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LEON: A New Framework for ML-Aided Query Optimization

Summary: LEON augments mature expert optimizers with deployment-specific ML rather than replacing them with RL. Pairwise ranking, uncertainty-guided exploration, and model-guided pruning improve plan quality, training efficiency, stability, and planning cost. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13264
Venue
VLDB
Year
2023
Pagerank
6.7079088e-05
Overall Rank
4,434 | 69.58%
DOI
10.14778/3598581.3598597

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{chen_vldb23,
        title = {{LEON: A New Framework for ML-Aided Query Optimization}},
        author = {Chen, Xu and Chen, Haitian and Liang, Zibo and Liu, Shuncheng and Wang, Jinghong and Zeng, Kai and Su, Han and Zheng, Kai},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {9},
        pages = {2261--2273},
        doi = {10.14778/3598581.3598597},
        url = {https://doi.org/10.14778/3598581.3598597},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 19 of 19 citing papers.

Rank Citing Paper Year Venue Pagerank
6,088 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 5.9813965e-05
6,271 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.9326197e-05
6,462 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 5.8717744e-05
8,163 Learned Offline Query Planning via Bayesian Optimization 2025 SIGMOD 5.4751517e-05
9,428 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 5.2709145e-05
9,601 Low Rank Learning for Offline Query Optimization 2025 SIGMOD 5.2487799e-05
9,626 Spatial Query Optimization With Learning 2024 VLDB 5.2434488e-05
10,108 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.1347137e-05
10,130 Does A Fish Need a Bicycle? The Case for On-Chip NPUs in DBMS 2026 CIDR 5.093636e-05
10,196 Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis] 2026 SIGMOD 5.093636e-05
10,316 GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints 2026 SIGMOD 5.093636e-05
10,401 SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer 2026 SIGMOD 5.093636e-05
10,492 Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization 2026 SIGMOD 5.093636e-05
10,506 This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! 2026 SIGMOD 5.093636e-05
10,559 OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning 2026 VLDB 5.093636e-05
10,672 Optimizing Block Skipping for High-Dimensional Data with Learned Adaptive Curve 2025 SIGMOD 5.093636e-05
11,065 Learned Cost Models for Query Optimization: From Batch to Streaming Systems 2025 VLDB 5.093636e-05
11,083 Graph Transformers for Query Plan Representation: Potentials and Challenges 2025 VLDB 5.093636e-05
11,091 LEAP: A Low-cost Spark SQL Query Optimizer using Pairwise Comparison 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 23 of 23 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 Access Path Selection in a Relational Database Management System 1979 SIGMOD 0.0024089429
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
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
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
388 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019410042
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
563 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001650812
697 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014888851
1,122 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.0001209124
1,241 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011521639
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011361878
1,344 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011094717
1,876 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 9.5717543e-05
1,988 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.3501502e-05
2,313 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.762627e-05
2,822 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 8.0898536e-05
2,888 AI Meets Database: AI4DB and DB4AI 2021 SIGMOD 7.9941489e-05
3,688 FACE: A Normalizing Flow based Cardinality Estimator 2022 VLDB 7.201795e-05
3,998 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 6.9676473e-05
5,059 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 6.3807509e-05
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