 129 G MAS U CTR COMP COOK D, 1997, J COMPUTATIONAL GRAP, V4, P464 COOK D, 1995, J COMPUTATIONAL GRAP, V4, P155 CRUZNEIRA C, 1993, ACM SIGGRAPH 93 P, P135 HARDLE W, 1995, XPLORE INTERACTIVE S HURLEY C, 1990, SIAM J SCI STAT COMP, V11, P1193 NEWTON CM, 1978, GRAPHICAL REPRESENTA, P59 SWAYNE DF, 1998, J COMPUT GRAPH STAT, V7, P113 SYMANZIK J, 1997, COMPUTING SCI STAT, V29, P41 TIERNEY L, 1991, LISPSTAT OBJECT ORIE VANTEYLINGEN R, 1997, IEEE T VIS COMPUT GR, V3, P65 WEGMAN EJ, 1991, 68 G MAS U CTR COMP WEGMAN EJ, 1993, HDB STAT, V9, P857 WILLS G, 1998, IN PRESS J COMPUTATI TC 3 BP 39 EP 51 PG 13 JI Comput. Stat. PY 1999 VL 14 IS 1 GA 185DT PI HEIDELBERG RP Nelson L Iowa State Univ, Iowa Ctr Emerging Mfg Technol, Ames, IA 50011 USA J9 COMPUTATION STAT PA TIERGARTENSTRASSE 17, 69121 HEIDELBERG, GERMANY UT ISI:000079653400004 ER PT Journal AU Tamayo, P Slonim, D Mesirov, J Zhu, Q Kitareewan, S Dmitrovsky, E Lander, ES Golub, TR TI Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation SO PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA LA English DT Article NR 28 SN 0027-8424 PU NATL ACAD SCIENCES C1 MIT, Ctr Genome Res, Bldg 300,1 Kendall Sq, Cambridge, MA 02139 USA Whitehead Inst Biomed Res, Cambridge Ctr 9, Cambridge, MA 02142 USA Dana Farber Canc Inst, Boston, MA 02115 USA Dartmouth Med Sch, Dept Pharmacol & Toxicol, Hanover, NH 03755 USA MIT, Dept Biol, Cambridge, MA 02139 USA ID ACUTE PROMYELOCYTIC LEUKEMIA; RETINOIC ACID; RAR-ALPHA; PROTEIN; PML; TRANSLOCATION; T(15-17); CLUSTER; ENCODES AB Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells). Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentiation- for example, highlighting certain genes and pathways involved in "differentiation therapy" used in the treatment of acute promyelocytic leukemia. CR BECK S, 1992, J MOL BIOL, V228, P433 CHO RJ, 1998, MOL CELL, V2, P65 CHU S, 1998, SCIENCE, V282, P699 DERISI JL, 1997, SCIENCE, V278, P680 DETHE H, 1991, CELL, V66, P675 EISEN MB, 1998, P NATL ACAD SCI USA, V95, P14863 GORDON AE, 1981, CLASSIFICATION METHO HARTIGAN J, 1975, CLUSTERING ALGORITHM HOHFELD J, 1997, EMBO J, V16, P6209 JIN YJ, 1992, J BIOL CHEM, V267, P10942 JOBSON J, 1992, APPL MULTIVARIATE DA KAKIZUKA A, 1991, CELL, V66, P663 KASKI S, 1997, NEURAL COMP SURV, V1, P102 KOHONEN T, 1991, P IEEE, V78, P1464 KOHONEN T, 1997, SELF ORG MAPS KOK K, 1993, P NATL ACAD SCI USA, V90, P6071 LOCKHART DJ, 1996, NAT BIOTECHNOL, V14, P1675 MANGIAMELI P, 1996, EUR J OPER RES, V93, P402 MIYATA Y, 1997, P NATL ACAD SCI USA, V94, P14500 MORGAN BJT, 1995, APPL STAT-J ROY ST C, V44, P117 NASONBURCHENAL K, 1997, DIFFERENTIATION, V61, P321 RUSSELL L, 1991, DNA CELL BIOL, V10, P581 SPELLMAN PT, 1998, MOL BIOL CELL, V9, P3273 VANOSDOL WW, 1994, J NATL CANCER I, V86, P1853 WEN XL, 1998, P NATL ACAD SCI USA, V95, P334 WODICKA L, 1997, NAT BIOTECHNOL, V15, P1359 YOSHIDA H, 1996, CANCER RES, V56, P2945 ZHENG P, 1998, NATURE, V396, P373 TC 372 BP 2907 EP 2912 PG 6 JI Proc. Natl. Acad. Sci. U. S. A. PY 1999 PD MAR 16 VL 96 IS 6 GA 177RH PI WASHINGTON RP Lander ES MIT, Ctr Genome Res, Bldg 300,1 Kendall Sq, Cambridge, MA 02139 USA J9 PROC NAT ACAD SCI USA PA 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA UT ISI:000079224500064 ER PT Journal AU Li, KC TI Nonlinear confounding in high-dimensional regression SO ANNALS OF STATISTICS LA English DT Article NR 35 SN 0090-5364 PU INST MATHEMATICAL STATISTICS C1 Univ Calif Los Angeles, Dept Math, Los Angeles, CA 90024 USA Univ Calif Los Angeles, Dept Math, Los Angeles, CA 90024 USA DE adaptiveness; dimension reduction; graphics; nonlinear regression; overlinearization; quasi-helical confounding; information matrices; regression diagnostics; semi-parametrics; sliced inverse regression ID SLICED INVERSE REGRESSION; PROJECTION PURSUIT REGRESSION; MULTIPLE-REGRESSION; DATA VISUALIZATION; REDUCTION; MODELS; LINK; PLOTS AB It is not uncommon to find nonlinear patterns in the scatterplots of regressor variables. But how such findings affect standard regression analysis remains largely unexplored. This article offers a theory on nonlinear confounding, a term for describing the situation where a certain nonlinear relationship in regressors leads to difficulties in modeling and related analysis of the data. The theory begins with a measure of nonlinearity between two regressor variables. It is then used to assess nonlinearity between any two projections from the high-dimensional regressor and a method of finding most nonlinear projections is given. Nonlinear confounding is addressed by taking a fresh new look at fundamental issues such as the validity of prediction and inference, diagnostics, regression surface approximation, model uncertainty and Fisher information loss. CR ALDRIN M, 1993, COMPUT STAT DATA AN, V16, P379 BICKEL PJ, 1992, EFFICIENT ADAPTIVE E BOX GEP, 1987, EMPIRICAL MODEL BUIL BOX GEP, 1964, J ROY STAT SOC B MET, V26, P211 BREIMAN L, 1985, J AM STAT ASSOC, V80, P580 BRILLINGER DR, 1983, FESTSCHRIFT EL LEHMA, P97 BRILLINGER DR, 1991, J AM STAT ASSOC, V86, P333 BUJA A, 1989, ANN STAT, V17, P453 CARROLL RJ, 1992, J AM STAT ASSOC, V87, P1040 CHEN H, 1991, ANN STAT, V19, P142 COOK RD, 1994, INTRO REGRESSION GRA COOK RD, 1994, J AM STAT ASSOC, V89, P177 COOK RD, 1994, J AM STAT ASSOC, V89, P592 COOK RD, 1991, J AM STAT ASSOC, V86, P328 COOK RD, 1993, TECHNOMETRICS, V35, P351 COX DR, 1981, APPL STAT PRINCIPLES DUAN N, 1991, ANN STAT, V19, P505 DUAN N, 1991, STAT SINICA, V1, P127 FRIEDMAN JH, 1981, J AM STAT ASSOC, V76, P817 GU C, 1992, J AM STAT ASSOC, V87, P1051 HALL P, 1993, ANN STAT, V21, P867 HALL P, 1989, ANN STAT, V17, P573 HARDLE W, 1993, ANN STAT, V21, P157 HARDLE W, 1989, J AM STAT ASSOC, V84, P986 HARRISON D, 1978, J ENVIRON ECON MANAG, V5, P81 HSING TL, 1992, ANN STAT, V20, P1040 LI KC, 1989, ANN STAT, V17, P1009 LI KC, 1990, DATA VISUALIZATION S LI KC, 1992, J AM STAT ASSOC, V87, P1025 LI KC, 1991, J AM STAT ASSOC, V86, P316 LI KC, 1992, PROBABILITY STAT, P138 NELDER JA, 1972, J ROYAL STATISTICA A, V135, P370 SAMAROV AM, 1993, J AM STAT ASSOC, V88, P836 TIERNEY L, 1990, LISP STAT OBJECT ORI WHITE H, 1989, J AM STAT ASSOC, V84, P1003 TC 7 BP 577 EP 612 PG 36 JI Ann. Stat. PY 1997 PD APR VL 25 IS 2 GA 176DB PI HAYWARD RP Li KC Univ Calif Los Angeles, Dept Math, Los Angeles, CA 90024 USA J9 ANN STATIST PA IMS BUSINESS OFFICE-SUITE 7, 3401 INVESTMENT BLVD, HAYWARD, CA 94545 USA UT ISI:000079134700007 ER PT Journal AU Friedrich, M Melle, M Saupe, D TI ATLAS2000 - Atlases of the future on the Internet SO COMPUTERS & GRAPHICS-UK LA English DT Article NR 5 SN 0097-8493 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Univ Freiburg, Inst Phys Geog, Hugstetter Str 55, Freiburg, Germany Univ Freiburg, Inst Phys Geog, Freiburg, Germany Univ Leipzig, Inst Comp Sci, D-7010 Leipzig, Germany AB In geography new measuring techniques and imaging modalities have led to a huge amount of distributed data requiring new digital processing techniques. Environmental monitoring programs and analysis of global change in geography, meteorology, and climatology require an interdisciplinary usage of knowledge about the ecological system earth. Until recently the traditional atlas has been the primary tool for collection and dissemination of geographical knowledge about the earth. To advance the concepts of the atlas it is necessary to work on a methodological base different From that discussed before under the slogan digital atlas. The new tools that are developed in this project will permit an interactive, individual, and problem-related representation, the combination, modeling, and the interchange of multi-dimensional spatial and temporal data sets. A number of theoretical and practical aspects will be addressed, e.g., the investigation and interpolation of the space-time-continuum, the interpretation of data using different scales, and the use of subject-specific models for representation of measured and simulated data. Within the scope of the project some goals are in the fields of the development of hierarchical methods for compression, visualization and data access, thus enabling efficient handling of distributed resources in worldwide data and computer networks. Another task of the project lies in the development of didactical concepts for digital use of scientific data and models by a wide class of users. The project is a joint venture of the Institute for Physical Geography at the University of Freiburg and the Institute of Computer Science at the University of Leipzig. (C) 1999 Published by Elsevier Science Ltd. All rights reserved. CR SPATIAL DATA TRANSFE *SUN MICR INC, 1997, JDBC GUID GETT START BENN W, 1998, INFORMATIK SPEKTRUM, V21, P1 ORMELING F, 1996, WIENER SCHRIFTEN GEO POSEGGA J, 1998, INFORMATIK SPEKTRUM, V21, P16 TC 0 BP 697 EP 701 PG 5 JI Comput. Graph.-UK PY 1998 PD DEC VL 22 IS 6 GA 167PE PI OXFORD RP Friedrich M Univ Freiburg, Inst Phys Geog, Hugstetter Str 55, Freiburg, Germany J9 COMPUT GRAPH-UK PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000078641800007 ER PT Journal AU Vega, J Sanchez, E Cremy, C Portas, A Dulya, CM Nilsson, J TI TJ-II data retrieving by means of a client/server model SO REVIEW OF SCIENTIFIC INSTRUMENTS LA English DT Article NR 4 SN 0034-6748 PU AMER INST PHYSICS C1 Asociac EURATOM CIEMAT Fus, Avda Complutense 22, Madrid 28040, Spain Asociac EURATOM CIEMAT Fus, Madrid 28040, Spain Royal Inst Technol, S-10044 Stockholm, Sweden AB The database of the TJ-II flexible heliac is centralized in a Unix server. This computer also commands the on-line processes related to data acquisition during TJ-II discharges: programming of measurement systems, connectivity with control systems, data visualization, and computations. The server has to provide access to the data so that signal analysis can be performed by local users or even from remote hosts. Data retrieving is accomplished by means of a client/server architecture in which two data servers are permanently running in the background of the Unix computer. One of them serves data requests from local clients and the other one sends data to remote clients. The communication protocol in both cases has been developed by using TCP/IP and Berkeley sockets. The client part consists of a set of routines (FORTRAN and C callable), which, in a transparent way, provide connectivity with the servers. This structure allows access to TJ-II data exactly in the same way from any computer, hiding not only specific aspects of the database, but hardware architecture of the server computer as well. In addition, the remote access makes it possible to distribute computations and to reduce the load on the Unix server from analysis and visualization tasks. At present, this software is running in four different environments: the Unix server itself, various types of Unix workstations, a CRAY J90 and a CRAY T3E. Finally, due to the fact that visualization is essential for TJ-II data analysis, a powerful and