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The Power of Sampling in Knowledge Discovery

Summary: Approximates truth of tuple-relational-calculus sentences by random sampling, introducing two error measures for universal sentences. Gives near-tight sample-size bounds to catch all n k-quantifier universals with error ≥ ε: O((log n)/ε) or O(|M|^{1-1/k}·log n/ε), and extends to universal–existential cases. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1019
Venue
PODS
Year
1994
Pagerank
6.3162039e-05
Overall Rank
4,250 | 70.47%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank Citing Paper Year Venue Pagerank
473 Sampling Large Databases for Association Rules 1996 VLDB 0.00022304724
2,271 Estimating the Confidence of Conditional Functional Dependencies 2009 SIGMOD 9.1467959e-05
6,285 A Dip in the Reservoir: Maintaining Sample Synopses of Evolving Datasets 2006 VLDB 5.1231886e-05
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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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