: Our framework provides a natural and interactive exploration of tracking graphs (middle), combined with a traditional 3D view of features (left), and allows a cross-linked selection among them to enable a full spatio-temporal exploration of data. The selected feature is displayed in 'red' in the 3D feature viewer while the relevant subtree in the tracking graph is highlighted in the other viewer. The images displayed here use the H_Control_none dataset. Exploring and analyzing the temporal evolution of features in largescale 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 cur-*e-mail: wathsy@sci.utah.edu t e-mail: cam@sci.utah.edu :j:e-mail: bremer5 @llnl. gOY § e-mail: pascucci@sci.utah.edu October 14 -15, Seattle, Washington, USA U.S. Government work not protected by U.S. copyright rent 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.
Widanagamaachchi et al. REF present tracking graphs that update quickly when the user changes the isovalue of the tracked features.
Interactive exploration of large-scale time-varying data using dynamic tracking graphs
IEEE Symposium on Large Data Analysis and Visualization (LDAV)
IEEE Symposium on Large Data Analysis and Visualization (LDAV)
Computer Science