Learned Offline Query Planning via Bayesian Optimization
Summary: Offline query planning for repeated analytics workloads; learned exploration. Variational auto-encoders + Bayesian optimization search broad plan space, using execution as feedback; outperforms PostgreSQL-optimal and RL baselines on several datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jeffrey Tao (University of Pennsylvania)
- 2. Natalie Maus (University of Pennsylvania)
- 3. Haydn Jones (University of Pennsylvania)
- 4. Yimeng Zeng (University of Pennsylvania)
- 5. Jacob R. Gardner (University of Pennsylvania)
- 6. Ryan Marcus (University of Pennsylvania)
BibTeX Citation
@inproceedings{tao_sigmod25,
title = {{Learned Offline Query Planning via Bayesian Optimization}},
author = {Tao, Jeffrey and Maus, Natalie and Jones, Haydn and Zeng, Yimeng and Gardner, Jacob R. and Marcus, Ryan},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725316},
url = {https://dl.acm.org/doi/10.1145/3725316},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,601 | Low Rank Learning for Offline Query Optimization | 2025 | SIGMOD | 5.2487799e-05 |
| 10,128 | Survivorship Bias in Industrial Database Workloads | 2026 | CIDR | 5.093636e-05 |
| 10,232 | EncoderForge: Generating Efficient SQL for Encoders in Machine Learning Inference Pipelines | 2026 | SIGMOD | 5.093636e-05 |
| 10,506 | This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! | 2026 | SIGMOD | 5.093636e-05 |
| 10,559 | OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning | 2026 | VLDB | 5.093636e-05 |
| 10,586 | TATA: An Efficient Framework for Task Transfer in Query Plan Representation | 2026 | VLDB | 5.093636e-05 |
| 11,007 | GRewriter: Practical Query Rewriting with Automatic Rule Set Expansion in GaussDB | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 46 of 46 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 11,548 | DeepO: A Learned Query Optimizer | 2022 | SIGMOD |
| 2 | 10,559 | OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning | 2026 | VLDB |
| 3 | 6,884 | Explaining Inference Queries with Bayesian Optimization | 2021 | VLDB |
| 4 | 10,508 | Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking | 2026 | SIGMOD |
| 5 | 7,809 | Can Large Language Models Be Query Optimizer for Relational Databases? | 2026 | SIGMOD |
| 6 | 9,601 | Low Rank Learning for Offline Query Optimization | 2025 | SIGMOD |
| 7 | 5,394 | Leveraging Query Logs and Machine Learning for Parametric Query Optimization | 2022 | VLDB |
| 8 | 3,051 | Towards a Hands-Free Query Optimizer through Deep Learning | 2019 | CIDR |
| 9 | 6,271 | Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective | 2024 | VLDB |
| 10 | 2,762 | Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection | 2022 | VLDB |