Interactive exploration of large-scale time-varying data using dynamic tracking graphs

Handle URI:
http://hdl.handle.net/10754/598646
Title:
Interactive exploration of large-scale time-varying data using dynamic tracking graphs
Authors:
Widanagamaachchi, W.; Christensen, C.; Bremer, P.-T; Pascucci, Valerio
Abstract:
Exploring and analyzing the temporal evolution of features in large-scale time-varying datasets is a common problem in many areas of science and engineering. One natural representation of such data is tracking graphs, i.e., constrained graph layouts that use one spatial dimension to indicate time and show the "tracks" of each feature as it evolves, merges or disappears. However, for practical data sets creating the corresponding optimal graph layouts that minimize the number of intersections can take hours to compute with existing techniques. Furthermore, the resulting graphs are often unmanageably large and complex even with an ideal layout. Finally, due to the cost of the layout, changing the feature definition, e.g. by changing an iso-value, or analyzing properly adjusted sub-graphs is infeasible. To address these challenges, this paper presents a new framework that couples hierarchical feature definitions with progressive graph layout algorithms to provide an interactive exploration of dynamically constructed tracking graphs. Our system enables users to change feature definitions on-the-fly and filter features using arbitrary attributes while providing an interactive view of the resulting tracking graphs. Furthermore, the graph display is integrated into a linked view system that provides a traditional 3D view of the current set of features and allows a cross-linked selection to enable a fully flexible spatio-temporal exploration of data. We demonstrate the utility of our approach with several large-scale scientific simulations from combustion science. © 2012 IEEE.
Citation:
Widanagamaachchi W, Christensen C, Bremer P-T, Pascucci V (2012) Interactive exploration of large-scale time-varying data using dynamic tracking graphs. IEEE Symposium on Large Data Analysis and Visualization (LDAV). Available: http://dx.doi.org/10.1109/ldav.2012.6378962.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Symposium on Large Data Analysis and Visualization (LDAV)
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
Oct-2012
DOI:
10.1109/ldav.2012.6378962
Type:
Conference Paper
Sponsors:
This work is supported in part by NSF awards IIS-1045032, OCI-0904631, OCI-0906379 and CCF-0702817, and by a KAUST award KUS-C1-016-04. This work was also performed under the auspices of the U.S. Department of Energy by the University of Utah under contracts DE-SCOOO1922, DE-AC52-07NA27344 and DE-FC02-06ER25781, and by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 (LLNL-PROC-577473).
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Full metadata record

DC FieldValue Language
dc.contributor.authorWidanagamaachchi, W.en
dc.contributor.authorChristensen, C.en
dc.contributor.authorBremer, P.-Ten
dc.contributor.authorPascucci, Valerioen
dc.date.accessioned2016-02-25T13:33:44Zen
dc.date.available2016-02-25T13:33:44Zen
dc.date.issued2012-10en
dc.identifier.citationWidanagamaachchi W, Christensen C, Bremer P-T, Pascucci V (2012) Interactive exploration of large-scale time-varying data using dynamic tracking graphs. IEEE Symposium on Large Data Analysis and Visualization (LDAV). Available: http://dx.doi.org/10.1109/ldav.2012.6378962.en
dc.identifier.doi10.1109/ldav.2012.6378962en
dc.identifier.urihttp://hdl.handle.net/10754/598646en
dc.description.abstractExploring and analyzing the temporal evolution of features in large-scale time-varying datasets is a common problem in many areas of science and engineering. One natural representation of such data is tracking graphs, i.e., constrained graph layouts that use one spatial dimension to indicate time and show the "tracks" of each feature as it evolves, merges or disappears. However, for practical data sets creating the corresponding optimal graph layouts that minimize the number of intersections can take hours to compute with existing techniques. Furthermore, the resulting graphs are often unmanageably large and complex even with an ideal layout. Finally, due to the cost of the layout, changing the feature definition, e.g. by changing an iso-value, or analyzing properly adjusted sub-graphs is infeasible. To address these challenges, this paper presents a new framework that couples hierarchical feature definitions with progressive graph layout algorithms to provide an interactive exploration of dynamically constructed tracking graphs. Our system enables users to change feature definitions on-the-fly and filter features using arbitrary attributes while providing an interactive view of the resulting tracking graphs. Furthermore, the graph display is integrated into a linked view system that provides a traditional 3D view of the current set of features and allows a cross-linked selection to enable a fully flexible spatio-temporal exploration of data. We demonstrate the utility of our approach with several large-scale scientific simulations from combustion science. © 2012 IEEE.en
dc.description.sponsorshipThis work is supported in part by NSF awards IIS-1045032, OCI-0904631, OCI-0906379 and CCF-0702817, and by a KAUST award KUS-C1-016-04. This work was also performed under the auspices of the U.S. Department of Energy by the University of Utah under contracts DE-SCOOO1922, DE-AC52-07NA27344 and DE-FC02-06ER25781, and by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344 (LLNL-PROC-577473).en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectFeature Detection and Trackingen
dc.subjectParallel Coordinatesen
dc.subjectTime-Varying Dataen
dc.subjectTopology-based Techniquesen
dc.subjectVisualization in Physical Sciences and Engineeringen
dc.titleInteractive exploration of large-scale time-varying data using dynamic tracking graphsen
dc.typeConference Paperen
dc.identifier.journalIEEE Symposium on Large Data Analysis and Visualization (LDAV)en
dc.contributor.institutionUniversity of Utah, Salt Lake City, United Statesen
dc.contributor.institutionLawrence Livermore National Laboratory, Livermore, United Statesen
kaust.grant.numberKUS-C1-016-04en
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