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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!

Summary: Booster augments existing DBMS autotuners with LLMs and query-level historical artifacts, turning prior tuning runs into per-query config contexts that can be reused under workload drift/schema transfer. Beam-search composition of query suggestions yields much faster re-optimization, up to 74% better configs than retraining/continuing from scratch. (summarized by gpt-5-mini on Apr 11 2026)

Paper ID
7529
Venue
SIGMOD
Year
2026
Pagerank
4.1945683e-05
Overall Rank
10,217 | 28.93%
DOI
10.1145/3786704

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Showing 50 of 69 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
183 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036721403
359 Self-Driving Database Management Systems 2017 CIDR 0.0002592783
514 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.0002124895
516 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00021196031
640 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00018759152
663 Adaptive Self-Tuning Memory in DB2 2006 VLDB 0.00018469455
716 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00017723171
782 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00016729063
884 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00015654004
1,017 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.00014634307
1,855 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00010315245
1,872 ReAcTable: Enhancing ReAct for Table Question Answering 2024 VLDB 0.00010259702
1,956 D-Bot: Database Diagnosis System using Large Language Models 2024 VLDB 9.960627e-05
2,020 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 9.762624e-05
2,985 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 7.7795847e-05
3,142 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 7.4815444e-05
3,169 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 7.4498425e-05
3,178 Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet 2024 VLDB 7.4325992e-05
3,400 ELPIS: Graph-Based Similarity Search for Scalable Data Science 2023 VLDB 7.1405533e-05
3,429 Real-time Workload Pattern Analysis for Large-scale Cloud Databases 2023 VLDB 7.1010535e-05
3,472 LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency 2025 VLDB 7.0639229e-05
3,779 Instance-Optimized Data Layouts for Cloud Analytics Workloads 2021 SIGMOD 6.7747205e-05
3,812 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 6.7373184e-05
3,828 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 6.7208524e-05
4,240 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 6.3318228e-05
4,380 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 6.2396606e-05
4,417 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 6.2037371e-05
4,462 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 6.1611784e-05
4,590 MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems 2021 SIGMOD 6.0620053e-05
4,623 Automated Generation of Materialized Views in Oracle 2020 VLDB 6.0411909e-05
4,762 METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection 2024 VLDB 5.9395463e-05
4,908 Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL 2024 VLDB 5.8339245e-05
4,913 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 5.8316231e-05
5,334 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 5.5649836e-05
5,337 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 5.5635208e-05
5,572 The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data 2023 SIGMOD 5.4277273e-05
5,633 Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server 2023 VLDB 5.4011156e-05
5,640 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 5.3933314e-05
5,645 Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts 2022 SIGMOD 5.3923454e-05
5,686 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 5.3712312e-05
6,110 Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud 2022 VLDB 5.2056003e-05
6,151 An Efficient Transfer Learning Based Configuration Adviser for Database Tuning 2024 VLDB 5.183652e-05
6,375 Dear User-Defined Functions, Inlining isn't working out so great for us. Let's try batching to make our relationship work. Sincerely, SQL 2024 CIDR 5.0923872e-05
6,379 A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning 2023 SIGMOD 5.0909479e-05
6,685 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 4.9627485e-05
6,765 Automatic Database Configuration Debugging using Retrieval-Augmented Language Models 2025 SIGMOD 4.9325583e-05
7,008 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 4.8643538e-05
7,221 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 4.797194e-05
7,309 DBMind: A Self-Driving Platform in openGauss 2021 VLDB 4.766574e-05
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