a very flexible visualization tool has been developed. It is a point and click application based on X Window/Motif. Data access is carried out through the client/server processes mentioned above and the software runs in the client computer. (C) 1999 American Institute of Physics. [S0034-6748(99)55001-6]. CR ALEJALDRE C, 1990, FUSION TECHNOL, V17, P131 DEPABLOS JL, 1996, IEEE T NUCL SCI 1, V43, P229 VEGA J, IN PRESS FUSION ENG VEGA J, 1996, REV SCI INSTRUM, V67, P4154 TC 2 BP 498 EP 501 PG 4 JI Rev. Sci. Instrum. PY 1999 PD JAN VL 70 IS 1 PN 2 GA 161FQ PI WOODBURY RP Vega J Asociac EURATOM CIEMAT Fus, Avda Complutense 22, Madrid 28040, Spain J9 REV SCI INSTR PA CIRCULATION FULFILLMENT DIV, 500 SUNNYSIDE BLVD, WOODBURY, NY 11797-2999 USA UT ISI:000078278900064 ER PT Journal AU Fischer, H Hennig, J TI Neural network-based analysis of MR time series SO MAGNETIC RESONANCE IN MEDICINE LA English DT Article NR 22 SN 0740-3194 PU JOHN WILEY & SONS INC C1 Univ Freiburg, Dept Radiol, Hugstetter Str 55, D-79106 Freiburg, Germany Univ Freiburg, Dept Radiol, D-79106 Freiburg, Germany DE time series; functional MRI; clustering; self-organizing map AB Clustering has been introduced to analyze fMRI data by means of partitioning data into time series of similar temporal behavior, It is hoped that one of these clusters represents a dynamic effect of interest, like functional activation. Using self-organizing maps for clustering, additional information can be obtained by ordering cluster centers on a two-dimensional projection plane. The map's capability of data visualization is used to summarize all dynamic effects of an experiment by means of data partitioning. The map does allow differently sized and populated clusters in the data by forming "superclusters" on the map. The method is introduced as a conceptual extension to clustering. Applications to fMRI and to MR mammography are discussed. Magn Reson Med 41:124-131, 1999. (C) 1999 Wiley- Liss, Inc. CR ANDERBERG MR, 1973, CLUSTER ANAL APPL BANDETTINI PA, 1993, MAGNET RESON MED, V30, P161 BEZDEK JC, 1981, PATTERN RECOGNITION BISHOP CM, 1996, LECT NOTES COMPUTER, V1112, P165 DING X, 1994, P SMR 2 ANN M SAN FR, P630 DING X, 1996, P SMR 4 ANN M NEW YO, P1798 DUDA RO, 1973, PATTERN CLASSIFICATI FISCHER H, 1996, P ISMRM 4 ANN M NEW, P1779 FRITZKE B, 1995, NEURAL PROCESS LETT, V2, P9 GOLAY X, 1996, P INT SOC MAGN RES M, P1787 GRAEPEL T, 1997, LECT NOTES COMPUTER, V1327, P619 HAKKINEN E, 1997, LECT NOTES COMPUTER, V1327, P601 KOHONEN T, 1996, SELF ORG MAP PROGRAM, PA31 KOHONEN T, 1995, SELF ORG MAPS MURTAGH F, 1995, PATTERN RECOGN LETT, V16, P399 PAL NR, 1993, P IJCNN, P2441 SAMMON JW, 1969, IEEE T COMPUT, V18, P401 SCARTH G, 1996, P SMR 4 ANN M NEW YO, P1764 SCARTH GB, 1995, P INT SOC MAGN RES M, P238 ULTSCH A, 1993, INFORMATION CLASSIFI, P307 WINDHAM MP, 1982, IEEE T PATTERN ANAL, V4, P357 ZHENG Y, 1996, IEEE T NEURAL NETWOR, V7, P87 TC 17 BP 124 EP 131 PG 8 JI Magn. Reson. Med. PY 1999 PD JAN VL 41 IS 1 GA 162GD PI NEW YORK RP Fischer H Univ Freiburg, Dept Radiol, Hugstetter Str 55, D-79106 Freiburg, Germany J9 MAGN RESON MED PA 605 THIRD AVE, NEW YORK, NY 10158-0012 USA UT ISI:000078336800017 ER PT Journal AU Stillerman, J Fredian, TW TI The MDSplus data acquisition system, current status and future directions SO FUSION ENGINEERING AND DESIGN LA English DT Article NR 5 SN 0920-3796 PU ELSEVIER SCIENCE SA C1 MIT, Plasma Sci & Fus Ctr, NW17-268,175 Albany St, Cambridge, MA 02139 USA MIT, Plasma Sci & Fus Ctr, Cambridge, MA 02139 USA DE data acquisition; data visualization; data management; MDSplus; Alcator C-Mod; distributed computing AB The MDSplus data acquisition system was developed in collaboration with the ZTH group at Los Alamos National Laboratory and the RFX group at CNR in Padua, Italy and is currently in use at MIT, RFX in Padua, and TCV at EPFL in Lausanne. MDSplus is based on a hierarchical experiment description which completely describes the data acquisition and analysis tasks and contains the results from these operations. It also includes a set of X/motif based tools for data acquisition and display, as well as diagnostic configuration and management. These tools were designed to operate in a distributed, client/server environment with multiple concurrent readers and writers to the data store. An interface to a relational database is provided for storage and management of processed data. A commercially available package called IDL is used as the primary data analysis and visualization tool. The current projects include a new interface to the electronic logbook, tools for remote collaborators and WWW access, and a port of the system to UNIX and Windows-NT/95. (C) 1999 Elsevier Science S.A. All rights reserved. CR FREDIAN TW, 1999, FUSION ENG DES, V43, P327 FRIEDIAN TW, 1997, REV SCI INSTRUM, V68, P935 HORNE S, 1991, P 14 S FUS ENG I EL, P242 SCHACHTER JM, 1997, PFCRR972 MIT PLASM S, P217 STILLERMAN JA, 1997, REV SCI INSTRUM 2, V68, P939 TC 4 BP 301 EP 308 PG 8 JI Fusion Eng. Des. PY 1999 PD JAN VL 43 IS 3-4 GA 161KQ PI LAUSANNE RP Stillerman J MIT, Plasma Sci & Fus Ctr, NW17-268,175 Albany St, Cambridge, MA 02139 USA J9 FUSION ENG DES PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND UT ISI:000078288200009 ER PT Journal AU Vega, J Cremy, C Sanchez, E Portas, A TI The TJ-II data acquisition system: an overview SO FUSION ENGINEERING AND DESIGN LA English DT Article NR 10 SN 0920-3796 PU ELSEVIER SCIENCE SA C1 CIEMAT, Asociac EURATOM, CIEMAT Fus, Edificio 66,Avda Complutense 22, Madrid 28040, Spain CIEMAT, Asociac EURATOM, CIEMAT Fus, Madrid 28040, Spain DE instrumentation mainframes; TJ-II diagnostics; host-centralized database; signal analog conditioning ID DIAGNOSTICS; DEVICES AB The data acquisition system for the TJ-II fusion machine has been developed to coordinate actions among the several experimental systems devoted to data capture and storage: instrumentation mainframes (VXI, VME, CAMAC). control systems of diagnostics and a host-centralized database. Connectivity between these elements is achieved through local area networks, which ensure both good connections and system growth capability. Three hundred VXI based digitizer channels have been developed for TJ-II diagnostics. They are completely software programmable and provide signal analog conditioning. In addition, some of them supply a programmable DSP for real time signal processing. Data will be stored in a central server using a special compression technique that allows compaction rates of over 80%. A specific application software has been developed to provide user interface for digitizer programming, signal visualization and data processing during TJ-II discharges. The software is an event based application that can be remotely launched from any X terminal. An authentication mechanism restricts access to authorised users only. (C) 1999 Elsevier Science S.A. All rights reserved. CR ALEJALDRE C, 1990, FUSION TECHNOL, V17, P131 DEPABLOS JL, 1996, IEEE T NUCL SCI 1, V43, P229 FLOR G, 1991, P IEEE 7 C REAL TIM, P109 PACIOS L, 1992, REV SCI INSTRUM, V63, P4806 SAUTHOFF NR, 1985, REV SCI INSTRUM, V56, P963 VANDERBEKEN H, 1989, IEEE T NUCL SCI, V36, P1639 VANHAREN PC, 1993, COMPUT EXP PHYS, V7, P638 VEGA J, 1996, REV SCI INSTRUM, V67, P4154 VEGA J, 1997, REV SCI INSTRUM 2, V68, P959 VEGA J, 1997, REV SCI INSTRUM 2, V68, P963 TC 6 BP 309 EP 319 PG 11 JI Fusion Eng. Des. PY 1999 PD JAN VL 43 IS 3-4 GA 161KQ PI LAUSANNE RP Vega J CIEMAT, Asociac EURATOM, CIEMAT Fus, Edificio 66,Avda Complutense 22, Madrid 28040, Spain J9 FUSION ENG DES PA PO BOX 564, 1001 LAUSANNE, SWITZERLAND UT ISI:000078288200010 ER PT Book in series AU Blasiak, J Dzwinel, W TI Visual clustering of multidimensional and large data sets using parallel environments SO HIGH-PERFORMANCE COMPUTING AND NETWORKING LA English DT Article NR 10 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Inst Comp Sci AGH, Al Mickiewicza 30, PL-30059 Krakow, Poland Inst Comp Sci AGH, PL-30059 Krakow, Poland ID EXPLORATORY PATTERN-ANALYSIS; MAPPING TECHNIQUES; RECOGNITION AB A method for visual clustering of large N-dimensional data sets is presented briefly. Its implementation on HP/Convex SPP/1600 enables visualization of data sets consisting of more than 10(4) multidimensional data vectors. The method was tested in PVM, MPI and data parallel environments. In the paper, the authors compare the parallel algorithm performance for these three interfaces. The results of tests, made to exemplify the algorithm "immunity" from data errors, are presented and discussed. CR BRODE S, 1986, COMPUT PHYS COMMUN, V42, P51 DZWINEL J, 1996, P 2 INT C APPL FUZZ DZWINEL W, 1995, ANN NUCL ENERGY, V22, P543 DZWINEL W, 1997, FUTURE GENER COMP SY, V12, P371 DZWINEL W, 1995, LECT NOTES COMPUT SC, V919, P508 DZWINEL W, 1995, P 3 C INT TECHN SOFT, V3, P1326 DZWINEL W, 1994, PATTERN RECOGN, V27, P949 JAIN D, 1988, ALGORITHMS CLUSTERIN, P37 SIEDLECKI W, 1988, PATTERN RECOGN, V21, P411 SIEDLECKI W, 1988, PATTERN RECOGN, V21, P431 TC 0 BP 403 EP 410 PG 8 SE LECTURE NOTES IN COMPUTER SCIENCE PY 1998 VL 1401 GA BM08P PI BERLIN RP Blasiak J Inst Comp Sci AGH, Al Mickiewicza 30, PL-30059 Krakow, Poland J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000077581300042 ER PT Journal AU Jain, LK Abbott, M TI DOVE: distributed objects based scientific visualization environment SO CONCURRENCY-PRACTICE AND EXPERIENCE LA English DT Article NR 7 SN 1040-3108 PU JOHN WILEY & SONS LTD C1 Oregon State Univ, Coll Ocean & Atmospher Sci, Corvallis, OR 97331 USA AB This paper describes the design and performance of a distributed, multi-tier architecture for scientific data visualization. A novel aspect of this framework is its integration of Java IDL, the CORBA distributed abject computing middleware, with JavaBeans, the Java Component model, to provide a flexible, interactive framework for distributed, high-performance scientific data visualization, CORBA server objects running in a distributed collaborative environment provide data acquisition and perform data-intensive computations, Clients such as Java Bean components use these server objects for data retrieval and provide an environment for visualization. The server objects use JDBC, the Java application programming interface to SQL databases, to retrieve data from the database. We discuss the system framework and its components and describe an example application and its performance, (C) 1998 John Wiley & Sons, Ltd. CR *OBJ MAN GROUP, 1996, COMM OBJ REQ BROK AR *SUN MICR, 1996, JAV SPEC *VIS SOFTW, 1997, VIS JAV PROGR GUID ENGLANDER R, 1997, JAVA SERIES GOLDSTEIN J, 1995, FRAMEWORK KNOWLEDGE GOSLING J, 1996, JAVA LANGUAGE ENV LANDIS S, 1997, THEORY PRACTICE OBJE TC 0 BP 1087 EP 1095 PG 9 JI Concurrency-Pract. Exp. PY 1998 PD SEP-NOV VL 10 IS 11-13 GA 150CB PI W SUSSEX RP Oregon State Univ, Coll Ocean & Atmospher Sci, Corvallis, OR 97331 USA J9 CONCURRENCY-PRACT EXPER PA BAFFINS LANE