Adaptive Log Compression for Massive Log Data
Summary: Adaptive log compression for massive log data, with data-driven tuning to exploit log-specific redundancies. Delivers ~30% better compression ratios vs. state-of-the-art, demonstrating scalable performance for high-volume log processing. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Robert Christensen (University of Utah)
- 2. Feifei Li (University of Utah)
BibTeX Citation
@inproceedings{christensen_sigmod13,
title = {{Adaptive Log Compression for Massive Log Data}},
author = {Christensen, Robert and Li, Feifei},
series = {{SIGMOD} '13},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2463676.2465341},
url = {https://dl.acm.org/doi/10.1145/2463676.2465341},
year = {2013}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,673 | High-Ratio Compression for Machine-Generated Data | 2023 | SIGMOD | 5.2383683e-05 |
| 10,909 | LLMLog: Advanced Log Template Generation via LLM-driven Multi-Round Annotation | 2025 | 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 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 689 | On Synopses for Distinct-Value Estimation Under Multiset Operations | 2007 | SIGMOD | 0.00014940023 |
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