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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
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
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 |
| 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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| 11,418 |
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SIGMOD |
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| 10,552 |
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| 8,043 |
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VLDB |
4.5954398e-05 |
| 5,673 |
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SIGMOD |
5.3789277e-05 |