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RIB: Robust Learning-based Index Benefit Estimation
Summary: Robust learned index-benefit estimation for index tuning under noisy telemetry. RIB combines a context-aware bidirectional GNN encoder with fully parameterized quantile regression to curb epistemic/aleatoric label noise and improve recommendation quality.
(summarized by gpt-5-mini on Apr 11 2026)
- Paper ID
- 7518
- Venue
- SIGMOD
- Year
- 2026
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,205 | 29.08%
- DOI
-
10.1145/3786691
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Incoming Citations (Sorted by Pagerank)
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| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 237 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00031727601 |
| 517 |
AutoAdmin "What-if" Index Analysis Utility |
1998 |
SIGMOD |
0.00021193179 |
| 680 |
Towards a Robust Query Optimizer: A Principled and Practical Approach |
2005 |
SIGMOD |
0.00018193263 |
| 1,856 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00010319105 |
| 2,022 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
9.7623022e-05 |
| 2,187 |
Least Expected Cost Query Optimization: What Can We Expect? |
2002 |
PODS |
9.3429142e-05 |
| 2,479 |
Efficient Use of the Query Optimizer for Automated Physical Design |
2007 |
VLDB |
8.6836615e-05 |
| 5,055 |
Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications |
2009 |
VLDB |
5.72317e-05 |
| 5,343 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
5.5582234e-05 |
| 5,673 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
5.3789277e-05 |
| 6,277 |
Uncertainty Aware Query Execution Time Prediction |
2014 |
VLDB |
5.1260947e-05 |
| 6,328 |
A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies |
2024 |
VLDB |
5.1034426e-05 |
| 6,364 |
ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning |
2022 |
SIGMOD |
5.0895007e-05 |
| 6,753 |
Breaking It Down: An In-depth Study of Index Advisors |
2024 |
VLDB |
4.9345582e-05 |
| 7,332 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
4.7553758e-05 |
| 8,043 |
DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning |
2022 |
VLDB |
4.5954398e-05 |
| 8,198 |
Leveraging Dynamic and Heterogeneous Workload Knowledge to Boost the Performance of Index Advisors |
2024 |
VLDB |
4.556207e-05 |
| 9,931 |
Wii: Dynamic Budget Reallocation In Index Tuning |
2024 |
SIGMOD |
4.2469394e-05 |
| 10,552 |
Esc: An Early-Stopping Checker for Budget-aware Index Tuning |
2025 |
VLDB |
4.1905499e-05 |
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