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Budget-aware Index Tuning with Reinforcement Learning
Summary: Budget-aware index tuning under a capped what-if budget; models the search as an MDP to balance exploration and exploitation. Applies Monte Carlo Tree Search for RL-guided index configuration, outperforming budget-aware baselines on benchmarks and real workloads.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6442
- Venue
- SIGMOD
- Year
- 2022
- Pagerank
- 5.3712312e-05
- Overall Rank
- 5,686 | 60.45%
- DOI
-
10.1145/3514221.3526128
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 18 of 18 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 6,379 |
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning |
2023 |
SIGMOD |
5.0909479e-05 |
| 6,750 |
Breaking It Down: An In-depth Study of Index Advisors |
2024 |
VLDB |
4.9392771e-05 |
| 7,336 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
4.7599411e-05 |
| 8,020 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6040862e-05 |
| 8,026 |
ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning |
2023 |
VLDB |
4.6030518e-05 |
| 8,041 |
DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning |
2022 |
VLDB |
4.5998045e-05 |
| 8,199 |
Leveraging Dynamic and Heterogeneous Workload Knowledge to Boost the Performance of Index Advisors |
2024 |
VLDB |
4.5605795e-05 |
| 8,636 |
WISK: A Workload-aware Learned Index for Spatial Keyword Queries |
2023 |
SIGMOD |
4.4801284e-05 |
| 9,006 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
4.4101482e-05 |
| 9,902 |
Robustness of Updatable Learning-based Index Advisors against Poisoning Attack |
2024 |
SIGMOD |
4.258022e-05 |
| 9,929 |
Wred: Workload Reduction for Scalable Index Tuning |
2024 |
SIGMOD |
4.2510122e-05 |
| 9,930 |
Wii: Dynamic Budget Reallocation In Index Tuning |
2024 |
SIGMOD |
4.2510122e-05 |
| 10,125 |
Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,205 |
RIB: Robust Learning-based Index Benefit Estimation |
2026 |
SIGMOD |
4.1945683e-05 |
| 10,217 |
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 |
4.1945683e-05 |
| 10,230 |
Breaking the Isolation-Freshness Trade-off: Joint Adaptive Storage Optimization for HTAP Systems |
2026 |
VLDB |
4.1945683e-05 |
| 10,543 |
Esc: An Early-Stopping Checker for Budget-aware Index Tuning |
2025 |
VLDB |
4.1945683e-05 |
| 10,668 |
Twisted Twin: A Collaborative and Competitive Memory Management Approach in HTAP Systems |
2025 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 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 |
| 158 |
Automated Selection of Materialized Views and Indexes for SQL Databases |
2000 |
VLDB |
0.00040071492 |
| 237 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00031726304 |
| 516 |
AutoAdmin "What-if" Index Analysis Utility |
1998 |
SIGMOD |
0.00021196031 |
| 1,017 |
Automatic Physical Database Tuning: A Relaxation-based Approach |
2005 |
SIGMOD |
0.00014634307 |
| 1,443 |
Compressing SQL Workloads |
2002 |
SIGMOD |
0.00011947004 |
| 1,855 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00010315245 |
| 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 |
| 2,047 |
Automatically Indexing Millions of Databases in Microsoft Azure SQL Database |
2019 |
SIGMOD |
9.6920209e-05 |
| 2,219 |
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning |
2019 |
SIGMOD |
9.2623533e-05 |
| 2,470 |
CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads |
2011 |
VLDB |
8.7333019e-05 |
| 2,484 |
Efficient Use of the Query Optimizer for Automated Physical Design |
2007 |
VLDB |
8.6864279e-05 |
| 3,072 |
Constrained Physical Design Tuning |
2008 |
VLDB |
7.6114086e-05 |
| 4,468 |
Comprehensive and Efficient Workload Compression |
2021 |
VLDB |
6.1584035e-05 |
| 5,060 |
Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications |
2009 |
VLDB |
5.7273583e-05 |
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