<?xml version="1.0" encoding="ISO-8859-1"?>
<metadatalist>
	<metadata ReferenceType="Conference Proceedings">
		<site>sibgrapi.sid.inpe.br 802</site>
		<identifier>8JMKD3MGPBW34M/3EEG6F8</identifier>
		<repository>sid.inpe.br/sibgrapi/2013/07.10.23.25</repository>
		<lastupdate>2013:07.10.23.25.12 sid.inpe.br/banon/2001/03.30.15.38 cjd.wong@gmail.com</lastupdate>
		<metadatarepository>sid.inpe.br/sibgrapi/2013/07.10.23.25.12</metadatarepository>
		<metadatalastupdate>2020:02.19.03.09.22 sid.inpe.br/banon/2001/03.30.15.38 administrator {D 2013}</metadatalastupdate>
		<citationkey>WongOlivMing:2013:MuPrEx</citationkey>
		<title>Multidimensional projections to explore time-varying multivariate volume data</title>
		<format>On-line.</format>
		<year>2013</year>
		<date>Aug. 5-8, 2013</date>
		<numberoffiles>1</numberoffiles>
		<size>1666 KiB</size>
		<author>Wong, Christian,</author>
		<author>Oliveira, Maria Cristina F.,</author>
		<author>Minghim, Rosane,</author>
		<affiliation>Instituto de Ciencias Matemâticas e de Computação</affiliation>
		<affiliation>Instituto de Ciencias Matemâticas e de Computação</affiliation>
		<affiliation>Instituto de Ciencias Matemâticas e de Computação</affiliation>
		<editor>Boyer, Kim,</editor>
		<editor>Hirata, Nina,</editor>
		<editor>Nedel, Luciana,</editor>
		<editor>Silva, Claudio,</editor>
		<e-mailaddress>cjd.wong@gmail.com</e-mailaddress>
		<conferencename>Conference on Graphics, Patterns and Images, 26 (SIBGRAPI)</conferencename>
		<conferencelocation>Arequipa, Peru</conferencelocation>
		<booktitle>Proceedings</booktitle>
		<publisher>IEEE Computer Society</publisher>
		<publisheraddress>Los Alamitos</publisheraddress>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
		<tertiarytype>Full Paper</tertiarytype>
		<keywords>Visualization, scientific visualization, multidimensional projections, exploratory volume visualization.</keywords>
		<abstract>Multidimensional Projections (MPs) have become popular as visual data analysis tools in several application domains, including Scientific Visualization. Current techniques are fast, precise and capable of handling local and global data features, having successfully supported spatial and abstract data visualizations. However, two major shortcomings hinder their application for exploratory analysis of time-varying multivariate volumetric data. Current techniques lack visual coherence when applied to data collected across consecutive time stamps and offer little support to investigating attribute-specific questions. Both are relevant properties when analysing time varying volumes. In this paper we revisit projection methods from this perspective and introduce modifications into two existing high-performance techniques to ensure temporal coherence. We also propose a hybrid visualization strategy that can assist users investigating the role of a specific attribute on data behavior through time. We illustrate how our approaches enhance projection-based visual exploration of time-varying multivariate volume data with their application to data sets from three distinct simulations, made available for editions of the IEEE Visualization Contest.</abstract>
		<language>en</language>
		<targetfile>paper114545_camera-ready.pdf</targetfile>
		<usergroup>cjd.wong@gmail.com</usergroup>
		<visibility>shown</visibility>
		<mirrorrepository>sid.inpe.br/banon/2001/03.30.15.38.24</mirrorrepository>
		<hostcollection>sid.inpe.br/banon/2001/03.30.15.38</hostcollection>
		<agreement>agreement.html .htaccess .htaccess2</agreement>
		<lasthostcollection>sid.inpe.br/banon/2001/03.30.15.38</lasthostcollection>
		<url>http://sibgrapi.sid.inpe.br/rep-/sid.inpe.br/sibgrapi/2013/07.10.23.25</url>
	</metadata>
</metadatalist>