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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
3388
Venue
SIGMOD
Year
2002
Pagerank
4.5826944e-05
Overall Rank
8,113 | 43.56%
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
3,013 Cardinality Estimation Using Sample Views with Quality Assurance 2007 SIGMOD 7.7137441e-05
6,519 Expand your Training Limits! Generating Training Data for ML-based Data Management 2021 SIGMOD 5.0316686e-05
12,291 Visualizing the robustness of query execution 2009 CIDR 4.1945683e-05
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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
182 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00036962631
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