Region-adaptive, Error-controlled Scientific Data Compression using Multilevel Decomposition-Reference-Cited by-同舟云学术

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Region-adaptive, Error-controlled Scientific Data Compression using Multilevel Decomposition

Published:2022-07-06 Issue: Volume: Page:
ISSN:
Container-title:34th International Conference on Scientific and Statistical Database Management
language:
Short-container-title:

Author:

Gong Qian¹,Whitney Ben²,Zhang Chengzhu³,Liang Xin⁴,Rangarajan Anand⁵,Chen Jieyang²,Wan Lipeng²,Ullrich Paul⁶,Liu Qing⁷,Jacob Robert⁸,Ranka Sanjay⁵,Klasky Scott²

Affiliation:

1. Computer Science and Mathematics Division, Oak Ridge National Laboratory, United States

2. Oak Ridge National Laboratory, United States

3. Lawrence Livermore National Laboratory, United States

4. Missouri Science & Technology, United States

5. University of Florida, United States

6. University of California, Davis, United States

7. New Jersey Institute of Technology, United States

8. Argonne National Laboratory, United States

Funder

Department of Energy ASCR

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3538712.3538717

Reference42 articles.

1. Automatic subspace clustering of high dimensional data for data mining applications

2. Multilevel techniques for compression and reduction of scientific data—the univariate case

3. Multilevel Techniques for Compression and Reduction of Scientific Data-Quantitative Control of Accuracy in Derived Quantities

4. Multilevel Techniques for Compression and Reduction of Scientific Data---The Multivariate Case

5. Multilevel Techniques for Compression and Reduction of Scientific Data---The Unstructured Case

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1. Hybrid Approaches for Data Reduction of Spatiotemporal Scientific Applications;2024 Data Compression Conference (DCC);2024-03-19

2. Spatiotemporally Adaptive Compression for Scientific Dataset with Feature Preservation – A Case Study on Simulation Data with Extreme Climate Events Analysis;2023 IEEE 19th International Conference on e-Science (e-Science);2023-10-09

3. A General Framework for Progressive Data Compression and Retrieval;IEEE Transactions on Visualization and Computer Graphics;2023

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