CHICHESTER, W SUSSEX PO19 1UD, ENGLAND UT ISI:000077644300026 ER PT Journal AU Zhu, DH Porter, AL TI Automated extraction and visualization of information for technological intelligence and forecasting SO TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE LA English DT Article NR 26 SN 0040-1625 PU ELSEVIER SCIENCE INC C1 Georgia Inst Technol, Technol Policy & Assessment Ctr, Atlanta, GA 30332 USA Georgia Inst Technol, Technol Policy & Assessment Ctr, Atlanta, GA 30332 USA Hefei Univ Technol, Inst Forecasting & Dev, Hefei 230009, Anhui, Peoples R China DE competitive technological intelligence; technology forecasting; text mining; innovation indicators; technology maps AB Empirical technology forecasting (TF) is not well utilized in technology management. Three factors could enhance managerial utilization: capability to exploit huge volumes of available information, ways to do so very quickly, and informative representations that help manage emerging technologies. This paper reports on efforts to address these three factors via partially automated processes to generate helpful knowledge from text quickly and graphically. We first illustrate a process to generate a family of technology maps that help convey emphases, players, and patterns in the development of a target technology. Second, we exemplify the generation of particular "innovation indicators" that measure particular facets of R&D activity to relate these to technological maturation, contextual influences, and market potential. Both technology mapping and innovation indicators rely upon searches in huge, easily accessible, abstract databases and text mining software. We augment these through "macros" (programming scripts) that automatically sequence the necessary steps to generate particular desired information products. These analytical findings can be tailored to the needs of particular technology managers. (C) 2002 Elsevier Science Inc. All rights reserved. CR BERRY MW, 1995, COMPUTATIONAL METHOD CARLISLE JP, 1999, HAW INT C SYST SCI H COATES V, 2001, TECHNOL FORECAST SOC, V67, P1 DECKER KM, 1995, TR9502 CSCS SWISS SC DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391 GLYMOUR C, 1996, DATA MIN KNOWL DISC, V1, P25 KIRBY MR, 2000, 2000 WORLD AV C AM I KOSTOFF RN, VARIOUS REPORTS BIBL LOSIEWICZ P, 2000, J INTELL INF SYST, V15, P99 MANNILA H, 1996, 8 INT C SCI STAT DAT, P1 PORTER AL, 2001, 22314 SOC COMP INT P PORTER AL, 2001, ENHANCING UTILIZATIO PORTER AL, IN PRESS FUTURES RES PORTER AL, 1998, MINING BIBLIOGRAPHIC PORTER AL, 1994, SRA J, V21, P21 PORTER AL, 1995, TECHNOL FORECAST SOC, V49, P237 PORTER AL, 2000, WHY DONT TECHNOLOGY SCHVANEVELDT RW, 1990, PATHFINDER ASS NETWO VANRAAN AFJ, 1993, RES EVALUAT, V3, P151 WATTS RJ, 1998, COMPET INTELL REV, V9, P1 WATTS RJ, 1999, INF KNOWL SYST MANAG, V1, P45 WATTS RJ, 1997, PRINCIPLES DATA MINI, P323 WATTS RJ, 1997, TECHNOL FORECAST SOC, V56, P25 ZHU D, 94 TOA ZHU D, TOA ILLUSTRATED CASE ZHU SP, 1999, COMPUTAT ENGN, V1, P1 TC 0 BP 495 EP 506 PG 12 JI Technol. Forecast. Soc. Chang. PY 2002 PD JUN VL 69 IS 5 GA 580YG PI NEW YORK RP Porter AL Georgia Inst Technol, Technol Policy & Assessment Ctr, Atlanta, GA 30332 USA J9 TECHNOL FORECAST SOC CHANGE PA 655 AVENUE OF THE AMERICAS, NEW YORK, NY 10010 USA UT ISI:000177263900006 ER PT Journal AU Keim, DA Hao, MC Dayal, U TI Hierarchical pixel bar charts SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS LA English DT Article NR 24 SN 1077-2626 PU IEEE COMPUTER SOC C1 AT&T Corp, Res, Florham Pk, NJ USA AT&T Corp, Res, Florham Pk, NJ USA Univ Constance, D-7750 Constance, Germany Hewlett Packard Res Labs, Palo Alto, CA USA DE information visualization; multidimensional data visualization; visual data exploration and data mining; very large multiattributes data sets; hierarchical visualization ID VISUALIZATION AB Simple presentation graphics are intuitive and easy-to-use, but only show highly aggregated data. Bar charts, for example, only show a rather small number of data values and x-y-plots often have a high degree of overlap. Presentation techniques are often chosen depending on the considered data type-bar charts, for example, are used for categorical data and x-y plots are used for numerical data. In this article, we propose a combination of traditional bar charts and x-y-plots, which allows the visualization of large amounts of data with categorical and numerical data. The categorical data dimensions are used for the partitioning into the bars and the numerical data dimensions are used for the ordering arrangement within the bars. The basic idea is to use the pixels within the bars to present the detailed information of the data records. Our so-called pixel bar charts retain the intuitiveness of traditional bar charts while applying the principle of x-y charts within the bars. In many applications, a natural hierarchy is defined on the categorical data dimensions such as time, region, or product type. In hierarchical pixel bar charts, the hierarchy is exploited to split the bars for selected portions of the hierarchy. Our application to a number of real-world e-business and Web services data sets shows the wide applicability and usefulness of our new idea. CR AHLBERG C, 1992, ACM CHI INTL C HUM F, P619 ANKERST M, 1996, P IEEE S VIS 96 ANUPAM V, 1995, P INT S INF VIS ATL, P82 BATTISTA GD, 1999, GRAPH DRAWING ALGORI BEDDOW J, 1990, P VISUALIZATION 90, P238 BUJA A, 1991, P VISUALIZATION 91, P156 CHIMERA R, 1992, P ACM SIGCHI 92 C HU, P293 EICK SG, 1999, VISUALIZING MULTIDIM HAO M, 1999, P IEEE S INF VIS 99, P124 HOFMANN H, 2000, METRIKA, V51, P11 INSELBERG A, 1990, P VISUALIZATION 90, P361 INSELBERG A, 1985, VISUAL COMPUT, V1, P69 KEIM DA, 1994, COMPUTER GRAPHIC SEP, P40 KEIM DA, 2000, IEEE T VIS COMPUT GR, V6, P59 KEIM DA, 2002, INFORMATION VISUALIZ, V1 KEIM DA, 2001, P IEEE S INF VIS 200 LAMPING J, 1994, ACM S US INT SOFTW T, P13 LAMPING J, 1995, P ACM SIGCHI C HUM F, P401 LEBLANC J, 1990, P VISUALIZATION 90, P230 PICKETT RM, 1988, P IEEE C SYST MAN CY, P514 RAO R, 1994, P ACM SIGCHI C HUM F, P318 ROBERTSON DE, 1991, BIOFACTORS, V3, P1 SHNEIDERMAN B, 1992, ACM T GRAPHIC, V11, P92 SHNEIDERMAN B, 1996, P VIS LANG TC 0 BP 255 EP 269 PG 15 JI IEEE Trans. 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PY 2002 PD JUL-SEP VL 8 IS 3 GA 577GA PI LOS ALAMITOS RP Keim DA AT&T Corp, Res, Florham Pk, NJ USA J9 IEEE TRANS VISUAL COMPUT GR PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000177052100005 ER PT Journal AU Hsu, WH Welge, M Redman, T Clutter, D TI High-performance commercial data mining: A multistrategy machine learning application SO DATA MINING AND KNOWLEDGE DISCOVERY LA English DT Article NR 41 SN 1384-5810 PU KLUWER ACADEMIC PUBL C1 Kansas State Univ, Dept Comp & Informat Sci, Manhattan, KS 66506 USA Kansas State Univ, Dept Comp & Informat Sci, Manhattan, KS 66506 USA Univ Illinois, Natl Ctr Supercomp Applicat, Automated Learning Grp, Champaign, IL 61820 USA DE constructive induction; scalable high-performance computing; real-world decision support applications; relevance determination; genetic algorithms; software development environments for knowledge discovery in databases (KDD) ID ALGORITHMS AB We present an application of inductive concept learning and interactive visualization techniques to a large-scale commercial data mining project. This paper focuses on design and configuration of high-level optimization systems (wrappers) for relevance determination and constructive induction, and on integrating these wrappers with elicited knowledge on attribute relevance and synthesis. In particular, we discuss decision support issues for the application (cost prediction for automobile insurance markets in several states) and report experiments using D2K, a Java-based visual programming system for data mining and information visualization, and several commercial and research tools. We describe exploratory clustering, descriptive statistics, and supervised decision tree learning in this application, focusing on a parallel genetic algorithm (GA) system, Jenesis, which is used to implement relevance determination (attribute subset selection). Deployed on several high-performance network-of-workstation systems (Beowulf clusters), Jenesis achieves a linear speedup, due to a high degree of task parallelism. Its test set accuracy is significantly higher than that of decision tree inducers alone and is comparable to that of the best extant search-space based wrappers. CR AHA DW, 1991, MACH LEARN, V6, P37 AUVIL L, 1999, DATA KNOWLEDGE D2K R BENJAMIN DP, 1990, CHANGE REPRESENTATIO BROOKS FP, 1995, MYTHICAL MAN MONTH E CHERKAUER KJ, 1996, P 2 INT C KNOWL DISC CHESEMAN P, 1988, P 5 INT C MACH LEARN, P54 DEJONG KA, 1993, MACH LEARN, V13, P161 DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1 DONOHO SK, 1996, UIUCDCSR1970 FAYYAD U, 1996, ADV KNOWLEDGE DISCOV, P82 GERSHO A, 1992, VECTOR QUANTIZATION GOLDBERG DE, 1989, GENETIC ALGORITHMS S GREFENSTETTE JJ, 1990, GENESIS GENETIC ALGO HAYKIN S, 1999, NEURAL NETWORKS COMP HSU W, IN PRESS ACTIVITIES HSU WH, 2000, MACH LEARN, V38, P213 HSU WH, 1999, P JOINT AAAI GECCO W HSU WH, 1998, UIUCDCSR2063 JOHNSONGENTILE K, 1994, J EDUC COMPUT RES, V11, P121 JONSKE J, 1999, UNPUB COMMUNICATION KIRA K, 1992, P 10 NAT C ART INT, P129 KOHAVI R, 1997, ARTIF INTELL, V97, P273 KOHAVI R, 1997, EUR C MACH LEARN ECM KOHAVI R, 1996, MCL PLUS PLUS MACHIN KOHAVI R, 1998, MINESET V2 6 KOHAVI R, 1995, THESIS STANFORD U KOHONEN T, 1996, A31 HELS U TECHN LAB KOHONEN T, 1990, P IEEE, V78, P1464 KONONENKO I, 1994, P EUR C MACH LEARN KOZA J, 1992, GENETIC PROGRAMMING KRISHNAMURTHY B, 1995, PRACTICAL REUSABLE U MITCHELL TM, 1997, MACHINE LEARNING NEAL RM, 1996, BAYESIAN LEARNING NE PORTER J, 1998, UNPUB COMMUNICATION PRINCIPE J, 1998, NEUROSOLUTIONS V3 02 QUINLAN JR, 1990, MACH LEARN, V5, P239 QUINLAN JR, 1985, MACH LEARN, V1, P81 RAYMER ML, 1997, P 7 INT C GEN ALG IC, P561 RUSSELL S, 1995, ARTIFICIAL INTELLIGE SARLE WS, NEURAL NETWORK FAQ P STERLING TL, 1999, BUILD BEOWULF GUIDE TC 0 BP 361 EP 391 PG 31 JI Data Min. Knowl. Discov. PY 2002 PD OCT VL 6 IS 4 GA 574AB PI DORDRECHT RP Hsu WH Kansas State Univ, Dept Comp & Informat Sci, Manhattan, KS 66506 USA J9 DATA MIN KNOWL DISCOV PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS UT ISI:000176865200002 ER PT Journal AU Glaser, DC Ubbelohde, MS TI Techniques for managing planar daylight data SO BUILDING AND ENVIRONMENT LA English DT Article NR 18 SN 0360-1323 PU PERGAMON-ELSEVIER SCIENCE LTD C1 Univ Calif Berkeley, Interdisciplinary Doctoral Program, 410 Soda Hall, Berkeley, CA 94720 USA Univ Calif Berkeley, Interdisciplinary Doctoral Program, Berkeley, CA 94720 USA DE daylight; energy efficiency; information visualization AB This paper describes fine-grained visualization techniques for reviewing time-dependent data common to building simulation. These techniques enable rapid inspection of trends and singularities in the