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 |
|---|---|---|---|---|
| 2,227 | GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization | 2024 | VLDB | 8.8007923e-05 |
| 4,384 | R-Bot: An LLM-based Query Rewrite System | 2025 | VLDB | 6.6232918e-05 |
| 4,470 | Real-time Workload Pattern Analysis for Large-scale Cloud Databases | 2023 | VLDB | 6.5833414e-05 |
| 7,021 | Rethinking Learned Cost Models: Why Start from Scratch? | 2023 | SIGMOD | 5.6168049e-05 |
| 7,238 | RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems | 2025 | VLDB | 5.5764098e-05 |
| 9,232 | SQL-Factory: A Multi-Agent Framework for High-Quality and Large-Scale SQL Generation | 2026 | VLDB | 5.2032182e-05 |
| 9,381 | Efficient and Effective Cardinality Estimation for Skyline Family | 2023 | SIGMOD | 5.1843659e-05 |
| 9,627 | Db2une: Tuning Under Pressure via Deep Learning | 2024 | VLDB | 5.1477233e-05 |
| 11,725 | SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation | 2023 | SIGMOD | 4.9769913e-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 |
|---|---|---|---|---|
| 15 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00061067652 |
| 85 | Learned Cardinalities: Estimating Correlated Joins with Deep Learning | 2019 | CIDR | 0.00035876108 |
| 98 | LEO - DB2's LEarning Optimizer | 2001 | VLDB | 0.00034099838 |
| 144 | Neo: A Learned Query Optimizer | 2019 | VLDB | 0.00029090793 |
| 314 | An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning | 2019 | SIGMOD | 0.00021276452 |
| 377 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00019564011 |
| 437 | QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning | 2019 | VLDB | 0.00018310278 |
| 462 | An End-to-End Learning-based Cost Estimator | 2020 | VLDB | 0.00017836105 |
| 510 | NeuroCard: One Cardinality Estimator for All Tables | 2021 | VLDB | 0.00017059914 |
| 4,492 | ARM-Net: Adaptive Relation Modeling Network for Structured Data | 2021 | SIGMOD | 6.5749958e-05 |
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