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

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Title: Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization
Authors: Yang Yang, Kecheng Lu, Yu Wu, Yunhai Wang, Yi Cao
Source: Computational Visual Media, Vol 9, Iss 3, Pp 513-529 (2023)
Publisher Information: SpringerOpen, 2023.
Publication Year: 2023
Collection: LCC:Electronic computers. Computer science
Subject Terms: correlation-awareness, large-scale data, multi-block methods, probabilistic data summarization, Electronic computers. Computer science, QA75.5-76.95
More Details: 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 statistical properties 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-art methods while providing a higher reconstruction accuracy at a lower computational cost.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2096-0433
2096-0662
Relation: https://doaj.org/toc/2096-0433; https://doaj.org/toc/2096-0662
DOI: 10.1007/s41095-022-0304-6
Access URL: https://doaj.org/article/e500bb84340944aca028768f64690009
Accession Number: edsdoj.500bb84340944aca028768f64690009
Database: Directory of Open Access Journals
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  Data: Computational Visual Media, Vol 9, Iss 3, Pp 513-529 (2023)
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  Data: 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 statistical properties 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-art methods while providing a higher reconstruction accuracy at a lower computational cost.
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      – SubjectFull: large-scale data
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      – SubjectFull: multi-block methods
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