data that are difficult to ascertain from conventional methods. In the case of daylight simulation, understanding when and where sunlight is available in a proposed design can lead to significant energy savings in the resulting electric lighting systems of buildings. This article presents an integrated framework of three main visualization routines for managing simulation data for exploratory data analysis. (C) 2002 Published by Elsevier Science Ltd. CR *ILL ENG SOC MS RE, 2000, IESNA LIGHT HDB REF, V1 BELL B, 2000, P 13 ANN ACM S US IN BIER E, 1993, P SIGGRAPH 93 CARD SK, 1999, READINGS INFORMATION GLASER D, IN PRESS ENV PLANN B HOPKINSON R, 1966, DAYLIGHTING MEYERS S, 1996, ASHRAE J JUN, P63 MUNEER T, 1997, SOLAR RAD DAYLIGHT M NAZZAL A, 1998, DAYLIGHTING 98 PAPAMICHAEL K, 1997, AUTOMAT CONSTR, V6, P341 ROGOWITZ BE, 1998, IEEE SPECTRUM, V35, P52 SHNEIDERMAN, 1983, IEEE COMPUTER, V16, P57 SPENCE R, 2001, INFORMATION VISUALIZ SUMPTION B, 1991, P COMP AID ARCH DES TUFTE ER, 1983, VISUAL DISPLAY QUANT, P197 TUKEY J, 1977, EXPLORATORY DATA ANA WARD G, 1994, COMPUTER GRAPHICS SI, V28, P459 WARE C, 2000, INFORMATION VISUALIZ TC 0 BP 825 EP 831 PG 7 JI Build. Environ. PY 2002 PD AUG-SEP VL 37 IS 8-9 GA 572NR PI OXFORD RP Glaser DC Univ Calif Berkeley, Interdisciplinary Doctoral Program, 410 Soda Hall, Berkeley, CA 94720 USA J9 BLDG ENVIRON PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND UT ISI:000176782100007 ER PT Book in series AU Saccomanno, FF Fu, LP Roy, RK TI Geographic information system-based integrated model for analysis and prediction of road accidents SO TRANSPORTATION DATA AND INFORMATION TECHNOLOGY LA English DT Article NR 13 SN 0361-1981 PU TRANSPORTATION RESEARCH BOARD NATL RESEARCH COUNCIL C1 Univ Waterloo, Dept Civil Engn, 200 Univ Ave W, Waterloo, ON N2L 3G1, Canada Univ Waterloo, Dept Civil Engn, Waterloo, ON N2L 3G1, Canada IBI Grp, Toronto, ON M5V 1V6, Canada AB The applicability and reliability of accident analysis and prediction models depend on their ability to integrate relevant input from disparate databases in a seamless and automated manner. These inputs include information on road geometry, traffic composition, accident profiles, and spatial referencing. With powerful functionality in spatial referencing, data management, and visualization, geographic information systems (GISs) provide a natural platform for this type of model. An integrated and user-friendly GIS platform for road accident analysis and prediction is described. To demonstrate this platform, it has been applied to safety problems specified at different levels of spatial aggregation, from individual route sections to the overall network. The model was developed by using databases obtained from the Ontario Ministry of Transportation. CR BAKER RJ, 1987, GLIM SYSTEM RELEASE CHONG KC, 1996, THESIS U WATERLOO WA DEAN C, 1986, J AM STAT ASSOC, V84, P467 FENG C, 1999, 78 ANN M TRANSP RES HAKKART AS, 1993, TRAFFIC ENG CONTROL HARKEY DL, 1999, J TRANSPORTATION RES, V1686, P13 HAUER E, 1992, ACCIDENT ANAL PREV, V24, P457 HAUER E, 1986, ACCIDENT ANAL PREV, V18, P1 JOBES B, 1998, 77 ANN M TRANSP RES MIAOU SP, 1994, ACCIDENT ANAL PREV, V26, P471 NASSAR S, 1994, INT J IMPACT ENG, V15 NASSAR SA, 1996, THESIS U WATERLOO WA PERSAUD BN, 1990, BLACK SPOT IDENTIFIC TC 0 BP 193 EP 202 PG 10 SE TRANSPORTATION RESEARCH RECORD PY 2001 IS 1768 GA BU64V PI WASHINGTON RP Saccomanno FF Univ Waterloo, Dept Civil Engn, 200 Univ Ave W, Waterloo, ON N2L 3G1, Canada J9 TRANSP RES REC PA 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA UT ISI:000176595000023 ER PT Journal AU Seo, J Shneiderman, B TI Interactively exploring hierarchical clustering results SO COMPUTER LA English DT Article NR 9 SN 0018-9162 PU IEEE COMPUTER SOC C1 Univ Maryland, Inst Adv Comp Studies, Dept Comp Sci, College Pk, MD 20742 USA Univ Maryland, Inst Adv Comp Studies, Dept Comp Sci, College Pk, MD 20742 USA Univ Maryland, Inst Adv Comp Studies, Human Comp Interact Lab, College Pk, MD 20742 USA ID GENOME AB To date, has focused largely on algorithmic methods for processing and manipulating vast biological data sets. Future improvements will likely provide users with guidance in selecting the most appropriate algorithms and metrics for identifying meaningful clusters-interesting patterns in large data sets, such as groups of genes with similar profiles. Hierarchical clustering has been shown to be effective in microarray data analysis for identifying genes with similar profiles and thus possibly with similar functions. Users also need an efficient visualization tool, however, to facilitate pattern extraction from microarray data sets. The Hierarchical Clustering Explorer integrates four interactive features to provide information visualization techniques that allow users to control the processes and interact with the results. Thus, hybrid approaches that combine powerful algorithms with interactive visualization tools will join the strengths of fast processors with the detailed understanding of domain experts. CR BITTNER M, 2000, NATURE, V406, P536 BROWN PO, 1999, NAT GENET S, V21, P33 CARD SK, 1999, READINGS INFORMATION EISEN MB, 1998, P NATL ACAD SCI USA, V95, P14863 HEDENFALK I, 2001, NEW ENGL J MED, V344, P539 INSELBERG A, 2000, P 6 INT C KNOWL DISC, P370 KANDOGAN E, 2001, P 7 INT C KNOWL DISC, P107 SHNEIDERMAN B, 1994, IEEE SOFTWARE 11 6, P70 WILLIAMSON C, 1992, P ACM SIGIR 92 C COP, P338 TC 0 BP 80 EP + PG 8 JI Computer PY 2002 PD JUL VL 35 IS 7 GA 568ZN PI LOS ALAMITOS RP Seo J Univ Maryland, Inst Adv Comp Studies, Dept Comp Sci, College Pk, MD 20742 USA J9 COMPUTER PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000176575000011 ER PT Journal AU Swaab, RI Postmes, T Neijens, P Kiers, MH Dumay, ACM TI Multiparty negotiation support: The role of visualization's influence on the development of shared mental models SO JOURNAL OF MANAGEMENT INFORMATION SYSTEMS LA English DT Article NR 50 SN 0742-1222 PU M E SHARPE INC C1 Amsterdam Sch Commun Res, Amsterdam, Netherlands Amsterdam Sch Commun Res, Amsterdam, Netherlands Univ Exeter, Sch Psychol, Exeter EX4 4QJ, Devon, England Univ Amsterdam, NL-1012 WX Amsterdam, Netherlands DE multiparty negotiation; negotiation support systems; prosocial climate; shared mental model; visualization of information ID INTERGROUP BIAS; ENTITATIVITY; SIMULATION; IDENTITY; CONFLICT AB The study examines a method for supporting multiparty negotiations by means of a Negotiation Support System (NSS). More specifically, this study investigated the effect of visualization support on the development of shared mental models among negotiators who resolved a spatial planning dispute. The objective of this study is to determine how to support the development of shared mental models in order to stimulate more productive negotiations. A further goal is to provide guidelines for the design of NSS. Compared with a control condition, visualization improved three aspects of negotiations: visualization support aided negotiators' convergence of perceptions of reality and had positive socio- emotional consequences in terms of increasing cohesiveness and entitativity. As a result, groups with visualization support reached consensus more easily and were more satisfied with the process. In sum, the current study provides support for the idea that presenting negotiators with unambiguous information helps negotiators develop shared mental models. 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Manage. Inform. Syst. PY 2002 PD SUM VL 19 IS 1 GA 565EV PI ARMONK RP Swaab RI Amsterdam Sch Commun Res, Amsterdam, Netherlands J9 J MANAGE INFORM SYST PA 80 BUSINESS PARK DR, ARMONK, NY 10504 USA UT ISI:000176356800006 ER PT Journal AU Peinel, G Rose, T TI Graphical information portals - The application of smart maps in GEoNET 4D SO GEOINFORMATICA LA English DT Article NR 32 SN 1384-6175 PU KLUWER ACADEMIC PUBL C1 FAW Ulm, Helmholtzstr 16, D-89081 Ulm, Germany FAW Ulm, D-89081 Ulm, Germany DE graphical information portals; information visualization; smart maps AB This paper presents a new brand of portals that elevates the concepts of information portals to a new level of interaction and navigation means. The graphical information portal is built upon the concept of smart maps for information visualization and exploration. Smart maps serve as navigational hub by indicating the existence of information carrying relevance for a specific location, by relating distributed multimedia information to a location on a map. of interest, and by categorizing information for easier orientation in subjects. Main advantage of a map-based information portal lies in the intuitive information presentation, navigation, search and retrieval. Representative usage scenarios include facility management, information kiosks, and environmental information systems. Groupware functions for collaborative visualization and interactive editing on maps are added on top in order to migrate the concept of graphical information portals to further, domains, such as concurrent engineering, risk management, and the like. An implementation of a geographical information portal has been designed and implemented in project GEoNET 4D, focussing on facility management for portuary zones. CR MUC MESS UND C TEXT RETR C TREC TIPSTER TEXT PROGRAM *GEON 4D CONS, 1997, ARCH FUNCT SPEC SYST *GEON 4D CONS, 1997, US REQ *NAT RES COUNC, 1999, DISTR GEOL SPAT INF ABEL DJ, 1998, EXPLORATION GIS ARCH ALBRECHT J, 1999, THESIS U VECHTA BEARD K, 1997, P 2 IEEE MET C SEPT CARTWRIGHT WE, 1998, INT CART ASS COMM VI CHEN H, 1997, INT J DIGITAL LIB COWIE J, 1996, CACM, V9, P80 CUGINI J, 1998, DOCUMENT CLUSTERING DAI F, 1998, VIRTUAL REALITY IND, P1 DAVENPORT TH, 1992, PROCESS INNOVATION R FITZKE J, 1997, GIS, V6, P25 GREENE S, 1997, CSTR3838 U MAR I ADV HARTENSTEIN K, 1995, P 95 GIS BALT SEA C HEARST M, 1997, MODERN INFORMATION R HEARST M, 1997, SCI AM KOCH T, 1997, ROLE CLASSIFICATION KRAAK MJ, 1997, COMPUT GEOSCI, V23, P457 KRATZ N, 1996, P INT C PRACT APPL K LARSON RR, 1996, GEOGRAPHIC INFORMATI, P81 LAURIAN C, 1990, ANN VASC SURG, V4, P1 MACEACHREN AM, 1998, P POL SPAT INF ASS C MALONE TW, 1993, GLOBALIZATION TECHNO, P37 MORSE EL, 1999, THESIS U PITTSBURGH ROSE T, 1996, MARC 96 EUR WORKSH A TARAYNOR CC, 1995, COMP ACM SIGCHI 1995, P288 TOMS EG, 1999, CHI 98 WORKSH INF EX WOODRUFF AG, 1994, J AM SOC INFORM SCI, V45, P645 TC 0 BP 327 EP 344 PG 18 JI Geoinformatica PY 2001 PD DEC VL 5 IS 4 GA 564CY PI DORDRECHT RP Peinel G FAW Ulm, Helmholtzstr 16, D-89081 Ulm, Germany J9 GEOINFORMATICA PA VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS UT ISI:000176298500002 ER PT Journal AU Allen, MM TI The hype over hyperbolic browsers SO ONLINE LA English DT Article NR 9 SN 0146-5422 PU ONLINE INC C1 Univ S Florida, Tampa, FL 33620 USA Univ S Florida, Tampa, FL 33620 USA ID INFORMATION VISUALIZATION CR CAMPBELL J, 1999, J AM SOC INFORM SCI, V50, P790 COLE B, 1998, ELECT