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Homogeneity Guided Probabilistic Data Summaries for Analysis and Visualization of Large-Scale Data Sets

• Soumya Dutta dutta.33 (at) osu.edu
  The Ohio State University
• Jonathan Woodring woodring (at) lanl.gov
  Los Alamos National Laboratory
• Han-Wei Shen shen.94 (at) osu.edu
  The Ohio State University
• Jen-Ping Chen chen.1210 (at) osu.edu
  The Ohio State University
• James Ahrens ahrens (at) lanl.gov
  Los Alamos National Laboratory

Abstract

High-resolution simulation data sets provide plethora of information, which needs to be explored by application scientists to gain enhanced understanding about various phenomena. Visual-analytics techniques using raw data sets are often expensive due to the data sets' extreme sizes. But, interactive analysis and visualization is crucial for big data analytics, because scientists can then focus on the important data and make critical decisions quickly. To assist efficient exploration and visualization, we propose a new region-based statistical data summarization scheme. Our method is superior in quality, as compared to the existing statistical summarization techniques, with a more compact representation, reducing the overall storage cost. The quantitative and visual efficacy of our proposed method is demonstrated using several data sets along with an in situ application study for an extreme-scale flow simulation.