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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
6946
Venue
SIGMOD
Year
2024
Pagerank
4.2469394e-05
Overall Rank
9,931 | 30.98%
DOI
10.1145/3654985

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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
71 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059446482
237 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00031727601
329 Neo: A Learned Query Optimizer 2019 VLDB 0.00027301488
517 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00021193179
804 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001643674
876 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00015660534
1,017 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00014627121
1,018 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.00014626746
1,756 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00010659753
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
2,467 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 8.7264908e-05
2,479 Efficient Use of the Query Optimizer for Automated Physical Design 2007 VLDB 8.6836615e-05
3,167 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 7.4561078e-05
3,623 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 6.9017341e-05
3,819 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 6.7267885e-05
4,075 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 6.4699689e-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
5,645 Database Workload Characterization with Query Plan Encoders 2022 VLDB 5.3928148e-05
5,673 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 5.3789277e-05
5,925 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 5.2669029e-05
6,277 Uncertainty Aware Query Execution Time Prediction 2014 VLDB 5.1260947e-05
6,364 ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning 2022 SIGMOD 5.0895007e-05
8,043 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 4.5954398e-05
9,930 Wred: Workload Reduction for Scalable Index Tuning 2024 SIGMOD 4.2469394e-05
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