ENG TIMES 0601, P24 CZERWINSKI M, 1998, INTERACTIONS NOV, P9 FOWLER RH, VISUALIZING BROWSING HAWKINS DT, 1999, ONLINE, V23, P88 HAWKINS DT, 1999, ONLINE, V23, P96 LAMPING J, 1996, P C HUM FACT COMP SY MUNZER T, VISUALIZING STRUCTUR PACK T, 1998, DATABASE, V21, P47 TC 0 BP 20 EP + PG 6 JI Online PY 2002 PD MAY-JUN VL 26 IS 3 GA 544VW PI WILTON RP Allen MM Univ S Florida, Tampa, FL 33620 USA J9 ONLINE PA 213 DANBURY RD, WILTON, CT 06897-4007 USA UT ISI:000175183200003 ER PT Journal AU Luttermann, H Freisleben, B Grauer, M Kelter, U Kamphusmann, T Merten, U Rossling, G Unger, T Waldhans, J TI Mediana: A workbench for the computer-based analysis of media data SO WIRTSCHAFTSINFORMATIK LA German DT Article NR 36 SN 0937-6429 PU VIEWEG C1 Univ Siegen, D-57076 Siegen, Germany Univ Siegen, D-57076 Siegen, Germany DE workbench; multimedia; database; face detection; video segmentation; cut detection; text detection; information visualization ID VIDEO; IMAGES AB This paper presents Mediana, a computer-based, integrated workbench for the management and analysis of media data supporting media researchers. Its main components are the recording and management of multimedia data (i.e. image, video and text data) in a database system, semi-automatical analysis tools for images and videos, and a graphical user interface (GUI) integrating all tools and components applied in media research. CR *CARN MELL U, 2001, INF DIG VID LIB *DFG SOND 240, 1998, ARB BILDSCH, V74 *U SO CA, 2001, NEUR WORKB ABERDEEN L, 1996, P TIPSTER 24 MONTH W ABERER K, 1998, HDB MULTIMEDIA COMPU, P579 ARMAN F, 1993, P 1 ACM INT C MULT A, P267 BORECZKY JS, 1998, P IEEE INT C AC SPEE, V6, P3741 BRAY T, 2000, EXTENSIBLE MARKUP LA BRUNELLI R, 1995, TEMPLATE MATCHING MA CANNY JF, 1986, PAMI, V8, P6 CARD SK, 1999, INFORMATION VISUALIZ DUNCAN G, BIOL WORKBENCH MOL B ENGEL M, 1999, CLIENT SERVER SYSTEM FREISLEBEN B, 1999, ARBEITSHEFTE BILDSCH, V76 GRAUER M, 1997, MULTIMEDIA ENTWURF E GRIFFEL F, 1998, COMPONENTWARE KONZEP HOLLFELDER S, 2000, MULTIMED TOOLS APPL, V11, P281 JAIN AK, 1998, PATTERN RECOGN, V31, P2055 KAMPFFMEYER U, 1997, GRUNDLAGEN DOKUMENTE KELTER U, 1992, P 16 ANN INT COMP SO, P45 KNOLL M, 2000, THESIS U SIEGEN KOBLA V, 1997, P SOC PHOTO-OPT INS, V3022, P200 KREUZER H, 1994, GESCH FERNSEBENS BUN LUDES P, 1998, ARBEITSHEFTE BILDSCH, V72 LUDES P, 2001, MULTIMEDIA MULTIMODE OLLIGSCHLAEGER A, 1999, 1999 ESRI US C JUL 2 PORNER B, 1998, 10 GMD FORSCH INF GM PUTZ S, 2000, ARBEITSHEFTE BILDSCH, V80 SEYLER AJ, 1965, P I RAD EL ENG AUSTR, V26, P355 STEINMETZ R, 2000, MULTIMEDIA TECHNOLOG SUEN HM, 1996, IEE P-VIS IMAGE SIGN, V143, P210 WALDHANS J, 1999, THESIS SIEGEN WALLNAU K, 2001, BUILDING SYSTEMS COM WECHSLER H, 1998, FACE RECOGNITION THE WU V, 1997, P 2 ACM INT C DIG LI, P3 YEO BL, 1996, P 2 INT C MULT COMP TC 0 BP 41 EP 51 PG 11 JI Wirtschaftsinformatik PY 2002 PD FEB VL 44 IS 1 GA 529WV PI WIESBADEN RP Luttermann H Univ Siegen, D-57076 Siegen, Germany J9 WIRTSCHAFTSINFORMATIK PA ABRAHAM-LINCOLN-STRABE 46, POSTFACH 15 47, D-65005 WIESBADEN, GERMANY UT ISI:000174325000006 ER PT Journal AU Chen, CH TI Generalized association plots: Information visualization via iteratively generated correlation matrices SO STATISTICA SINICA LA English DT Article NR 25 SN 1017-0405 PU STATISTICA SINICA C1 Acad Sinica, Inst Stat Sci, Taipei 115, Taiwan Acad Sinica, Inst Stat Sci, Taipei 115, Taiwan DE data visualization; divisive clustering tree; latent structure; perfect symmetry; proximity matrices; seriation ID NEGATIVE SYMPTOMS; SCHIZOPHRENIA; SANS; SAPS AB Given a p-dimensional proximity matrix D-pxp, a sequence of correlation matrices, R = (R-(l), R-(2),...), is iteratively formed from it. Here R-(1) is the correlation matrix of the original proximity matrix D and R-(n) is the correlation matrix of R(n-1), n > 1. This sequence was first introduced by McQuitty (1968), Breiger, Boorman and Arabie (1975) developed an algorithm, CONCOR, based on their rediscovery of its convergence. The sequence R often converges to a matrix R- (infinity) whose elements are +1 or -1. This special pattern of R-(infinity) partitions the p objects into two disjoint groups and so can be recursively applied to generate a divisive hierarchical clustering tree. While convergence is itself useful, we are more concerned with what happens before convergence. Prior to convergence, we note a rank reduction property with elliptical structure: when the rank of R-(n) reaches two, the column vectors of R-(n) fall on an ellipse in a two-dimensional subspace. The unique order of relative positions for the p points on the ellipse can be used to solve seriation problems such as the reordering of a Robinson matrix. A software package, Generalized Association Plots (GAP), is developed which utilizes computer graphics to retrieve important information hidden in the data or proximity matrices. CR ANDREASEN NC, 1995, ARCH GEN PSYCHIAT, V52, P341 ANDREASEN NC, 1983, SCALE ASSESSMENT NEG ANDREASEN NC, 1984, SCALE ASSESSMENT POS BREIGER RL, 1975, J MATH PSYCHOL, V12, P328 CARAUX G, 1984, REV STAT APPL, V32, P5 CHEN CH, 2000, STAT SINICA, V10, P665 FISHER RA, 1936, ANN EUGENICS 2, V7, P179 GALE N, 1984, J CLASSIF, V1, P75 HUBERT LJ, 1976, BRIT J MATH STAT PSY, V29, P32 KRUSKAL BJ, 1977, UNPUB THEOREM CONCOR LIN ASK, 1998, PSYCHIAT RES, V77, P121 LYER VR, 1999, SCIENCE, V283, P83 MARCOTORCHINO F, 1991, APPL STOCH MODEL BUS, V7, P139 MCFARLANE M, 1994, J COMPUTATIONAL GRAP, V3, P23 MCQUITTY LL, 1968, MULTIVARIATE BEHAVIO, V3, P465 MINAS IH, 1992, SCHIZOPHR RES, V8, P143 MINNOTTE M, 1998, 1998 P SECT STAT GRA MURDOCH DJ, 1996, AM STAT, V50, P178 ROBINSON WS, 1951, AM ANTIQUITY, V16, P293 SOKAL RR, 1963, PRINCIPLES NUMERICAL STRENG R, 1991, CLASSIFICATION DATA, P121 STUART GW, 1995, SCHIZOPHR RES, V16, P175 TAKANE Y, 1977, PSYCHOMETRIKA, V42, P7 TIERNEY L, 1990, LISP STAT OBJECT ORI WEN XL, 1998, P NATL ACAD SCI USA, V95, P334 TC 0 BP 7 EP 29 PG 23 JI Stat. Sin. PY 2002 PD JAN VL 12 IS 1 GA 530RM PI TAIPEI RP Chen CH Acad Sinica, Inst Stat Sci, Taipei 115, Taiwan J9 STAT SINICA PA C/O DR H C HO, INST STATISTICAL SCIENCE, ACADEMIA SINICA, TAIPEI 115, TAIWAN UT ISI:000174372800002 ER PT Book in series AU Poon, SH Shin, CS Strijk, T Wolff, A TI Labeling points with weights SO ALGORITHMS AND COMPUTATION, PROCEEDINGS LA English DT Article NR 13 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 HKUST, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China HKUST, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China Univ Utrecht, Dept Comp Sci, NL-3508 TB Utrecht, Netherlands Univ Greifswald, Inst Math & Informatik, Greifswald, Germany ID ALGORITHMS; PLACEMENT; SET AB Annotating maps, graphs, and diagrams with pieces of text is an important step in information visualization that is usually referred to as label placement. We define nine label-placement models for labeling points with axis-parallel rectangles given a weight for each point. There are two groups; fixed-position models and slider models. We aim to maximize the weight sum of those points that receive a label. We first compare our models by giving bounds for the ratios between the weights of maximum- weight labelings in different models, Then we present algorithms for labeling points with unit-height rectangles. We show how an O(n log n)-time factor-2 approximation algorithm and a PTAS for fixed-position models can be extended to handle the weighted case. Our main contribution is the first algorithm for weighted sliding labels. Its approximation factor is (2 + epsilon), it runs in O(n(2)/epsilon) time and uses O(n/epsilon) space. We also investigate some special cases. CR AGARWAL PK, 1998, COMP GEOM-THEOR APPL, V11, P209 BERMAN P, 2000, J COMB OPTIM, V4, P307 CHAZELLE B, 1999, ADV DISCRETE COMPUTA, V223, P407 CHRISTENSEN J, 1995, ACM T GRAPHIC, V14, P203 ERLEBACH T, 2001, P 12 ACM SIAM S DISC, P671 FORMANN M, 1991, P 7 ANN ACM S COMP G, P281 HSIAO JY, 1992, INFORM PROCESS LETT, V43, P229 ITURRIAGA C, 1999, THESIS U WATERLOO MORRISON JL, 1980, COMPUTER CONT CARTOG POON SH, 2001, 72001 TR STRIJK T, 1999, P 7 ACM S ADV GEOGR, P47 VANKREVELD M, 1999, COMP GEOM-THEOR APPL, V13, P21 WOLFF A, 1996, MAP LABELING BIBLIO TC 0 BP 610 EP 622 PG 13 SE LECTURE NOTES IN COMPUTER SCIENCE PY 2001 VL 2223 GA BT91P PI BERLIN RP Poon SH HKUST, Dept Comp Sci, Hong Kong, Hong Kong, Peoples R China J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000174430700052 ER PT Book in series AU Bjork, S Redstrom, J Ljungstrand, P Holmquist, LE TI PowerView - Using information links and information views to navigate and visualize information on small displays SO HANDHELD AND UBIQUITOUS COMPUTING, PROCEEDINGS LA English DT Article NR 19 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Interact Inst, PLAY Appl Res Art & Technol, Box 620, SE-40530 Gothenburg, Sweden Interact Inst, PLAY Appl Res Art & Technol, SE-40530 Gothenburg, Sweden AB PowerView is a PDA application designed to support people with situational information, primarily during conversations and meetings with other people. PowerView was designed to address a number of issues in interface design concerning both information visualization and interaction on small, mobile devices. In terms of information visualization, the system was required to provide the user With a single integrated information System that enabled quick access to related information once an object of interest had been selected. In terms of interaction, the system was required to enable easy and efficient information retrieval, including single-handed use of the device. These problems were addressed by introducing Information Links and Information Views. An evaluation of the application against the standard application suite bundle of die PDA, a Casio Cassiopeia E-11, proved the interfaces equivalent in usability even though the PowerView application uses a novel interface paradigm and the test subjects were given no training time with the system. CR BJORK S, 2000, EXT ABSTR CHI 2000, P265 BJORK S, 1999, P ACM UIST 99, P187 BJORK S, 2000, P AVI 2000, P232 BJORK S, 1999, P IEEE INF VIS 99, P53 CARD SK, 1999, READINGS INFORMATION, P1 FELLBAUM C, 1998, WORDNET ELECT LEXICA FURNAS GW, 1986, P SIGCHI86, P16 HOLMQUIST LE, 1997, EXT ABSTR CHI 97 KAWACHIYA K, 1998, P CHI 98, P1 KRISTOFFERSEN S, 1999, P GROUP 99 LEUNG YK, 1994, ACM T COMPUTER HUMAN, V1, P126 NORMAN DA, 1998, INVISIBLE COMPUTER RAO R, 1995, COMMUN ACM, V38, P29 REKIMOTO J, 1996, P ACM S US INT SOFTW, P167 SARKAR M, 1994, COMMUN ACM, V37, P73 SCHILIT BN, 1994, P WORKSH MOB COMP SY, P85 SUGIMOTO M, 1996, P ACM C HUM FACT COM, P7 TAIVALSAARI A, 1999, TR9974 SUN MICR LAB WANT R, 1995, CSL951 XER PAL ALT R TC 0 BP 46 EP 62 PG 17 SE LECTURE NOTES IN COMPUTER SCIENCE PY 2000 VL 1927 GA BT81N PI BERLIN RP Bjork S Interact Inst, PLAY Appl Res Art & Technol, Box 620, SE-40530 Gothenburg, Sweden J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000174114700004 