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Selectivity Estimation For Boolean Queries

Summary: Proposes compact Monte Carlo set-hash signatures that represent the set of strings containing each substring, enabling on-the-fly estimation of correlations among substring predicates to answer arbitrary Boolean substring queries. Space-efficient, approximate method scales to super-exponential predicate combinations and empirically outperforms independence-based selectivity estimates for IR/query-optimization and query-refinement tasks. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1212
Venue
PODS
Year
2000
Pagerank
9.3807165e-05
Overall Rank
2,171 | 84.90%
DOI
-

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Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
326 Optimal Histograms with Quality Guarantees 1998 VLDB 0.00027358981
1,146 Estimating Alphanumeric Selectivity in the Presence of Wildcards 1996 SIGMOD 0.00013679782
1,379 Substring Selectivity Estimation 1999 PODS 0.00012286879
3,035 Multi-Dimensional Substring Selectivity Estimation 1999 VLDB 7.6748073e-05
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