DBScholar

Back to papers

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
h318924391a764ed3
Venue
VLDB
Year
2023
Pagerank
6.7064546e-05
Overall Rank
4,240 | 71.51%
DOI
10.14778/3598581.3598597
PDF
Download (CC BY-NC-ND 4.0)

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 21 of 21 citing papers.

Rank Citing Paper Year Venue Pagerank
5,316 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 6.1827415e-05
5,700 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 6.028998e-05
6,298 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.8177684e-05
7,546 Learned Offline Query Planning via Bayesian Optimization 2025 SIGMOD 5.4966669e-05
8,373 GenJoin: Conditional Generative Plan-to-Plan Query Optimizer that Learns from Subplan Hints 2026 SIGMOD 5.3427119e-05
8,638 LIMAO: A Framework for Lifelong Modular Learned Query Optimization 2025 VLDB 5.2965922e-05
8,888 Spatial Query Optimization With Learning 2024 VLDB 5.2543468e-05
9,635 Low Rank Learning for Offline Query Optimization 2025 SIGMOD 5.1453041e-05
9,962 Graph Transformers for Query Plan Representation: Potentials and Challenges 2025 VLDB 5.1014161e-05
10,147 Does A Fish Need a Bicycle? The Case for On-Chip NPUs in DBMS 2026 CIDR 5.0691578e-05
10,296 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 5.0407989e-05
10,343 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.0176429e-05
10,424 Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis] 2026 SIGMOD 4.9769913e-05
10,607 SEFRQO: A Self-Evolving Fine-Tuned RAG-Based Query Optimizer 2026 SIGMOD 4.9769913e-05
10,690 Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization 2026 SIGMOD 4.9769913e-05
10,751 OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning 2026 VLDB 4.9769913e-05
10,917 DBAgent: An RL-Based Agent for Autonomous Database Operations and Maintenance 2026 VLDB 4.9769913e-05
10,928 ScaleSense: Cost-Intelligent Scaling Framework via Learned Resource Estimation in Alibaba AnalyticDB 2026 VLDB 4.9769913e-05
11,122 Optimizing Block Skipping for High-Dimensional Data with Learned Adaptive Curve 2025 SIGMOD 4.9769913e-05
11,431 Learned Cost Models for Query Optimization: From Batch to Streaming Systems 2025 VLDB 4.9769913e-05
11,449 LEAP: A Low-cost Spark SQL Query Optimizer using Pairwise Comparison 2025 VLDB 4.9769913e-05
Previous Page 1 / 1 Next

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 0.0023943337
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061067652
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
361 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00020000855
386 Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors 2009 VLDB 0.00019446558
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
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016408613
691 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014737455
981 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012713454
1,195 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011574218
1,280 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011224914
1,289 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011159167
1,735 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 9.7566604e-05
2,002 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.2076835e-05
2,278 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.7057608e-05
2,833 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9539771e-05
2,908 AI Meets Database: AI4DB and DB4AI 2021 SIGMOD 7.8716173e-05
3,742 FACE: A Normalizing Flow based Cardinality Estimator 2022 VLDB 7.0564546e-05
3,948 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 6.9051584e-05
5,110 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 6.2678118e-05
Previous Page 1 / 1 Next

Semantically Similar Papers