Exploring ensemble visualization


Conference


M. Phadke, L. Pinto, O. S. Alabi, J. Harter, R. M. Taylor, X.Wu, H.Petersen, S. Bass, C. G. Healey
Proceedings Visualization and Data Analysis (VDA '12), paper 0B, 8294, 2012, pp. 1-12

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APA   Click to copy
Phadke, M., Pinto, L., Alabi, O. S., Harter, J., Taylor, R. M., X.Wu, … Healey, C. G. (2012). Exploring ensemble visualization (Vol. paper 0B, 8294, pp. 1–12).


Chicago/Turabian   Click to copy
Phadke, M., L. Pinto, O. S. Alabi, J. Harter, R. M. Taylor, X.Wu, H.Petersen, S. Bass, and C. G. Healey. “Exploring Ensemble Visualization.” In , paper 0B, 8294:1–12, 2012.


MLA   Click to copy
Phadke, M., et al. Exploring Ensemble Visualization. Vol. paper 0B, 8294, 2012, pp. 1–12.


BibTeX   Click to copy

@conference{m2012a,
  title = {Exploring ensemble visualization},
  year = {2012},
  edition = {},
  journal = {},
  pages = {1-12},
  series = {},
  volume = {paper 0B, 8294},
  author = {Phadke, M. and Pinto, L. and Alabi, O. S. and Harter, J. and Taylor, R. M. and X.Wu and H.Petersen and Bass, S. and Healey, C. G.},
  booktitle = {Proceedings Visualization and Data Analysis (VDA '12)}
}

Abstract

An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation. To draw inferences from an ensemble, scientists need to compare data both within and between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of ensemble data.


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