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QURE: AI-Assisted and Automatically Verified UDF Inlining
Summary: QURE uses LLMs to translate Python/Pandas UDFs to SQL and a formal equivalence verifier to confirm translations across diverse UDFs. Imperative constructs modeled in an intermediate verifier derive SQL-semantics conditions, achieving 88% equivalence (84% translations) with 23x/12x speedups.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 7060
- Venue
- SIGMOD
- Year
- 2025
- Pagerank
- 4.2815042e-05
- Overall Rank
- 9,764 | 32.14%
- DOI
-
10.1145/3709716
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 1,107 |
Froid: Optimization of Imperative Programs in a Relational Database |
2018 |
VLDB |
0.0001397627 |
| 1,875 |
An Architecture for Compiling UDF-centric Workflows |
2015 |
VLDB |
0.00010243959 |
| 2,955 |
Magpie: Python at Speed and Scale using Cloud Backends |
2021 |
CIDR |
7.8188583e-05 |
| 3,649 |
One WITH RECURSIVE is Worth Many GOTOs |
2021 |
SIGMOD |
6.8764882e-05 |
| 3,772 |
Flexible Rule-Based Decomposition and Metadata Independence in Modin: A Parallel Dataframe System |
2022 |
VLDB |
6.7736479e-05 |
| 4,171 |
Computation Reuse in Analytics Job Service at Microsoft |
2018 |
SIGMOD |
6.3800823e-05 |
| 4,571 |
BlackMagic: Automatic Inlining of Scalar UDFs into SQL Queries with Froid |
2019 |
VLDB |
6.0693276e-05 |
| 4,645 |
Aggify: Lifting the Curse of Cursor Loops using Custom Aggregates |
2020 |
SIGMOD |
6.0190618e-05 |
| 4,813 |
Putting Pandas in a Box |
2021 |
CIDR |
5.8993009e-05 |
| 5,844 |
Stage: Query Execution Time Prediction in Amazon Redshift |
2024 |
SIGMOD |
5.3060581e-05 |
| 6,112 |
PL/SQL Without the PL |
2020 |
SIGMOD |
5.2009649e-05 |
| 6,211 |
Snakes on a Plan: Compiling Python Functions into Plain SQL Queries |
2022 |
SIGMOD |
5.1503069e-05 |
| 6,374 |
Dear User-Defined Functions, Inlining isn't working out so great for us. Let's try batching to make our relationship work. Sincerely, SQL |
2024 |
CIDR |
5.0874998e-05 |
| 9,384 |
Versatile Optimization of UDF-heavy Data Flows with Sofa |
2014 |
SIGMOD |
4.3432098e-05 |
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| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 9,717 |
YeSQL: Rich User-Defined Functions without the Overhead |
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VLDB |
4.2939577e-05 |
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VLDB |
6.0693276e-05 |
| 7,137 |
Automated Validating and Fixing of Text-to-SQL Translation with Execution Consistency |
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SIGMOD |
4.8165495e-05 |
| 6,703 |
YeSQL: “You extend SQL” with Rich and Highly Performant User-Defined Functions in Relational Databases |
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VLDB |
4.9514593e-05 |
| 6,191 |
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CIDR |
5.1598046e-05 |
| 10,901 |
Welding Natural Language Queries to Analytics IRs with LLMs |
2024 |
CIDR |
4.1905499e-05 |
| 10,469 |
UDFBench: A Tool for Benchmarking UDF Queries on SQL Engines |
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SIGMOD |
4.1905499e-05 |
| 8,580 |
Efficient Execution of User-Defined Functions in SQL Queries |
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VLDB |
4.4876382e-05 |
| 9,992 |
Leveraging Query Optimizers to Verify the Soundness of LLM-based Query Rewrites for Real-World Workloads, and More! |
2026 |
CIDR |
4.1905499e-05 |