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2017 IEEE Pacific Visualization Symposium (PacificVis) (2017)
Seoul, South Korea
April 18, 2017 to April 21, 2017
ISSN: 2165-8773
ISBN: 978-1-5090-5739-9
pp: 111-120
Soumya Dutta , The Ohio State University, United States of America
Jonathan Woodring , Los Alamos National Laboratory, United States of America
Han-Wei Shen , The Ohio State University, United States of America
Jen-Ping Chen , The Ohio State University, United States of America
James Ahrens , Los Alamos National Laboratory, United States of America
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.
INDEX TERMS
Data visualization, Data models, Computational modeling, Uncertainty, Histograms, Probabilistic logic, Entropy
CITATION
Soumya Dutta, Jonathan Woodring, Han-Wei Shen, Jen-Ping Chen, James Ahrens, "Homogeneity guided probabilistic data summaries for analysis and visualization of large-scale data sets", 2017 IEEE Pacific Visualization Symposium (PacificVis), vol. 00, no. , pp. 111-120, 2017, doi:10.1109/PACIFICVIS.2017.8031585
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