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High-Ratio Compression for Machine-Generated Data

Summary: Pattern-Based Compression (PBC) exploits patterns in machine-generated data to achieve Pareto-optimal compression. Per-record encoding enables fast random access, delivering ~2× ratios over state-of-the-art with production-ready deployment in DB systems. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6809
Venue
SIGMOD
Year
2023
Pagerank
5.2383683e-05
Overall Rank
9,673 | 33.64%
DOI
10.1145/3626732

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{zhang_sigmod23,
        title = {{High-Ratio Compression for Machine-Generated Data}},
        author = {Zhang, Jiujing and Shen, Zhitao and Yang, Shiyu and Meng, Lingkai and Xiao, Chuan and Jia, Wei and Li, Yue and Sun, Qinhui and Zhang, Wenjie and Lin, Xuemin},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3626732},
        url = {https://dl.acm.org/doi/10.1145/3626732},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
9,038 The FastLanes File Format 2025 VLDB 5.3270943e-05
10,589 Morphing-based Compression for Data-centric ML Pipelines 2026 VLDB 5.093636e-05
10,946 LogLite: Lightweight Plug-and-Play Streaming Log Compression 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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