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Index-Based Join Size Estimation Using Adaptive Sampling

Summary: Adaptive sampling with persisted index counts for index-based join size estimation; budget- or confidence-driven sampling stops. On skewed benchmarks, it yields 16% overall speedup and 34% per-query gains when plans adapt to estimates. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6072
Venue
SIGMOD
Year
2021
Pagerank
5.093636e-05
Overall Rank
11,644 | 20.12%
DOI
10.1145/3448016.3450572

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Authors

BibTeX Citation

@inproceedings{pocol_sigmod21,
        title = {{Index-Based Join Size Estimation Using Adaptive Sampling}},
        author = {Pocol, Sergiu},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3450572},
        url = {https://dl.acm.org/doi/10.1145/3448016.3450572},
        year = {2021}
}

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

Rank Cited Paper Year Venue Pagerank
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
694 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014911698
4,277 Adaptive Statistics in Oracle 12c 2017 VLDB 6.7873816e-05
5,693 Exploiting Ordered Dictionaries to Efficiently Construct Histograms with Q-Error Guarantees in SAP HANA 2014 SIGMOD 6.1189424e-05
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