ER PT Journal AU Oldfield, T TI Pattern-recognition methods to identify secondary structure within X-ray crystallographic electron-density maps SO ACTA CRYSTALLOGRAPHICA SECTION D-BIOLOGICAL CRYSTALLOGRAPHY LA English DT Article NR 15 SN 0907-4449 PU MUNKSGAARD INT PUBL LTD C1 Univ York, Dept Chem, Accelrys Inc, York YO10 5DD, N Yorkshire, England Univ York, Dept Chem, Accelrys Inc, York YO10 5DD, N Yorkshire, England ID FRAGMENT; PROTEINS; ACID AB The interpretation of macromolecular crystallographic electron- density maps is a difficult and traditionally a manual step in the determination of a protein structure. The visualization of information within an electron-density map can be extremely arduous owing to the amount and complexity of information present. The ability to see the overall fold and structure of the molecule is usually lost among all the detail, particularly with larger structures. This paper describes a novel method of analysis of electron density in real space that can determine the secondary structure of a protein within minutes without any user intervention. The method is able to work with poor data as well as good data at resolutions down to 3.5 Angstrom and is integral to the functionality of QUANTA. This article describes the methodology of the pattern recognition and its use with a number of sets of experimental data. CR COWTAN K, 1998, ACTA CRYSTALLOGR D 4, V54, P487 COWTAN K, 1998, ACTA CRYSTALLOGR D 5, V54, P750 COWTAN KD, 1999, PROG BIOPHYS MOL BIO, V72, P245 DICKERSON RE, 1969, STRUCTURE ACTIONS PR, P28 DUGGLEBY HJ, 1995, NATURE, V373, P264 GREER J, 1974, J MOL BIOL, V82, P279 JONES TA, 1992, P CCP4 STUD WEEK MOL, P91 KLEYWEGT GJ, 1997, ACTA CRYSTALLOGR D 2, V53, P179 MIZUGUCHI K, 1995, PROTEIN ENG, V8, P353 OLDFIELD TJ, 2001, ACTA CRYSTALLOGR 10, V57, P1421 OLDFIELD TJ, 2001, ACTA CRYSTALLOGR D 1, V57, P82 OLDFIELD TJ, 1992, J MOL GRAPHICS, V10, P247 SEVCIK J, 1991, ACTA CRYSTALLOGR B, V47, P240 SNIJDER HJ, 1999, NATURE, V401, P717 TYRRELL R, 1997, STRUCTURE, V5, P1017 TC 0 BP 487 EP 493 PG 7 JI Acta Crystallogr. Sect. D-Biol. Crystallogr. PY 2002 PD MAR VL 58 PN 3 GA 528CQ PI COPENHAGEN RP Oldfield T Univ York, Dept Chem, Accelrys Inc, York YO10 5DD, N Yorkshire, England J9 ACTA CRYSTALLOGR D-BIOL CRYST PA 35 NORRE SOGADE, PO BOX 2148, DK-1016 COPENHAGEN, DENMARK UT ISI:000174227200014 ER PT Journal AU Bouton, CMLS Pevsner, J TI DRAGON View: information visualization for annotated microarray data SO BIOINFORMATICS LA English DT Article NR 4 SN 1367-4803 PU OXFORD UNIV PRESS C1 Johns Hopkins Sch Med, Dept Neurosci, Baltimore, MD 21205 USA Johns Hopkins Sch Med, Dept Neurosci, Baltimore, MD 21205 USA Kennedy Krieger Inst, Dept Neurol, Baltimore, MD 21205 USA ID PROTEIN AB The DRAGON View information visualization tools aid in the comprehensive analysis of large-scale gene expression data that has been annotated with biologically relevant information through the generation of three types of complementary graphical outputs. CR BAIROCH A, 2000, NUCLEIC ACIDS RES, V28, P45 BATEMAN A, 2000, NUCLEIC ACIDS RES, V28, P263 BOUTON CMLS, 2000, BIOINFORMATICS, V16, P1038 KANEHISA M, 2000, NUCLEIC ACIDS RES, V28, P27 TC 0 BP 323 EP 324 PG 2 JI Bioinformatics PY 2002 PD FEB VL 18 IS 2 GA 524TC PI OXFORD RP Pevsner J Johns Hopkins Sch Med, Dept Neurosci, Baltimore, MD 21205 USA J9 BIOINFORMATICS PA GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND UT ISI:000174028100014 ER PT Journal AU Dahlberg, TA Subramanian, KR TI Visualization of mobile network Simulations SO SIMULATION LA English DT Article NR 29 SN 0037-5497 PU SIMULATION COUNCILS INC C1 Univ N Carolina, Dept Comp Sci, 9201 Univ City Blvd, Charlotte, NC 28223 USA Univ N Carolina, Dept Comp Sci, Charlotte, NC 28223 USA DE mobile networks; visualization AB The use of adaptive techniques in mobile networks permits scalable resource allocation policies to meet varying demand as well as Quality of Service (QoS) performance objectives. As these algorithms operate at multiple layers of communications architecture, evaluation of such techniques must take into account a variety of scenarios, which are in turn parameterized by a large number of variables. The need to monitor algorithm behavior in real time results in a data explosion. In this work, we propose new real-time metrics to characterize an identify the critical states of a mobile network in the wake of channel failures, congestion, signal degradation, etc. We use these metrics to define a survivability index, a measure of mobile network performance in the wake of failures. We demonstrate the effectiveness of information visualization techniques in understanding the complex spatial and temporal relationships between performance and cost metrics that influence adaptive algorithms. Our visualization system is highly scalable and interactive, permitting multiple algorithms to be simultaneously evaluated. We demonstrate applications to network monitoring and to the design and evaluation of adaptive admission control algorithms. CR ANASTASI G, 1998, IEEE PERSONAL CO OCT, P53 BOUMERDASSI S, 1999, MOBILE NETW APPL, V4, P111 BUJA A, 1991, P VIS 91 SAN DIEG CA, P22 CLEVELAND WS, 1985, ELEMENTS GRAPHING DA DAHLBERG T, IN PRESS ACM BALTZER DAHLBERG TA, 2000, P MOD SIM WIR MOB SY ECKHARDT DA, 1999, MOBILE NETW APPL, V4, P273 FEINER S, 1992, P VIS 1992 BOST MA O, P283 FUA YH, 2000, IEEE T VIS COMPUT GR, V6, P150 HARPANTIDOU Z, 1998, IEEE PERSONAL CO APR, P48 HENDERSON DA, ACM T GRAPHICS, V5, P211 IERA A, 1996, WINET, V2, P249 INSELBERG A, 1997, P INF VIS OCT, P100 INSELBERG A, 1990, P VISUALIZATION 90, P361 JAIN R, 1991, ART COMPUTER SYSTEMS LEBLANC J, 1990, P VISUALIZATION 90, P230 LEE K, 1996, P INT C MOB COMP NET, P62 LU S, 1996, P SIGCOMM AUG MODIANO E, 1999, WIREL NETW, V5, P279 MOITRA SD, 1997, IEICE T COMMUNICAT B, V80 OLIVEIRA C, 1998, IEEE J SEL AREA COMM, V16, P858 PRISCOLI FD, 1998, IEEE PERSONAL CO APR, P56 RYU B, 1999, IEEE COMMUNICATI JUL, P48 SCHROEDER W, 1998, VISUALIZATION TOOLKI SUBRAMANIAN KR, IEEE S INF VIS 2000 TALUKDAR AK, 1999, WIREL NETW, V5, P111 TIPPER D, 1999, P MOB WIR COMM NETW VANWIJK JJ, 1993, P IEEE VISUALIZATION, P119 WARD MO, 1994, P IEEE C VIS SAN JOS, P326 TC 0 BP 128 EP 140 PG 13 JI Simulation PY 2001 PD SEP-OCT VL 77 IS 3-4 GA 520GN PI SAN DIEGO RP Dahlberg TA Univ N Carolina, Dept Comp Sci, 9201 Univ City Blvd, Charlotte, NC 28223 USA J9 SIMULATION PA PO BOX 17900, SAN DIEGO, CA 92117 USA UT ISI:000173772800005 ER PT Journal AU Thomas, J Cowley, P Kuchar, O Nowell, L Thomson, J Wong, PC TI Discovering knowledge through visual analysis SO JOURNAL OF UNIVERSAL COMPUTER SCIENCE LA English DT Article NR 29 PU SPRINGER-VERLAG C1 Battelle Mem Inst, Pacific NW Natl Lab, Richland, WA 99352 USA Battelle Mem Inst, Pacific NW Natl Lab, Richland, WA 99352 USA DE information visualization; knowledge management; digital content and media; digital libraries; visual paradigms; higher- order interaction; human-computer interaction ID VISUALIZATION AB This paper describes our vision for the near future in digital content analysis as it relates to the creation, verification, and presentation of knowledge. We focus on how visualization enables humans to make discoveries and gain knowledge. Visualization, in this context, is not just the picture representing the data but also a two-way interaction between humans and their information resources for the purposes of knowledge discovery, verification, and the sharing of knowledge with others. We present visual interaction and analysis examples to demonstrate how one current visualization tool analyzes large, diverse collections of text. This is followed by lessons learned and the presentation of a core concept for a new human information discourse. CR AHLBERG C, 1994, P ACM C HUM FACT COM, P313 CARD S, 1999, READINGS INFORMATION CARD SK, 1991, P CHI 91 HUMAN FACTO, P181 FAIRCHILD KM, 1988, COGNITIVE SCI ITS AP, P201 GERSHON N, 1997, IEEE COMPUTER GR JUL, P29 HAVRE S, 2000, P IEEE S INF VIS INF, P115 HEARST M, 1995, P CHI 95, P59 HEMMJE M, 1994, P 17 ANN INT ACM SIG, P249 JAIN A, 1988, ALGORITHMS CLUSTERIN KEIM DA, 1996, IEEE T KNOWL DATA EN, V8, P923 LIN X, 1992, P VIS 92, P274 LYMAN P, 2001, MUCH INFORMATION MCCORMICK BH, 1987, COMPUT GRAPHICS, V21, P1 MILLER NE, 1998, P IEEE VIS 98 NEW YO, P189 NOWELL LT, 1996, P 19 INT ACM SIGIR C, P67 OLSEN KA, 1993, INFORM PROCESS MANAG, V29, P69 RAO R, 1994, P ACM SIGCHI C HUM F, P318 RASMUSSEN E, 1992, INFORMATION RETRIEVA, P419 RBARSKY W, 1999, IEEE COMPUT GRAPH, V19, P32 RICH E, 1991, ARTIF INTELL, P487 RUSSELL S, 1995, ARTIFICIAL INTELLIGE SCHANK R, 1995, RETHINKING THEORY SPOERRI A, 1993, P IEEE VIS 93, P150 THOMAS J, 1999, P BRIT COMP SOC C TUFTE E, 1990, ENVISIONING INFORMAT TUFTE E, 1983, VISUAL DISPLAY QUANT TUFTE ER, 1997, VISUAL EXPLANATIONS, P90 WARE C, 2000, INFORMATION VISUALIZ WONG PC, 1999, IEEE COMPUT GRAPH, V19, P20 TC 0 BP 517 EP 529 PG 13 JI J. Univers. Comput. Sci. PY 2001 VL 7 IS 6 GA 519LB PI NEW YORK RP Thomas J Battelle Mem Inst, Pacific NW Natl Lab, Richland, WA 99352 USA J9 J UNIVERS COMPUT SCI PA 175 FIFTH AVE, NEW YORK, NY 10010 USA UT ISI:000173726800007 ER PT Journal AU Keim, DA TI Information visualization and visual data mining SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS LA English DT Article NR 67 SN 1077-2626 PU IEEE COMPUTER SOC C1 AT&T Shannon Res Labs, Florham Pk, NJ 07932 USA AT&T Shannon Res Labs, Florham Pk, NJ 07932 USA Univ Constance, Dept Comp Sci, D-7750 Constance, Germany DE information visualization; visual data mining; visual data exploration; classification ID SPACE AB Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section. 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Vis. Comput. Graph. PY 2002 PD JAN-MAR VL 8 IS 1 GA 515FT PI LOS ALAMITOS RP Keim DA AT&T Shannon Res Labs, Florham Pk, NJ 07932 USA J9 IEEE TRANS VISUAL COMPUT GR PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000173485300001 ER PT Journal AU Kreuseler, M Schumann, H TI A flexible approach for visual data mining SO IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS LA English DT Article NR 33 SN 1077-2626 PU IEEE COMPUTER SOC C1 Univ Rostock, Fachbereich Informat, PostBox 999, D-18051 Rostock, Germany Univ Rostock, Fachbereich Informat, D-18051 Rostock, Germany DE information visualization; multidimensional information modeling; hierarchies; focus plus context techniques; clustering; maps; information analysis AB The exploration of heterogenous information spaces requires suitable mining methods as well as effective visual interfaces. Most of the existing systems concentrate either on mining algorithms or on visualization techniques. This paper describes a flexible framework for Visual Data Mining which combines analytical and visual methods to achieve a better understanding of the information space. We provide several preprocessing methods for unstructured information spaces such as a flexible hierarchy generation with user controlled refinement. Moreover, we develop new visualization techniques including an intuitive Focus+Context technique to visualize complex hierarchical graphs. A special feature of our system is a new paradigm for visualizing information structures within their frame of reference. 