A Learned Query Rewrite System using Monte Carlo Tree Search
Summary: Policy-tree-based learned rewrite uses Monte Carlo Tree Search to explore rewrite orders, avoiding fixed-order local optima. A learned performance model guides the search; parallel tree exploration speeds up optimization. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xuanhe Zhou
- 2. Guoliang Li
- 3. Chengliang Chai
- 4. Jianhua Feng
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Showing 23 of 23 cited papers.
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Semantically Similar Papers
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|---|---|---|---|---|
| 9,993 | Leveraging Query Optimizers to Verify the Soundness of LLM-based Query Rewrites for Real-World Workloads, and More! | 2026 | CIDR | 4.1945683e-05 |
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| 7,499 | Monte Carlo Tree Search for Generating Interactive Data Analysis Interfaces | 2020 | SIGMOD | 4.7180617e-05 |
| 12,090 | Probabilistic Query Rewriting for Efficient and Effective Keyword Search on Graph Data | 2013 | VLDB | 4.1945683e-05 |
| 3,462 | Efficient and Provable Multi-Query Optimization | 2017 | PODS | 7.0703696e-05 |
| 5,144 | Scalable Query Rewriting: A Graph-Based Approach | 2011 | SIGMOD | 5.6651982e-05 |
| 5,023 | GenRewrite: Query Rewriting via Large Language Models | 2026 | SIGMOD | 5.75363e-05 |
| 74 | Efficient Query Evaluation on Probabilistic Databases | 2004 | VLDB | 0.00057857292 |
| 3,472 | LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency | 2025 | VLDB | 7.0639229e-05 |
| 8,969 | A Learned Query Rewrite System | 2023 | VLDB | 4.4189226e-05 |