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Improved Histograms for Selectivity Estimation of Range Predicates

Summary: Proposes a systematic taxonomy of histograms for range-predicate selectivity estimation, unifying prior types and enabling novel design combinations. Demonstrates sampling to reduce histogram construction cost and empirically evaluates types on range queries to identify the best performers. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
2892
Venue
SIGMOD
Year
1996
Pagerank
0.00063612837
Overall Rank
64 | 99.56%
DOI
-

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