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Vis. Comput. Graph. PY 2002 PD JAN-MAR VL 8 IS 1 GA 515FT PI LOS ALAMITOS RP Kreuseler M Univ Rostock, Fachbereich Informat, PostBox 999, D-18051 Rostock, Germany J9 IEEE TRANS VISUAL COMPUT GR PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000173485300004 ER PT Journal AU Castello, R Mili, R Tollis, IG TI Automatic layout of statecharts SO SOFTWARE-PRACTICE & EXPERIENCE LA English DT Article NR 32 SN 0038-0644 PU JOHN WILEY & SONS LTD C1 Univ Texas, Dept Comp Sci, Box 830688, Richardson, TX 75083 USA Univ Texas, Dept Comp Sci, Richardson, TX 75083 USA DE requirements specification; statecharts; information visualization; graph drawing ID DIRECTED-GRAPHS; SYSTEMS AB Graphical notations are widely used for system specification. The usefulness of these notations depends primarily on their readability. Hence, automatic methods are needed to obtain efficient and understandable graphical representations of requirements. In this paper, we present an algorithm that automatically generates layouts of statecharts. We assume that relevant information is stored in a structure that we call a decomposition tree, and we draw the graph that models a statechart in a hierarchical fashion. Our approach excludes diagrams with inter-level transitions. Copyright (C) 2001 John Wiley Sons, Ltd. CR *ART SOFTW TOOLS, REAL TIM STUD RAT AL CASTELLO R, 2000, P FORM METH TOOLS 20 CHRISTIANI K, 1995, NERVENHEILKUNDE, V14, P3 DIBATTISTA G, 1994, COMP GEOM-THEOR APPL, P235 DIBATTISTA G, 1999, GRAPH DRAWING ALGORI DODDI S, 1997, P 8 ACM SIAM S DISCR, P148 EADES P, 1993, INT J COMPUTATIONAL, V3, P133 EADES P, 1997, LECT NOTES COMPUTER, V1353, P146 EADES P, 1997, LNCS, V1190, P113 FORMANN M, 1991, P 7 ANN ACM S COMP G, P281 GANSNER ER, 1993, IEEE T SOFTWARE ENG, V19, P214 GANSNER ER, 1988, SOFTWARE PRACTICE EX, V18, P1047 GAREY MR, 1983, SIAM J ALGEBRA DISCR, V4, P312 HAREL D, 1990, IEEE T SOFTWARE ENG, V16, P403 HAREL D, 1990, P INT C ADV VIS INT HAREL D, 1987, SCI COMPUT PROGRAM, V8, P231 IMHOF E, 1975, AM CARTOGRAPHER, V2, P128 KAKOULIS K, 1997, LECT NOTES COMPUTER, V1353, P169 KAKOULIS KG, 1997, LECT NOTES COMPUTER, V1190, P241 KUH ES, 1990, P IEEE, V78, P237 LENGAUER T, 1990, COMBINATORIAL ALGORI MESSINGER EB, 1991, IEEE T SYST MAN CYB, V21, P1 ODONNEL R, AUTOMATIC CODE EMBED PETERSON J, OVERCOMING CRISIS RE PURCHASE H, 1997, LECT NOTES COMPUTER, V1353, P248 ROWE LA, 1987, SOFTWARE PRACT EXPER, V17, P61 SEEMAN J, 1997, LECT NOTES COMPUTER, V1353, P415 STOCKMEYER L, 1983, INFORM CONTR, V57, P91 SUGIYAMA K, 1981, IEEE T SYST MAN CYB, V11, P109 WAGNER F, 1995, P 11 ANN ACM S COMP, P109 WIMER S, 1988, IEEE T CIRCUITS SYST, V35, P267 YOELI P, 1972, CARTOGR J, V9, P99 TC 0 BP 25 EP 55 PG 31 JI Softw.-Pract. Exp. PY 2002 PD JAN VL 32 IS 1 GA 510GY PI W SUSSEX RP Mili R Univ Texas, Dept Comp Sci, Box 830688, Richardson, TX 75083 USA J9 SOFTWARE-PRACT EXP PA BAFFINS LANE CHICHESTER, W SUSSEX PO19 1UD, ENGLAND UT ISI:000173201400002 ER PT Book in series AU Martin-Merino, M Munoz, A TI Self Organizing Map and Sammon Mapping for asymmetric proximities SO ARTIFICAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS LA English DT Article NR 18 SN 0302-9743 PU SPRINGER-VERLAG BERLIN C1 Univ Salamanca, C Compania 3, Salamanca 37002, Spain Univ Salamanca, Salamanca 37002, Spain Univ Carlos III Madrid, Getafe 28903, Spain ID ORGANIZATION AB Self Organizing Maps (SOM) and Sammon Mapping (SM) are two information visualization techniques widely used in the data mining community. These techniques assume that the similarity matrix for the data set under consideration is symmetric. However there are many interesting problems where asymmetric proximities arise, like text mining problems are. In this work we propose modified versions of SOM and SM to deal with data where the proximity matrix is asymmetric. The algorithms are tested using a real document database, and performance is reported using appropriate measures. As a result, the asymmetric algorithms proposed outperform their symetric counterparts. CR BAEZAYATES R, 1999, MODERN INFORMATION R BEZDEK JC, 1995, PATTERN RECOGN, V28, P381 CARPENTER KE, 1992, HARVARD LIBR BULL, V3, P5 CHEN HC, 1998, J AM SOC INFORM SCI, V49, P582 CHENETIER M, 1996, AM BOOK REV, V18, P8 COX TF, 2001, MULTIDIMENSIONAL SCA DAGAN I, 1999, MACH LEARN, V34, P48 KASKI S, 1998, P IJCNN 98 INT JOINT, V1, P413 KOHONEN T, 2000, IEEE T NEURAL NETWOR, V11, P574 KOPCSA A, 1998, J AM SOC INFORM SCI, V49, P7 KOSKO B, 1991, NEURAL NETWORKS FUZZ LEWIS DD, REUTERS 21578 DISTRI MULIER F, 1995, NEURAL COMPUT, V7, P1165 MUNOZ A, 1997, J INTELLIGENT DATA A, V1 MUNOZ A, LNCS, V866, P376 RORVIG M, 1999, J AM SOC INFORM SCI, V50, P639 SAMMON JW, 1969, IEEE T COMPUT, V18, P401 ZIELMAN B, 1996, BRIT J MATH STAT P 1, V49, P127 TC 0 BP 429 EP 435 PG 7 SE LECTURE NOTES IN COMPUTER SCIENCE PY 2001 VL 2130 GA BT43Y PI BERLIN RP Martin-Merino M Univ Salamanca, C Compania 3, Salamanca 37002, Spain J9 LECT NOTE COMPUT SCI PA HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY UT ISI:000173024600059 ER PT Journal AU Munzner, T TI Information visualization SO IEEE COMPUTER GRAPHICS AND APPLICATIONS LA English DT Editorial Material NR 4 SN 0272-1716 PU IEEE COMPUTER SOC C1 Compaq Syst Res Ctr, Palo Alto, CA USA Compaq Syst Res Ctr, Palo Alto, CA USA CR AHLBERG C, 1994, P ACM C HUM FACT COM, P313 CARD S, 1999, READINGS INFORMATION SPENCE R, 2001, INFORMATION VISUALIZ WARE C, 2000, INFORMATION VISUALIZ TC 0 BP 20 EP 21 PG 2 JI IEEE Comput. Graph. Appl. PY 2002 PD JAN-FEB VL 22 IS 1 GA 505MC PI LOS ALAMITOS RP Munzner T Compaq Syst Res Ctr, Palo Alto, CA USA J9 IEEE COMPUT GRAPH APPL PA 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720- 1314 USA UT ISI:000172915700005 ER PT Journal AU Gage, SH Colunga-Garcia, M Helly, JJ Safir, GR Momin, A TI Structural design for management and visualization of information for simulation models applied to a regional scale SO COMPUTERS AND ELECTRONICS IN AGRICULTURE LA English DT Article NR 10 SN 0168-1699 PU ELSEVIER SCI LTD C1 Michigan State Univ, Dept Entomol, Computat Ecol & Visualizat Lab, E Lansing, MI 48824 USA Michigan State Univ, Dept Entomol, Computat Ecol & Visualizat Lab, E Lansing, MI 48824 USA Michigan State Univ, Dept Plant Pathol, Computat Ecol & Visualizat Lab, E Lansing, MI 48824 USA Michigan State Univ, Dept Comp Sci, E Lansing, MI 48824 USA Univ Calif San Diego, San Diego Supercomp Ctr, La Jolla, CA 92093 USA DE modelling environment; regional modeling; crop growth; crop yield; visualization; commercial software ID TEMPERATURE AB A Modeling Applications System Integrative Framework (MASIF) was developed to facilitate regional-scale long-term simulations. MASIF links an array of existing visualization, analytical, and data management software to manage large volumes of model inputs and outputs as well as model execution to facilitate model development and analysis. Information from MASIF is shown in visual form, an approach that we believe is preferable for comprehending information contained in large datasets associated with models that simulate processes and patterns at regional scales. As an example, MASIF was used to manage and visualize daily simulations of maize growth, development, and yield from 1055 Midwestern locations in the United States. (C) 2001 Published by Elsevier Science B.V. CR BROWN JR, 1995, VISUALIZATION COMPUT, P287 BURKE IC, 1997, ECOLOGY, V78, P1330 MCCARTER JB, 1998, J FOREST, V96, P17 MUCHOW RC, 1991, AGRON J, V83, P1052 MUCHOW RC, 1990, AGRON J, V82, P338 PIJANOWSKI BC, 2000, LANDSCAPE ECOLOGY TO, P246 REYNOLDS KM, 2001, J FOREST, V99, P26 THOMPSON MV, 1999, GLOB CHANGE BIOL, V5, P371 VOINOV A, 1999, ENVIRON MODELL SOFTW, V14, P473 WILSON DR, 1995, FIELD CROP RES, V43, P1 TC 0 BP 77 EP 84 PG 8 JI Comput. Electron. Agric. PY 2001 PD DEC VL 33 IS 1 GA 505VC PI OXFORD RP Gage SH Michigan State Univ, Dept Entomol, Computat Ecol & Visualizat Lab, E Lansing, MI 48824 USA J9 COMPUT ELECTRON AGRIC PA THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND UT ISI:000172935400006 ER PT Journal AU Chakravarty, S Shahar, Y TI Acquisition and analysis of repeating patterns in time-oriented clinical data SO METHODS OF INFORMATION IN MEDICINE LA English DT Article NR 17 SN 0026-1270 PU F K SCHATTAUER VERLAG GMBH C1 Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel Stanford Univ, Stanford, CA 94305 USA DE temporal reasoning; pattern matching; temporal abstraction; periodicity ID TEMPORAL ABSTRACTION AB Objectives: (1) Creation of an expressive language for specification of temporal patterns in clinical domains, (2) Development of a graphical knowledge-aquisition tool allowing expert physicians to define meaningful domain-specific patterns, (3) Implementation of on interpreter capable of detecting such patterns in clinical databases, and (4) Evaluation of the tools in the domains of diabetes and oncology. Methods. We describe a constraint-based language, named CAPSUL, for specification of temporal patterns. We implemented a knowledge-acquisition tool and a temporal-pattern interpreter within Resume, a larger temporal-abstraction architecture. We evaluated the knowledge-acquisition process with the help of domain experts. In collaboration with the Rush Presbyterain/St. Luke's Medical Center, we analyzed data of bone-marrow transplantation patients. The expert compared the detected patterns to a manual inspection of the data, with the help of on experimental information-visualization tool we are developing in a related project. Results: The CAPSUL language was expressive enough during the knowledge-acquisition process to capture almost all of the patterns that-the experts found useful. The patterns detected in the data by the pattern interpreter were all verified as correct. Completeness (whether all correct patterns were found) was difficult to assess, due to the size of the database. Conclusions: The CAPSUL language enables medical experts to express temporal patterns involving multiple-levels of abstraction of clinical data. The ability to reuse both domain-patterns and abstract constraints seems highly useful. The Resume interpreter, augmented by the CAPSUL semantics, finds the complex patterns within a clinical time- oriented database in a sound fashion. CR ALLEN JF, 1984, ARTIF INTELL, V23, P123 CHAKRAVARTY S, 1999, P 6 INT WORKSH TEMP, P29 CLIFFORD J, 1988, TEMPORAL ASPECTS INF, P17 CUKIERMAN D, 1998, P INT WORKSH TEMP RE, P140 CUKIERMAN D, 1996, P TIME 96, P80 KHATIB L, 1994, THESIS FLORIDA I TEC LADKIN P, 1996, P AAAI 86, P354 LADKIN P, 