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Query Log Compression for Workload Analytics

Summary: LogR compresses massive SQL logs as interpretable mixtures of co-occurring structural patterns, preserving aggregate workload statistics beyond sampling. A tunable space–fidelity trade-off, efficient construction, and information-theoretic guarantees yield stronger summaries than prior methods. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
12065
Venue
VLDB
Year
2019
Pagerank
5.4079618e-05
Overall Rank
8,583 | 41.12%
DOI
10.14778/3291264.3291265

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{xie_vldb19,
        title = {{Query Log Compression for Workload Analytics}},
        author = {Xie, Ting and Chandola, Varun and Kennedy, Oliver},
        journal = {PVLDB},
        series = {{VLDB} '19},
        volume = {12},
        number = {3},
        pages = {183--196},
        doi = {10.14778/3291264.3291265},
        url = {https://doi.org/10.14778/3291264.3291265},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

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Rank Citing Paper Year Venue Pagerank
4,084 Comprehensive and Efficient Workload Compression 2021 VLDB 6.9151691e-05
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Outgoing Citations (Sorted by Pagerank)

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

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