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Computational Visual Media 2023, 9(3): 513-529 https://doi.org/10.1007/s41095-022-0304-6
Research Article | Open Access | Issue | Published: 18 March 2023

Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization

Show Author's Information Yang Yang1,  Kecheng Lu2,  Yu Wu1,  Yunhai Wang2,  Yi Cao1( )
Institute of Applied Physics and ComputationalMathematics, Beijing 100094, China
School of Computer Science and Technology, Shandong University, Qingdao 266237, China
Keywords:
correlation-awareness, large-scale data, multi-block methods, probabilistic data summarization
Cite this article:
Yang Y, Lu K, Wu Y, et al. Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization. Computational Visual Media, 2023, 9(3): 513-529. https://doi.org/10.1007/s41095-022-0304-6
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Abstract Full text About this article

In this paper, we propose a correlation-aware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific simulations. The core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information, spatial location, and correlation distribution using Bayes’ rule. This effectively preserves statisticalproperties without merging data blocks in different parallel computing nodes and repartitioning them, thus significantly reducing the computational cost. Furthermore, this enables reconstruction of the original data more accurately than existing methods. We demonstrate the effectiveness of our technique using six datasets, with the largest having one billion grid points. The experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-artmethods while providing a higher reconstruction accuracy at a lower computational cost.


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About this article

Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization

Show Author's information Yang Yang1, Kecheng Lu2, Yu Wu1, Yunhai Wang2, Yi Cao1( )
Institute of Applied Physics and ComputationalMathematics, Beijing 100094, China
School of Computer Science and Technology, Shandong University, Qingdao 266237, China

Abstract

In this paper, we propose a correlation-aware probabilistic data summarization technique to efficiently analyze and visualize large-scale multi-block volume data generated by massively parallel scientific simulations. The core of our technique is correlation modeling of distribution representations of adjacent data blocks using copula functions and accurate data value estimation by combining numerical information, spatial location, and correlation distribution using Bayes’ rule. This effectively preserves statisticalproperties without merging data blocks in different parallel computing nodes and repartitioning them, thus significantly reducing the computational cost. Furthermore, this enables reconstruction of the original data more accurately than existing methods. We demonstrate the effectiveness of our technique using six datasets, with the largest having one billion grid points. The experimental results show that our approach reduces the data storage cost by approximately one order of magnitude compared to state-of-the-artmethods while providing a higher reconstruction accuracy at a lower computational cost.

Keywords: correlation-awareness, large-scale data, multi-block methods, probabilistic data summarization

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Publication history

Received: 07 March 2022
Accepted: 01 July 2022
Published: 18 March 2023
Issue date: September 2023

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© The Author(s) 2023.

Acknowledgements

This work was supported by the Chinese Postdoctoral Science Foundation (2021M700016).

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