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AI Meets AI: Leveraging Query Executions to Improve Index Recommendations

Summary: Replaces optimizer-cost plan comparison in index tuning with an ML classifier predicting cheaper plans across configs. Integrates with advanced tuners, delivering up to 5x fewer errors on benchmarks and real workloads, reducing cost regressions. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5828
Venue
SIGMOD
Year
2019
Pagerank
0.00011361878
Overall Rank
1,279 | 91.23%
DOI
10.1145/3299869.3324957

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{ding_sigmod19,
        title = {{AI Meets AI: Leveraging Query Executions to Improve Index Recommendations}},
        author = {Ding, Bailu and Das, Sudipto and Marcus, Ryan and Wu, Wentao and Chaudhuri, Surajit and Narasayya, Vivek R.},
        series = {{SIGMOD} '19},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3299869.3324957},
        url = {https://dl.acm.org/doi/10.1145/3299869.3324957},
        year = {2019}
}

Incoming Citations (Sorted by Pagerank)

Showing 50 of 58 citing papers.

Rank Citing Paper Year Venue Pagerank
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
1,337 DB-BERT: A Database Tuning Tool that "Reads the Manual" 2022 SIGMOD 0.00011117488
1,481 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010644613
1,548 Automatically Indexing Millions of Databases in Microsoft Azure SQL Database 2019 SIGMOD 0.00010392475
1,832 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 9.6607418e-05
2,313 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.762627e-05
2,355 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7022189e-05
2,420 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.605257e-05
2,651 Magpie: Python at Speed and Scale using Cloud Backends 2021 CIDR 8.2918086e-05
3,809 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.1074195e-05
3,926 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.0128068e-05
3,998 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 6.9676473e-05
4,368 Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process 2022 SIGMOD 6.7393882e-05
4,434 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7079088e-05
4,470 Kepler: Robust Learning for Faster Parametric Query Optimization 2023 SIGMOD 6.6817353e-05
4,643 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.5907466e-05
5,011 Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach 2020 SIGMOD 6.4020848e-05
5,073 Database Workload Characterization with Query Plan Encoders 2022 VLDB 6.3751266e-05
5,091 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 6.3669569e-05
5,107 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.3623786e-05
5,132 Facilitating SQL Query Composition and Analysis 2020 SIGMOD 6.3534526e-05
5,340 Machine Learning for Databases 2021 VLDB 6.2603359e-05
5,558 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.1749098e-05
5,573 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1682747e-05
5,767 A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies 2024 VLDB 6.0945741e-05
5,974 Towards instance-optimized data systems 2021 VLDB 6.0230488e-05
6,024 Expand your Training Limits! Generating Training Data for ML-based Data Management 2021 SIGMOD 6.0031118e-05
6,327 Breaking It Down: An In-depth Study of Index Advisors 2024 VLDB 5.9124005e-05
6,434 Yannakakis+: Practical Acyclic Query Evaluation with Theoretical Guarantees 2025 SIGMOD 5.8799421e-05
6,462 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 5.8717744e-05
6,600 A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning 2023 SIGMOD 5.8250114e-05
6,939 Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and Opportunities 2022 SIGMOD 5.7338637e-05
7,076 Refactoring Index Tuning Process with Benefit Estimation 2024 VLDB 5.7098893e-05
7,661 Machine Learning for Cloud Data Systems: the Progress so far and the Path Forward 2021 VLDB 5.5736026e-05
7,750 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 5.5523652e-05
7,825 RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems 2025 VLDB 5.5367884e-05
7,846 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.5331459e-05
8,040 PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 2021 VLDB 5.5018396e-05
9,351 DBG-PT: A Large Language Model Assisted Query Performance Regression Debugger 2024 VLDB 5.2839552e-05
9,490 Robustness of Updatable Learning-based Index Advisors against Poisoning Attack 2024 SIGMOD 5.2629522e-05
9,615 Wii: Dynamic Budget Reallocation In Index Tuning 2024 SIGMOD 5.2436464e-05
9,734 Optimizing Dataflow Systems for Scalable Interactive Visualization 2024 SIGMOD 5.227679e-05
9,739 Waffle: In-memory Grid Index for Moving Objects with Reinforcement Learning-based Configuration Tuning System 2022 VLDB 5.227679e-05
10,082 Wred: Workload Reduction for Scalable Index Tuning 2024 SIGMOD 5.1587525e-05
10,196 Are Learned DBMS Components Robust to Workload Drift?: [Experiments & Analysis] 2026 SIGMOD 5.093636e-05
10,327 Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads 2026 SIGMOD 5.093636e-05
10,343 APQO: An Adaptive Framework for Parametric Query Optimization 2026 SIGMOD 5.093636e-05
10,413 Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] 2026 SIGMOD 5.093636e-05
10,494 RIB: Robust Learning-based Index Benefit Estimation 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
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Outgoing Citations (Sorted by Pagerank)

