2017 IEEE Pacific Visualization Symposium (PacificVis) (2017)
Seoul, South Korea
April 18, 2017 to April 21, 2017
Ko-Chih Wang , The Ohio State University, United States of America
Kewei Lu , The Ohio State University, United States of America
Tzu-Hsuan Wei , The Ohio State University, United States of America
Naeem Shareef , The Ohio State University, United States of America
Han-Wei Shen , The Ohio State University, United States of America
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.
Graphical models, Distribution functions, Histograms, Isosurfaces, Computational modeling, Rendering (computer graphics)
K. Wang, Kewei Lu, T. Wei, N. Shareef and H. Shen, "Statistical visualization and analysis of large data using a value-based spatial distribution," 2017 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Seoul, South Korea, 2017, pp. 161-170.