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Histograms Revisited: When are histograms the best approximation method for aggregates over joins?

Summary: Replace the uniform-bucket assumption with a weaker “random arrangement” model, showing it yields the same histogram approximation formulas and permits tight error bounds. Characterize input regimes where histograms beat sampling/sketching for join-aggregate approximation and where they fail on average. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1355
Venue
PODS
Year
2005
Pagerank
5.6166792e-05
Overall Rank
7,443 | 48.94%
DOI
10.1145/1065167.1065196

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{dobra_pods05,
        address = {New York, NY, USA},
        series = {{PODS} '05},
        title = {{Histograms Revisited: When are histograms the best approximation method for aggregates over joins?}},
        url = {https://dl.acm.org/doi/10.1145/1065167.1065196},
        doi = {10.1145/1065167.1065196},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Dobra, Alin},
        year = {2005}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
1,536 Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses 2018 VLDB 0.00010460864
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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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