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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
5.1066866e-05
Overall Rank
9,829 | 34.12%
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
9,154 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 5.2050937e-05
10,566 On Self-Designing Learned Indexes 2026 SIGMOD 4.9605343e-05
10,676 Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] 2026 SIGMOD 4.9605343e-05
10,753 RIB: Robust Learning-based Index Benefit Estimation 2026 SIGMOD 4.9605343e-05
10,982 Tuning the Lookahead Distance for PostgreSQL Asynchronous IO 2026 VLDB 4.9605343e-05
10,988 Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server 2026 VLDB 4.9605343e-05
11,288 Esc: An Early-Stopping Checker for Budget-aware Index Tuning 2025 VLDB 4.9605343e-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 0.00061463175
142 Neo: A Learned Query Optimizer 2019 VLDB 0.00029114937
151 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028611216
380 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00019530546
453 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001801482
545 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001657599
676 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00014838681
756 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.00014209974
1,261 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011289724
1,282 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011197535
1,400 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010759064
1,823 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5457638e-05
2,036 Efficient Use of the Query Optimizer for Automated Physical Design 2007 VLDB 9.1356845e-05
2,169 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.9151935e-05
2,619 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.1985665e-05
2,828 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9476509e-05
3,384 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 7.3392425e-05
4,629 Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications 2009 VLDB 6.4794914e-05
4,726 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.4329878e-05
4,990 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 6.3120056e-05
5,211 Database Workload Characterization with Query Plan Encoders 2022 VLDB 6.2141929e-05
5,645 ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning 2022 SIGMOD 6.0354456e-05
5,670 Uncertainty Aware Query Execution Time Prediction 2014 VLDB 6.0266818e-05
5,687 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.0201937e-05
7,921 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 5.4098206e-05
9,983 Wred: Workload Reduction for Scalable Index Tuning 2024 SIGMOD 5.0860177e-05
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