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.
|Title of host publication
|IEEE Symposium on Large Data Analysis and Visualization (LDAV)
|Institute of Electrical and Electronics Engineers (IEEE)
|Number of pages
|Published - Oct 2012
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: 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).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.