Academic Journal
Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualization
| 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 |
| ISSN: | 20960433 20960662 |
|---|---|
| DOI: | 10.1007/s41095-022-0304-6 |
| Published in: | Computational Visual Media |
| Language: | English |