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Leveraging Query Optimizers to Verify the Soundness of LLM-based Query Rewrites for Real-World Workloads, and More!
Summary: Empirical study on Microsoft SQL Server shows LLM-based query rewriting improves real-world workloads but cannot guarantee semantic equivalence. Propose a sound, efficient equivalence-checking technique leveraging optimizer capabilities and show LLM rewrites expose missing optimizer transformation rules.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 595
- Venue
- CIDR
- Year
- 2026
- Pagerank
- 4.1945683e-05
- Overall Rank
- 9,993 | 30.49%
- DOI
-
-
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 24 of 24 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059038975 |
| 639 |
Orthogonal Optimization of Subqueries and Aggregation |
2001 |
SIGMOD |
0.00018791492 |
| 659 |
The Making of TPC-DS |
2006 |
VLDB |
0.00018500853 |
| 661 |
Database Tuning Advisor for Microsoft SQL Server 2005 |
2004 |
VLDB |
0.00018481174 |
| 1,057 |
Cosette: An Automated Prover for SQL |
2017 |
CIDR |
0.0001439886 |
| 1,869 |
WinMagic : Subquery Elimination Using Window Aggregation |
2003 |
SIGMOD |
0.00010265836 |
| 2,099 |
Axiomatic Foundations and Algorithms for Deciding Semantic Equivalences of SQL Queries |
2018 |
VLDB |
9.5479391e-05 |
| 2,596 |
WeTune: Automatic Discovery and Verification of Query Rewrite Rules |
2022 |
SIGMOD |
8.4729982e-05 |
| 2,985 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
7.7795847e-05 |
| 3,248 |
A Learned Query Rewrite System using Monte Carlo Tree Search |
2022 |
VLDB |
7.3258782e-05 |
| 3,284 |
Configuration-Parametric Query Optimization for Physical Design Tuning |
2008 |
SIGMOD |
7.2790444e-05 |
| 3,472 |
LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency |
2025 |
VLDB |
7.0639229e-05 |
| 4,238 |
Panda: Performance Debugging for Databases using LLM Agents |
2024 |
CIDR |
6.331901e-05 |
| 4,388 |
Proving Query Equivalence Using Linear Integer Arithmetic |
2023 |
SIGMOD |
6.2303078e-05 |
| 5,023 |
GenRewrite: Query Rewriting via Large Language Models |
2026 |
SIGMOD |
5.75363e-05 |
| 5,243 |
QED: A Powerful Query Equivalence Decider for SQL |
2024 |
VLDB |
5.6071695e-05 |
| 5,525 |
QueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting |
2023 |
VLDB |
5.4600815e-05 |
| 6,673 |
Incorporating Super-Operators in Big-Data Query Optimizers |
2020 |
VLDB |
4.966799e-05 |
| 7,035 |
R-Bot: An LLM-based Query Rewrite System |
2025 |
VLDB |
4.8548467e-05 |
| 7,205 |
Unified Query Optimization in the Fabric Data Warehouse |
2024 |
SIGMOD |
4.8014977e-05 |
| 8,016 |
User-Optimizer Communication using Abstract Plans in Sybase ASE |
2001 |
VLDB |
4.6050078e-05 |
| 8,847 |
Towards Foundation Database Models |
2025 |
CIDR |
4.4371897e-05 |
| 9,277 |
DBG-PT: A Large Language Model Assisted Query Performance Regression Debugger |
2024 |
VLDB |
4.3640804e-05 |
| 9,850 |
COMPARE: Accelerating Groupwise Comparison in Relational Databases for Data Analytics |
2021 |
VLDB |
4.2721228e-05 |
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| 5,840 |
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LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency |
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VLDB |
7.0639229e-05 |
| 5,023 |
GenRewrite: Query Rewriting via Large Language Models |
2026 |
SIGMOD |
5.75363e-05 |