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 |
|---|---|---|---|---|
| 2,298 | GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization | 2024 | VLDB | 8.7886538e-05 |
| 4,398 | Real-time Workload Pattern Analysis for Large-scale Cloud Databases | 2023 | VLDB | 6.7248611e-05 |
| 6,259 | R-Bot: An LLM-based Query Rewrite System | 2025 | VLDB | 5.93967e-05 |
| 6,921 | Rethinking Learned Cost Models: Why Start from Scratch? | 2023 | SIGMOD | 5.7388557e-05 |
| 7,825 | RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems | 2025 | VLDB | 5.5367884e-05 |
| 9,047 | SQL-Factory: A Multi-Agent Framework for High-Quality and Large-Scale SQL Generation | 2026 | VLDB | 5.3251649e-05 |
| 9,192 | Efficient and Effective Cardinality Estimation for Skyline Family | 2023 | SIGMOD | 5.3058708e-05 |
| 9,441 | Db2une: Tuning Under Pressure via Deep Learning | 2024 | VLDB | 5.2678428e-05 |
| 11,404 | SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation | 2023 | SIGMOD | 5.093636e-05 |
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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 |
|---|---|---|---|---|
| 18 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00059284255 |
| 84 | Learned Cardinalities: Estimating Correlated Joins with Deep Learning | 2019 | CIDR | 0.00035838391 |
| 100 | LEO - DB2's LEarning Optimizer | 2001 | VLDB | 0.00034385207 |
| 154 | Neo: A Learned Query Optimizer | 2019 | VLDB | 0.00028726181 |
| 334 | An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning | 2019 | SIGMOD | 0.00020875082 |
| 397 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00019278189 |
| 465 | An End-to-End Learning-based Cost Estimator | 2020 | VLDB | 0.0001803934 |
| 498 | QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning | 2019 | VLDB | 0.00017440583 |
| 513 | NeuroCard: One Cardinality Estimator for All Tables | 2021 | VLDB | 0.00017190574 |
| 4,390 | ARM-Net: Adaptive Relation Modeling Network for Structured Data | 2021 | SIGMOD | 6.7290926e-05 |
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