Showing 34 of 34 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
43 The Case for Learned Index Structures 2018 SIGMOD 0.00046060254
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
100 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034385207
126 Schism: a Workload-Driven Approach to Database Replication and Partitioning 2010 VLDB 0.00030779127
156 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028636811
184 DB2 Design Advisor: Integrated Automatic Physical Database Design 2004 VLDB 0.00026256101
199 Integrating Vertical and Horizontal Partitioning into Automated Physical Database Design 2004 SIGMOD 0.00025612088
222 Adaptive Selectivity Estimation Using Query Feedback 1994 SIGMOD 0.00024193708
234 Self-Driving Database Management Systems 2017 CIDR 0.00023810722
246 Automating Physical Database Design in a Parallel Database 2002 SIGMOD 0.00023457421
259 Database Cracking 2007 CIDR 0.00023119313
290 An Overview of Query Optimization in Relational Systems 1998 PODS 0.0002227038
387 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00019442332
501 Database Tuning Advisor for Microsoft SQL Server 2005 2004 VLDB 0.0001738508
523 Adaptive Self-Tuning Memory in DB2 2006 VLDB 0.00017133451
524 Automatic SQL Tuning in Oracle 10g 2004 VLDB 0.00017120666
624 Performance Prediction for Concurrent Database Workloads 2011 SIGMOD 0.00015683402
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00015014887
781 Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering 2002 VLDB 0.00014085674
1,199 Bridging the Archipelago between Row-Stores and Column-Stores for Hybrid Workloads 2016 SIGMOD 0.00011703966
1,256 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011457194
1,367 H2O: A Hands-free Adaptive Store 2014 SIGMOD 0.00011014419
1,548 Automatically Indexing Millions of Databases in Microsoft Azure SQL Database 2019 SIGMOD 0.00010392475
1,616 The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models 2018 SIGMOD 0.00010213691
1,811 Merging What's Cracked, Cracking What's Merged: Adaptive Indexing in Main-Memory Column-Stores 2011 VLDB 9.698026e-05
1,904 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5040429e-05
2,466 Goal-Oriented Buffer Management Revisited* 1996 SIGMOD 8.5428771e-05
2,722 Automatic Physical Design Tuning: Workload as a Sequence 2006 SIGMOD 8.206711e-05
2,944 Query Optimizers: Time to Rethink the Contract? 2009 SIGMOD 7.9335187e-05
3,598 Access Path Selection in Main-Memory Optimized Data Systems: Should I Scan or Should I Probe? 2017 SIGMOD 7.2718988e-05
4,637 Columnstore and B+ tree – Are Hybrid Physical Designs Important? 2018 SIGMOD 6.592197e-05
5,365 A Pay-As-You-Go Framework for Query Execution Feedback 2008 VLDB 6.2462467e-05
7,655 Plan Stitch: Harnessing the Best of Many Plans 2018 VLDB 5.5741093e-05
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