Effective visualization of temporal ensembles


Journal article


L. Hao, C. G. Healey, S. Bass
IEEE Transactions on Visualization and Computer Graphics, vol. 22(1), 2016, pp. 787-796

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APA
Hao, L., Healey, C. G., & Bass, S. (2016). Effective visualization of temporal ensembles. IEEE Transactions on Visualization and Computer Graphics, 22(1), 787–796.

Chicago/Turabian
Hao, L., C. G. Healey, and S. Bass. “Effective Visualization of Temporal Ensembles.” IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (2016): 787–796.

MLA
Hao, L., et al. “Effective Visualization of Temporal Ensembles.” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 1, 2016, pp. 787–96.


Abstract

An ensemble is a collection of related datasets, called members, built from a series of runs of a simulation or an experiment. Ensembles are large, temporal, multidimensional, and multivariate, making them difficult to analyze. Another important challenge is visualizing ensembles that vary both in space and time. Initial visualization techniques displayed ensembles with a small number of members, or presented an overview of an entire ensemble, but without potentially important details. Recently, researchers have suggested combining these two directions, allowing users to choose subsets of members to visualization. This manual selection process places the burden on the user to identify which members to explore. We first introduce a static ensemble visualization system that automatically helps users locate interesting subsets of members to visualize. We next extend the system to support analysis and visualization of temporal ensembles. We employ 3D shape comparison, cluster tree visualization, and glyph based visualization to represent different levels of detail within an ensemble. This strategy is used to provide two approaches for temporal ensemble analysis: (1) segment based ensemble analysis, to capture important shape transition time-steps, clusters groups of similar members, and identify common shape changes over time across multiple members; and (2) time-step based ensemble analysis, which assumes ensemble members are aligned in time by combining similar shapes at common time-steps. Both approaches enable users to interactively visualize and analyze a temporal ensemble from different perspectives at different levels of detail. We demonstrate our techniques on an ensemble studying matter transition from hadronic gas to quark-gluon plasma during gold-on-gold particle collisions.


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