1986, P AAAI 86, P360 MORRIS R, 1998, P INT WORKSH TEMP RE, P74 MORRIS R, 1996, PERIODIC REPEATING E, P1 MUSEN MA, 1996, J AM MED INFORM ASSN, V3, P367 SHAH A, 1997, J ROY SOC MED, V90, P1 SHAHAR Y, 1998, ANN MATH ARTIF INTEL, V22, P159 SHAHAR Y, 1996, ARTIF INTELL MED, V8, P267 SHAHAR Y, IN PRESS J AM MED IN, V6, P99 SHAHAR Y, 1999, IN PRESS TOPICS HLTH TERENZIANI P, 1994, P INT WORKSH TEMP RE TC 0 BP 410 EP 420 PG 11 JI Methods Inf. Med. PY 2001 VL 40 IS 5 GA 501EJ PI STUTTGART RP Shahar Y Ben Gurion Univ Negev, Dept Informat Syst Engn, IL-84105 Beer Sheva, Israel J9 METHODS INFORM MED PA P O BOX 10 45 43, LENZHALDE 3, D-70040 STUTTGART, GERMANY UT ISI:000172654200009 ER PT Journal AU Handschin, E Leder, C TI Innovative visualization of the power system state for predictive process control SO ELECTRICAL ENGINEERING LA English DT Article NR 12 SN 0948-7921 PU SPRINGER-VERLAG C1 Univ Dortmund, Inst Elect Energy Syst, D-44221 Dortmund, Germany Univ Dortmund, Inst Elect Energy Syst, D-44221 Dortmund, Germany AB Stressed operating conditions and frequently changing generation patterns in today's power systems require new ways of information visualization in the control center. The paper introduces a user-oriented visualization concept, which presents the results of online security assessment and provides efficient control actions during critical situations. High- dimensional measurement sets are reduced using compact state indicators. The application of computational intelligence within the comprehensive information management system guarantees fast and human-focused information processing. CR CACCIABUE PC, 1998, MODELLING SIMULATION GERMOND AJ, 1909, AI TECHNIQUES APPL P GOMEZ A, 1993, IEEE T POWER SYSTEMS, V8, P937 HANDSCHIN E, 2001, IN PRESS IEEE POWER HAUSER AJ, 1999, IEEE POWER TECH 99 ILLIC M, 1998, ELECT POWER ENERGY S, V20, P99 JOHANNSEN G, 1993, MENSCH MASCHINE SYST KOHONEN TJ, 1995, SELF ORG MAPS LEMAITRE C, 1990, IEEE T POWER SYST, V5, P154 MAHADEV PM, 1993, IEEE T POWER SYST, V8, P1084 OVERBYE TJ, 2001, IEEE SPECTRUM, V38, P52 REHTANZ C, 2000, 8 EUR S ART NEU NETW, P401 TC 0 BP 297 EP 301 PG 5 JI Electr. Eng. PY 2001 PD NOV VL 83 IS 5-6 GA 498AJ PI NEW YORK RP Leder C Univ Dortmund, Inst Elect Energy Syst, D-44221 Dortmund, Germany J9 ELECTR ENG PA 175 FIFTH AVE, NEW YORK, NY 10010 USA UT ISI:000172487400011 ER PT Journal AU Newby, GB TI Empirical study of a 3D visualization for information retrieval tasks SO JOURNAL OF INTELLIGENT INFORMATION SYSTEMS LA English DT Article NR 34 SN 0925-9902 PU KLUWER ACADEMIC PUBL C1 Univ N Carolina, Sch Lib & Informat Sci, CB 3360 Manning Hall, Chapel Hill, NC 27599 USA Univ N Carolina, Sch Lib & Informat Sci, Chapel Hill, NC 27599 USA DE information visualization; information retrieval; visual interfaces; evaluation ID INDIVIDUAL-DIFFERENCES; USERS; SPACE AB There are many challenges to visualizing information including choosing between 2D and 3D interfaces, navigation and interaction methods, and selecting an appropriate level of detail. Visualizing information retrieval (IR) search results, including Web search engine results, poses additional challenges, notably the determination of appropriate relative locations for terms and document in a visual display. Latent Semantic Indexing (LSI) and related techniques offer approaches to visualizing relations among terms and documents. In this work, "information space" is presented as a framework for discussing relations among terms and documents, and a technique related to LSI is utilized to generate information spaces from IR search results. This paper provides an overview of more than three decades of work on information visualization, identifying several trends and some relatively unexplored areas. An experimental evaluation of a prototype interface for visualizing IR results is described. Results indicate that the 3D navigation interface for IR search results was usable, but that subjects had difficulty with some aspects. Further study and development of 2D and 3D methods for interacting with retrieval search results is suggested. 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Intell. Inf. Syst. PY 2001 VL 18 IS 1 GA 495YF PI DORDRECHT RP Newby GB Univ N Carolina, Sch Lib & Informat Sci, CB 3360 Manning Hall, Chapel Hill, NC 27599 USA J9 J INTELL INF SYST PA SPUIBOULEVARD 50, PO BOX 17, 3300 AA DORDRECHT, NETHERLANDS UT ISI:000172367400003 ER PT Journal AU Cruz, IF Leveille, PS TI As you like it: Personalized database visualization using a visual language SO JOURNAL OF VISUAL LANGUAGES AND COMPUTING LA English DT Article NR 56 SN 1045-926X PU ACADEMIC PRESS LTD C1 Univ Illinois, Dept Comp Sci, 851 S Morgan M-C 152, Chicago, IL 60607 USA Univ Illinois, Dept Comp Sci, Chicago, IL 60607 USA Mitre Corp, Bedford, MA 01730 USA DE Visual Query Languages; information visualization; personalization; constraint solving AB The Delaunay system supports a visual language that is specifically geared to the querying and visualization of databases. In this paper, we concentrate on the information visualization capabilities of the system. A distinctive feature of Delaunay is its full personalization capabilities: users can define their visualizations from scratch without limiting themselves to pre-defined visualization modes. Fine customization of the visualization is achieved by the availability of a visual alphabet of atomic graphical symbols and by the expressive power of the visual query language, which supports recursion. We describe the key components of the Delaunay system, namely its interface modules, which support advanced visualization techniques and principles, and its efficient constraint solver. The successful implementation of Delaunay demonstrates the feasibility of a powerful database system with which users can effectively specify a wide variety of visualizations supporting data and visualization exploration for different kinds of applications including graph visualization and business analysis. (C) 2001 Academic Press. 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Vis. Lang. Comput. PY 2001 PD OCT VL 12 IS 5 GA 491CC PI LONDON RP Cruz IF Univ Illinois, Dept Comp Sci, 851 S Morgan M-C 152, Chicago, IL 60607 USA J9 J VISUAL LANG COMPUTING PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND UT ISI:000172089600004 ER PT Journal AU Woodruff, A Olston, C Aiken, A Chu, M Ercegovac, V Lin, M Spalding, M Stonebraker, M TI DataSplash: A direct manipulation environment for programming semantic zoom visualizations of tabular data SO JOURNAL OF VISUAL LANGUAGES AND COMPUTING LA English DT Article NR 32 SN 1045-926X PU ACADEMIC PRESS LTD C1 Xerox Corp, Palo Alto Res Ctr, 3333 Coyote Hill Rd, Palo Alto, CA 94304 USA Univ Calif Berkeley, Berkeley, CA 94720 USA DE database visualization; direct manipulation; information visualization; semantic zoom AB We describe DataSplash, a direct manipulation system for creating semantic zoom visualizations of tabular (relational) data. DataSplash makes contributions in three areas that are key to the construction of such visualizations. Firsts DataSplash helps users graphically specify the visual appearance of groups of objects. Second, the system helps users visually program the way the appearance of groups of objects changes as users browse the visualization. Third, DataSplash allows users to create groups of graphical links between canvases. These direct manipulation facilities simplify the process of constructing semantic zoom applications, particularly ones that display large data sets. (C) 2001 Academic Press. 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Vis. Lang. Comput. PY 2001 PD OCT VL 12 IS 5 GA 491CC PI LONDON RP Woodruff A Xerox Corp, Palo Alto Res Ctr, 3333 Coyote Hill Rd, Palo Alto, CA 94304 USA J9 J VISUAL LANG COMPUTING PA 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND UT ISI:000172089600005 ER PT Journal AU Fraga, ES Patel, R Rowe, GWA TI A visual representation of process heat exchange as a basis for user interaction and stochastic optimization SO CHEMICAL ENGINEERING RESEARCH & DESIGN LA English DT Article NR 16 SN 0263-8762 PU INST CHEMICAL ENGINEERS C1 Univ London Univ Coll, Dept Chem Engn, Torrington Pl, London WC1E 7JE, England Univ London Univ Coll, Dept Chem Engn, London WC1E 7JE, England Univ Dundee, Dept Appl Comp, Dundee, Scotland DE heat exchanger network synthesis; visualization; optimization; simulated annealing; graphical user interface ID INTEGRATED DISTILLATION SEQUENCES; NETWORKS; SYSTEMS AB The use of visualization for heat exchanger network synthesis, both grass-roots and retrofit, has a long history. Visualization is appealing to the user because it helps gain insight into the underlying problem. When coupled with user interaction, this insight generation can be enhanced. Furthermore, when the interaction includes access to an optimization procedure, the result is a tool that is appealing and has the potential to generate good results. This paper describes the implementation of an interactive visualization procedure for heat integrated process design. The visualization includes information about the underlying process responsible for the heating and cooling requirements which define the heat exchanger network synthesis problem. Furthermore, the data structures which underpin the visualization method provide a natural problem formulation for simulated annealing as an optimizer for the heat exchanger network structure. The combination of visualization, user interaction, and stochastic optimization results in a robust and easy to use tool for heat integrated process design. 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PY 2001 PD OCT VL 79 IS A7 GA 493GW PI RUGBY RP Fraga ES Univ London Univ Coll, Dept Chem Engn, Torrington Pl, London WC1E 7JE, England J9 CHEM ENG RES DES PA 165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3BR, ENGLAND UT ISI:000172215000008 ER PT Journal AU Eagle, S TI Information visualization SO JOURNAL OF LIBRARIANSHIP AND INFORMATION SCIENCE LA English DT Book Review NR 1 SN 0961-0006 PU BOWKER-SAUR CR SPENCE R, 2001, INFORMATION VISUALIZ TC 0 BP 163 EP 163 PG 1 JI J. Libr. Inf. Sci. PY 2001 PD SEP VL 33 IS 3 GA 476MB PI E GRINSTEAD J9 J LIBR INF SCI PA WINDSOR COURT, EAST GRINSTEAD HOUSE, E GRINSTEAD RH19 1XA, W SUSSEX, ENGLAND UT ISI:000171229900011 ER PT Journal AU Cole, C Mandelblatt, B Stevenson, J TI Visualizing a high recall search strategy output for undergraduates in an exploration stage of researching a term paper SO INFORMATION PROCESSING & MANAGEMENT LA English DT Article NR 63 SN 0306-4573 PU PERGAMON-ELSEVIER SCIENCE LTD C1 McGill Univ, Grad Sch Lib & Informat Studies, 3459 McTavish St, Montreal, PQ H3A 1Y1, Canada McGill Univ, Grad Sch Lib & Informat Studies, Montreal, PQ H3A 1Y1, Canada Concordia Univ, Montreal, PQ H4B 1R6, Canada DE cartographic maps; information retrieval; information visualization; information s