Improving I/O Performance with Adaptive Data Compression for Big Data Applications

Author:

Zou Hongbo,Yu Yongen,Tang Wei,Chen Hsuanwei Michelle

Publisher

IEEE

Cited by 21 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Data reduction in big data: a survey of methods, challenges and future directions;International Journal of Data Science and Analytics;2024-07-10

2. TizenRT OS Based Data Compression Algorithms for IoT Devices;2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS);2023-11-03

3. FORTE: an extensible framework for robustness and efficiency in data transfer pipelines;Proceedings of the 17th ACM International Conference on Distributed and Event-based Systems;2023-06-27

4. Unbalanced Parallel I/O: An Often-Neglected Side Effect of Lossy Scientific Data Compression;2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7);2021-11

5. Parallel I/O on Compressed Data Files: Semantics, Algorithms, and Performance Evaluation;2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID);2020-05

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