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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
h3b3b26408a593df0
Venue
PODS
Year
1994
Pagerank
6.4726631e-05
Overall Rank
4,679 | 68.55%
DOI
10.1145/182591.182601

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kivinen_pods94,
        address = {New York, NY, USA},
        series = {{PODS} '94},
        title = {{The Power of Sampling in Knowledge Discovery}},
        url = {https://dl.acm.org/doi/10.1145/182591.182601},
        doi = {10.1145/182591.182601},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Kivinen, Jyrki and Mannila, Heikki},
        year = {1994}
}

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 0.00017673931
2,904 Estimating the Confidence of Conditional Functional Dependencies 2009 SIGMOD 7.8801067e-05
5,851 A Dip in the Reservoir: Maintaining Sample Synopses of Evolving Datasets 2006 VLDB 5.9718136e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

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