Statistical visualization and analysis of large data using a value-based spatial distribution

Ko Chih Wang, Kewei Lu, Tzu Hsuan Wei, Naeem Shareef, Han Wei Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Citations (Scopus)

Abstract

The size of large-scale scientific datasets created from simulations and computed on modern supercomputers continues to grow at a fast pace. A daunting challenge is to analyze and visualize these intractable datasets on commodity hardware. A recent and promising area of research is to replace the dataset with a distribution based proxy representation that summarizes scalar information into a much reduced memory footprint. Proposed representations subdivide the dataset into local blocks, where each block holds important statistical information, such as a histogram. A key drawback is that a distribution representing the scalar values in a block lacks spatial information. This manifests itself as large errors in visualization algorithms. We present a novel statistically-based representation by augmenting the block-wise distribution based representation with location information, called a value-based spatial distribution. Information from both spatial and scalar spaces are combined using Bayes' rule to accurately estimate the data value at a given spatial location. The representation is compact using the Gaussian Mixture Model. We show that our approach is able to preserve important features in the data and alleviate uncertainty.

Original languageEnglish
Title of host publication2017 IEEE Pacific Visualization Symposium, PacificVis 2017 - Proceedings
EditorsYingcai Wu, Daniel Weiskopf, Tim Dwyer
PublisherIEEE Computer Society
Pages161-170
Number of pages10
ISBN (Electronic)9781509057382
DOIs
Publication statusPublished - 2017 Sep 11
Externally publishedYes
Event10th IEEE Pacific Visualization Symposium, PacificVis 2017 - Seoul, Korea, Republic of
Duration: 2017 Apr 182017 Apr 21

Publication series

NameIEEE Pacific Visualization Symposium
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773

Conference

Conference10th IEEE Pacific Visualization Symposium, PacificVis 2017
Country/TerritoryKorea, Republic of
CitySeoul
Period2017/04/182017/04/21

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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