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: 7708
Venue: SIGMOD
Year: 2026
Pagerank: 5.093636e-05
Overall Rank: 10,494 | 28.01%
DOI: 10.1145/3786691

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Authors

1. Sifan Chen (Fudan University)
2. Chenning Wu (Fudan University)
3. Yinan Jing (Fudan University)
4. Wentao Wu (Microsoft)
5. Zhenying He (Fudan University)
6. Kai Zhang (Fudan University)
7. X. Sean Wang (Fudan University)

BibTeX Citation

@inproceedings{chen_sigmod26,
        title = {{RIB: Robust Learning-based Index Benefit Estimation}},
        author = {Chen, Sifan and Wu, Chenning and Jing, Yinan and Wu, Wentao and He, Zhenying and Zhang, Kai and Wang, X. Sean},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3786691},
        url = {https://dl.acm.org/doi/10.1145/3786691},
        year = {2026}
}

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
156	An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server	1997	VLDB	0.00028636811
387	AutoAdmin "What-if" Index Analysis Utility	1998	SIGMOD	0.00019442332
566	Towards a Robust Query Optimizer: A Principled and Practical Approach	2005	SIGMOD	0.00016436005
1,279	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00011361878
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
1,795	Least Expected Cost Query Optimization: What Can We Expect?	2002	PODS	9.738718e-05
1,997	Efficient Use of the Query Optimizer for Automated Physical Design	2007	VLDB	9.3378162e-05
4,550	Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications	2009	VLDB	6.635765e-05
4,643	Learned Index Benefits: Machine Learning Based Index Performance Estimation	2022	VLDB	6.5907466e-05
5,091	Budget-aware Index Tuning with Reinforcement Learning	2022	SIGMOD	6.3669569e-05
5,537	Uncertainty Aware Query Execution Time Prediction	2014	VLDB	6.1820087e-05
5,767	A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies	2024	VLDB	6.0945741e-05
5,869	ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning	2022	SIGMOD	6.0610922e-05
6,327	Breaking It Down: An In-depth Study of Index Advisors	2024	VLDB	5.9124005e-05
7,076	Refactoring Index Tuning Process with Benefit Estimation	2024	VLDB	5.7098893e-05
7,750	DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning	2022	VLDB	5.5523652e-05
8,076	Leveraging Dynamic and Heterogeneous Workload Knowledge to Boost the Performance of Index Advisors	2024	VLDB	5.4927414e-05
9,615	Wii: Dynamic Budget Reallocation In Index Tuning	2024	SIGMOD	5.2436464e-05
10,815	Esc: An Early-Stopping Checker for Budget-aware Index Tuning	2025	VLDB	5.093636e-05

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