LEO - DB2's LEarning Optimizer
Summary: LEO, DB2's LEarning Optimizer, uses a feedback loop to repair cardinality estimates by comparing forecasts with actuals at each QEP step. Online or offline, incremental or batched, it updates costs and statistics across operators (joins, DISTINCT, GROUP BY) with low overhead and large potential gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Michael Stillger (Siebel Systems, Inc.)
- 2. Guy Lohman (IBM)
- 3. Volker Markl (IBM)
- 4. Mokhtar Kandil (IBM)
BibTeX Citation
@article{stillger_vldb01,
title = {{LEO - DB2's LEarning Optimizer}},
author = {Stillger, Michael and Lohman, Guy and Markl, Volker and Kandil, Mokhtar},
journal = {PVLDB},
series = {{VLDB} '01},
year = {2001}
}Incoming Citations (Sorted by Pagerank)
Showing 23 of 123 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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|---|---|---|---|---|
| 1 | 836 | Proactive Re-Optimization | 2005 | SIGMOD |
| 2 | 1,257 | Sampling-Based Query Re-Optimization | 2016 | SIGMOD |
| 3 | 5,788 | Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries | 2023 | SIGMOD |
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| 5 | 6,660 | Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation | 2023 | SIGMOD |
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| 8 | 10,698 | LIO: A lightweight and interpretable query optimizer based on an evolutionary forest | 2026 | VLDB |
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| 10 | 8,288 | Learning Table Access Cardinalities with LEO | 2002 | SIGMOD |