Can Transfer Learning be used to build a Query Optimizer?
Summary: Proposes transferring a query optimizer: given DB System A with a good optimizer and System B with poor/no optimizer but similar physical operators, learn A's optimization patterns to build B's optimizer. Unique focus on cross-system transfer learning using shared operator semantics to reuse optimizer knowledge rather than per-system tuning, and outlines feasibility and challenges. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yunjia Zhang (University of Wisconsin)
- 2. Yannis Chronis (University of Wisconsin)
- 3. Jignesh M. Patel (University of Wisconsin)
- 4. Theodoros Rekatsinas (University of Wisconsin)
BibTeX Citation
@inproceedings{zhang_cidr22,
address = {Amsterdam, Netherlands},
series = {{CIDR} '22},
title = {{Can Transfer Learning be used to build a Query Optimizer?}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Zhang, Yunjia and Chronis, Yannis and Patel, Jignesh M. and Rekatsinas, Theodoros},
year = {2022}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 378 | Bao: Making Learned Query Optimization Practical | 2021 | SIGMOD | 0.00019638121 |
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