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Refactoring Index Tuning Process with Benefit Estimation

Summary: RIBE refactors index tuning by removing redundant workload queries and using plan statistics with an attention-based model to estimate index benefits. It skips costly what-if calls without DBMS changes, delivering 1–2 orders of magnitude speedups and better tuning quality. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
hdac866b4c342b6ad
Venue
VLDB
Year
2024
Pagerank
5.5834823e-05
Overall Rank
7,217 | 51.48%
DOI
10.14778/3654621.3654622

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{yu_vldb24,
        title = {{Refactoring Index Tuning Process with Benefit Estimation}},
        author = {Yu, Tao and Zou, Zhaonian and Sun, Weihua and Yan, Yu},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {7},
        pages = {1528--1541},
        doi = {10.14778/3654621.3654622},
        url = {https://doi.org/10.14778/3654621.3654622},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

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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
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061066921
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
151 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028672526
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021167555
362 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019989474
378 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00019549382
461 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017829982
512 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017050173
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016403151
751 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.0001425375
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202282
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011226878
1,397 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010789242
1,815 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5791737e-05
2,004 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.2065719e-05
2,250 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7533306e-05
2,842 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.949193e-05
3,327 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.4207879e-05
3,815 Comprehensive and Efficient Workload Compression 2021 VLDB 7.0075744e-05
4,711 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.4573842e-05
4,968 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 6.3348803e-05
5,630 ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning 2022 SIGMOD 6.0582762e-05
5,674 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.0430252e-05
7,900 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 5.4303143e-05
9,641 CEDA: Learned Cardinality Estimation with Domain Adaptation 2023 VLDB 5.1456789e-05
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