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Refactoring Index Tuning Process with Benefit Estimation
Summary: RIBE prunes redundant workload queries and uses plan statistics with an attention-based model to predict per-query index impacts, enabling many costly what-if calls to be skipped. Gains 1–2 orders-of-magnitude speedup and improved estimation vs full-workload and compression baselines, with no DBMS changes.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13395
- Venue
- VLDB
- Year
- 2024
- Pagerank
- 4.7599411e-05
- Overall Rank
- 7,336 | 48.97%
- DOI
-
10.14778/3654621.3654622
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 25 of 25 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 |
| 237 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00031726304 |
| 333 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027206884 |
| 516 |
AutoAdmin "What-if" Index Analysis Utility |
1998 |
SIGMOD |
0.00021196031 |
| 608 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019235898 |
| 640 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018759152 |
| 806 |
An End-to-End Learning-based Cost Estimator |
2020 |
VLDB |
0.00016434274 |
| 884 |
Plan-Structured Deep Neural Network Models for Query Performance Prediction |
2019 |
VLDB |
0.00015654004 |
| 910 |
NeuroCard: One Cardinality Estimator for All Tables |
2021 |
VLDB |
0.00015423056 |
| 1,017 |
Automatic Physical Database Tuning: A Relaxation-based Approach |
2005 |
SIGMOD |
0.00014634307 |
| 1,703 |
Are We Ready For Learned Cardinality Estimation? |
2021 |
VLDB |
0.00010836769 |
| 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 |
| 2,470 |
CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads |
2011 |
VLDB |
8.7333019e-05 |
| 2,762 |
FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation |
2021 |
VLDB |
8.1585394e-05 |
| 3,169 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
7.4498425e-05 |
| 3,580 |
Query Performance Prediction for Concurrent Queries using Graph Embedding |
2020 |
VLDB |
6.9500996e-05 |
| 4,417 |
Robust Query Driven Cardinality Estimation under Changing Workloads |
2023 |
VLDB |
6.2037371e-05 |
| 4,468 |
Comprehensive and Efficient Workload Compression |
2021 |
VLDB |
6.1584035e-05 |
| 5,337 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
5.5635208e-05 |
| 5,686 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
5.3712312e-05 |
| 5,924 |
HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning |
2023 |
VLDB |
5.2719183e-05 |
| 6,366 |
ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning |
2022 |
SIGMOD |
5.0943443e-05 |
| 8,041 |
DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning |
2022 |
VLDB |
4.5998045e-05 |
| 9,388 |
CEDA: Learned Cardinality Estimation with Domain Adaptation |
2023 |
VLDB |
4.3443083e-05 |
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| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 9,618 |
A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach |
2025 |
SIGMOD |
4.3173366e-05 |
| 8,041 |
DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning |
2022 |
VLDB |
4.5998045e-05 |
| 5,074 |
Learned Index: A Comprehensive Experimental Evaluation |
2023 |
VLDB |
5.7175726e-05 |
| 10,032 |
Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads |
2026 |
SIGMOD |
4.1945683e-05 |
| 11,100 |
Looking Deeply into the Magic Mirror: An Interactive Analysis of Database Index Selection Approaches |
2024 |
VLDB |
4.1945683e-05 |
| 10,125 |
Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] |
2026 |
SIGMOD |
4.1945683e-05 |
| 1,855 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00010315245 |
| 5,686 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
5.3712312e-05 |
| 5,337 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
5.5635208e-05 |
| 10,205 |
RIB: Robust Learning-based Index Benefit Estimation |
2026 |
SIGMOD |
4.1945683e-05 |