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Wii: Dynamic Budget Reallocation In Index Tuning

Summary: Wii addresses budgeted index tuning by dynamically reallocating what-if calls away from QCPs whose optimizer costs can be safely derived, avoiding spurious expensive evaluations. Lightweight and plug-in compatible with existing enumeration methods, it improves final configurations by spending budget where cost derivation is less accurate. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID
h2781cf0a18c0d163
Venue
SIGMOD
Year
2024
Pagerank
5.1260323e-05
Overall Rank
9,790 | 34.18%
DOI
10.1145/3654985

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{wang_sigmod24,
        title = {{Wii: Dynamic Budget Reallocation In Index Tuning}},
        author = {Wang, Xiaoying and Wu, Wentao and Wang, Chi and Narasayya, Vivek and Chaudhuri, Surajit},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654985},
        url = {https://dl.acm.org/doi/10.1145/3654985},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

Rank Citing Paper Year Venue Pagerank
9,133 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 5.2229655e-05
10,489 On Self-Designing Learned Indexes 2026 SIGMOD 4.9793485e-05
10,604 Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] 2026 SIGMOD 4.9793485e-05
10,681 RIB: Robust Learning-based Index Benefit Estimation 2026 SIGMOD 4.9793485e-05
10,912 Tuning the Lookahead Distance for PostgreSQL Asynchronous IO 2026 VLDB 4.9793485e-05
10,918 Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server 2026 VLDB 4.9793485e-05
11,224 Esc: An Early-Stopping Checker for Budget-aware Index Tuning 2025 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 26 of 26 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
378 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00019549382
461 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017829982
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016403151
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.0001481781
751 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.0001425375
1,257 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011310561
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
2,023 Efficient Use of the Query Optimizer for Automated Physical Design 2007 VLDB 9.1669658e-05
2,250 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7533306e-05
2,834 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9560627e-05
2,885 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9094988e-05
3,519 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 7.2389387e-05
4,607 Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications 2009 VLDB 6.5037336e-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
5,194 Database Workload Characterization with Query Plan Encoders 2022 VLDB 6.2353557e-05
5,630 ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning 2022 SIGMOD 6.0582762e-05
5,656 Uncertainty Aware Query Execution Time Prediction 2014 VLDB 6.0488629e-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
10,297 Wred: Workload Reduction for Scalable Index Tuning 2024 SIGMOD 5.0430432e-05
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