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
- 2. Sai Wu
- 3. Mingli Song
- 4. Shanshan Ying
- 5. Feifei Li
- 6. Gang Chen
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,114 | GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization | 2024 | VLDB | 7.5451724e-05 |
| 3,429 | Real-time Workload Pattern Analysis for Large-scale Cloud Databases | 2023 | VLDB | 7.1010535e-05 |
| 7,035 | R-Bot: An LLM-based Query Rewrite System | 2025 | VLDB | 4.8548467e-05 |
| 7,753 | Rethinking Learned Cost Models: Why Start from Scratch? | 2023 | SIGMOD | 4.660151e-05 |
| 7,989 | RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems | 2025 | VLDB | 4.6124681e-05 |
| 8,896 | SQL-Factory: A Multi-Agent Framework for High-Quality and Large-Scale SQL Generation | 2026 | VLDB | 4.427232e-05 |
| 9,352 | Db2une: Tuning Under Pressure via Deep Learning | 2024 | VLDB | 4.3522361e-05 |
| 11,190 | Efficient and Effective Cardinality Estimation for Skyline Family | 2023 | SIGMOD | 4.1945683e-05 |
| 11,203 | SSIN: Self-Supervised Learning for Rainfall Spatial Interpolation | 2023 | SIGMOD | 4.1945683e-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 |
|---|---|---|---|---|
| 71 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00059038975 |
| 182 | LEO - DB2's LEarning Optimizer | 2001 | VLDB | 0.00036962631 |
| 204 | Learned Cardinalities: Estimating Correlated Joins with Deep Learning | 2019 | CIDR | 0.00034784455 |
| 333 | Neo: A Learned Query Optimizer | 2019 | VLDB | 0.00027206884 |
| 513 | TURL: Table Understanding through Representation Learning | 2021 | VLDB | 0.00021288342 |
| 514 | An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning | 2019 | SIGMOD | 0.0002124895 |
| 782 | QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning | 2019 | VLDB | 0.00016729063 |
| 806 | An End-to-End Learning-based Cost Estimator | 2020 | VLDB | 0.00016434274 |
| 910 | NeuroCard: One Cardinality Estimator for All Tables | 2021 | VLDB | 0.00015423056 |
| 3,148 | ARM-Net: Adaptive Relation Modeling Network for Structured Data | 2021 | SIGMOD | 7.4751269e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,623 | Qr-Hint: Actionable Hints Towards Correcting Wrong SQL Queries | 2024 | SIGMOD | 4.3161663e-05 |
| 806 | An End-to-End Learning-based Cost Estimator | 2020 | VLDB | 0.00016434274 |
| 5,371 | LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning | 2022 | SIGMOD | 5.5428776e-05 |
| 9,351 | On Efficient Approximate Queries over Machine Learning Models | 2023 | VLDB | 4.3524472e-05 |
| 3,580 | Query Performance Prediction for Concurrent Queries using Graph Embedding | 2020 | VLDB | 6.9500996e-05 |
| 10,859 | Graph Transformers for Query Plan Representation: Potentials and Challenges | 2025 | VLDB | 4.1945683e-05 |
| 884 | Plan-Structured Deep Neural Network Models for Query Performance Prediction | 2019 | VLDB | 0.00015654004 |
| 5,473 | Facilitating SQL Query Composition and Analysis | 2020 | SIGMOD | 5.4885366e-05 |
| 3,169 | QueryFormer: A Tree Transformer Model for Query Plan Representation | 2022 | VLDB | 7.4498425e-05 |
| 5,637 | Database Workload Characterization with Query Plan Encoders | 2022 | VLDB | 5.3979505e-05 |