TY - GEN

T1 - Statistical visualization and analysis of large data using a value-based spatial distribution

AU - Wang, Ko Chih

AU - Lu, Kewei

AU - Wei, Tzu Hsuan

AU - Shareef, Naeem

AU - Shen, Han Wei

N1 - Publisher Copyright:
© 2017 IEEE.

PY - 2017/9/11

Y1 - 2017/9/11

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85032037729&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032037729&partnerID=8YFLogxK

U2 - 10.1109/PACIFICVIS.2017.8031590

DO - 10.1109/PACIFICVIS.2017.8031590

M3 - Conference contribution

AN - SCOPUS:85032037729

T3 - IEEE Pacific Visualization Symposium

SP - 161

EP - 170

BT - 2017 IEEE Pacific Visualization Symposium, PacificVis 2017 - Proceedings

A2 - Wu, Yingcai

A2 - Weiskopf, Daniel

A2 - Dwyer, Tim

PB - IEEE Computer Society

T2 - 10th IEEE Pacific Visualization Symposium, PacificVis 2017

Y2 - 18 April 2017 through 21 April 2017

ER -