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LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans
Summary: LOGER models table/predicate relations with a Graph Transformer and predicts operator-restriction masks (not per-join operator picks), delegating physical operator choice to the DBMS for robustness. With epsilon-beam search and reward-weighted, log-transformed loss to stabilize DRL, LOGER yields superior plans (2.07× on JOB vs PostgreSQL).
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13035
- Venue
- VLDB
- Year
- 2023
- Pagerank
- 6.1611784e-05
- Overall Rank
- 4,462 | 68.96%
- DOI
-
10.14778/3587136.3587150
Incoming Non-self Citations Over Time
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