Learning Table Access Cardinalities with LEO

Summary: LEO uses a feedback loop to learn from executions, repairing cardinalities and predicate selectivities to improve estimates. It generalizes to similar queries, detects cardinality errors across operators, piggybacks on prior runs with negligible overhead. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 3450
Venue: SIGMOD
Year: 2002
Pagerank: 5.4829273e-05
Overall Rank: 8,125 | 44.26%
DOI: 10.1145/564691.564766

Incoming Non-self Citations Over Time

Authors

1. Volker Markl (IBM)
2. Guy Lohman (IBM)

BibTeX Citation

@inproceedings{markl_sigmod02,
        title = {{Learning Table Access Cardinalities with LEO}},
        author = {Markl, Volker and Lohman, Guy},
        series = {{SIGMOD} '02},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/564691.564766},
        url = {https://dl.acm.org/doi/10.1145/564691.564766},
        year = {2002}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,404	Cardinality Estimation Using Sample Views with Quality Assurance	2007	SIGMOD	8.6225576e-05
6,024	Expand your Training Limits! Generating Training Data for ML-based Data Management	2021	SIGMOD	6.0031118e-05
12,486	Visualizing the robustness of query execution	2009	CIDR	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
100	LEO - DB2's LEarning Optimizer	2001	VLDB	0.00034385207

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9	6,543	Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation	2023	SIGMOD
10	100	LEO - DB2's LEarning Optimizer	2001	VLDB