PreQR: Pre-training Representation for SQL Understanding
Summary: PreQR introduces a pretrained SQL representation with an automaton-encoded query structure and a schema-conditioned graph neural network. Attention-based SQL encoding enables on-the-fly schema linking, replacing one-hot encodings and boosting performance on cardinality estimation and join order. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiu Tang (Zhejiang University)
- 2. Sai Wu (Zhejiang University)
- 3. Mingli Song (Zhejiang University)
- 4. Shanshan Ying (Alibaba)
- 5. Feifei Li (Alibaba)
- 6. Gang Chen (Zhejiang University)
BibTeX Citation
@inproceedings{tang_sigmod22,
title = {{PreQR: Pre-training Representation for SQL Understanding}},
author = {Tang, Xiu and Wu, Sai and Song, Mingli and Ying, Shanshan and Li, Feifei and Chen, Gang},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3517878},
url = {https://dl.acm.org/doi/10.1145/3514221.3517878},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,231 | GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization | 2024 | VLDB | 8.7982985e-05 |
| 4,385 | R-Bot: An LLM-based Query Rewrite System | 2025 | VLDB | 6.6235293e-05 |
| 4,468 | Real-time Workload Pattern Analysis for Large-scale Cloud Databases | 2023 | VLDB | 6.5863349e-05 |
| 7,033 | Rethinking Learned Cost Models: Why Start from Scratch? | 2023 | SIGMOD | 5.6168499e-05 |
| 7,236 | RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems | 2025 | VLDB | 5.5790509e-05 |
| 9,222 | SQL-Factory: A Multi-Agent Framework for High-Quality and Large-Scale SQL Generation | 2026 | VLDB | 5.2056825e-05 |
| 9,372 | Efficient and Effective Cardinality Estimation for Skyline Family | 2023 | SIGMOD | 5.1868213e-05 |
| 9,620 | Db2une: Tuning Under Pressure via Deep Learning | 2024 | VLDB | 5.1501614e-05 |
| 11,719 | SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation | 2023 | SIGMOD | 4.9793485e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 15 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00061066921 |
| 85 | Learned Cardinalities: Estimating Correlated Joins with Deep Learning | 2019 | CIDR | 0.00035864347 |
| 98 | LEO - DB2's LEarning Optimizer | 2001 | VLDB | 0.00034106982 |
| 145 | Neo: A Learned Query Optimizer | 2019 | VLDB | 0.0002908188 |
| 314 | An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning | 2019 | SIGMOD | 0.00021282642 |
| 377 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00019570264 |
| 437 | QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning | 2019 | VLDB | 0.00018315867 |
| 461 | An End-to-End Learning-based Cost Estimator | 2020 | VLDB | 0.00017829982 |
| 512 | NeuroCard: One Cardinality Estimator for All Tables | 2021 | VLDB | 0.00017050173 |
| 4,489 | ARM-Net: Adaptive Relation Modeling Network for Structured Data | 2021 | SIGMOD | 6.5781098e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 8,972 | Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries | 2024 | SIGMOD |
| 2 | 461 | An End-to-End Learning-based Cost Estimator | 2020 | VLDB |
| 3 | 4,482 | LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning | 2022 | SIGMOD |
| 4 | 5,911 | On Efficient Approximate Queries over Machine Learning Models | 2023 | VLDB |
| 5 | 2,842 | Query Performance Prediction for Concurrent Queries using Graph Embedding | 2020 | VLDB |
| 6 | 9,956 | Graph Transformers for Query Plan Representation: Potentials and Challenges | 2025 | VLDB |
| 7 | 560 | Plan-Structured Deep Neural Network Models for Query Performance Prediction | 2019 | VLDB |
| 8 | 5,158 | Facilitating SQL Query Composition and Analysis | 2020 | SIGMOD |
| 9 | 2,250 | QueryFormer: A Tree Transformer Model for Query Plan Representation | 2022 | VLDB |
| 10 | 5,194 | Database Workload Characterization with Query Plan Encoders | 2022 | VLDB |