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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
1356
Venue
PODS
Year
2005
Pagerank
4.8163484e-05
Overall Rank
7,150 | 50.26%
DOI
-

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Rank Citing Paper Year Venue Pagerank
1,981 Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses 2018 VLDB 9.8687545e-05
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