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A Comparison of Selectivity Estimators for Range Queries on Metric Attributes

Summary: Comparison of nonparametric selectivity estimators for range queries on metric attributes with large domains and limited samples; includes histograms, kernel estimators, and a histogram–kernel hybrid. Kernels perform best on continuously distributed data; the hybrid est. is most promising on real data; key factors: sample size and smoothing (bins). (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
3165
Venue
SIGMOD
Year
1999
Pagerank
6.4807645e-05
Overall Rank
4,846 | 66.76%
DOI
10.1145/304182.304203

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{blohsfeld_sigmod99,
        title = {{A Comparison of Selectivity Estimators for Range Queries on Metric Attributes}},
        author = {Blohsfeld, Björn and Korus, Dieter and Seeger, Bernhard},
        series = {{SIGMOD} '99},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/304182.304203},
        url = {https://dl.acm.org/doi/10.1145/304182.304203},
        year = {1999}
}

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