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Self-tuning Histograms: Building Histograms Without Looking at Data

Summary: Self-tuning histograms infer distributions from query-execution feedback, not data samples, and refine via range-selectivity observations. Data-size independent, cheaper than multi-dimensional histograms; effective for low–moderate skew, with initialization/refinement techniques and experimental validation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hdcfd44ae6c3d1545
Venue
SIGMOD
Year
1999
Pagerank
0.00017962189
Overall Rank
454 | 96.95%
DOI
10.1145/304182.304198

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{aboulnaga_sigmod99,
        title = {{Self-tuning Histograms: Building Histograms Without Looking at Data}},
        author = {Aboulnaga, Ashraf and Chaudhuri, Surajit},
        series = {{SIGMOD} '99},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/304182.304198},
        url = {https://dl.acm.org/doi/10.1145/304182.304198},
        year = {1999}
}

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