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Learned Index Benefits: Machine Learning Based Index Performance Estimation
Summary: End-to-end ML-based index-benefit estimator replaces what-if plans for candidate indexes. Feature extraction/encoding, attention for index interactions, and transfer learning for cross-DB adaptation; outperforms what-if estimates in accuracy and efficiency, improving index selection.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 12906
- Venue
- VLDB
- Year
- 2022
- Pagerank
- 5.5582234e-05
- Overall Rank
- 5,343 | 62.87%
- DOI
-
10.14778/3565838.3565848
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 14 of 14 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 6,753 |
Breaking It Down: An In-depth Study of Index Advisors |
2024 |
VLDB |
4.9345582e-05 |
| 7,332 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
4.7553758e-05 |
| 8,003 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6049527e-05 |
| 8,079 |
Accelerating String-key Learned Index Structures via Memoization-based Incremental Training |
2024 |
VLDB |
4.5873372e-05 |
| 9,931 |
Wii: Dynamic Budget Reallocation In Index Tuning |
2024 |
SIGMOD |
4.2469394e-05 |
| 9,982 |
Does A Fish Need a Bicycle? The Case for On-Chip NPUs in DBMS |
2026 |
CIDR |
4.1905499e-05 |
| 10,032 |
Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,125 |
Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,205 |
RIB: Robust Learning-based Index Benefit Estimation |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,217 |
This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,300 |
TATA: An Efficient Framework for Task Transfer in Query Plan Representation |
2026 |
VLDB |
4.1905499e-05 |
| 10,507 |
PLM4NDV: Minimizing Data Access for Number of Distinct Values Estimation with Pre-trained Language Models |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,552 |
Esc: An Early-Stopping Checker for Budget-aware Index Tuning |
2025 |
VLDB |
4.1905499e-05 |
| 10,780 |
Automatic Indexing in Oracle |
2025 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 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 |
| 203 |
Learned Cardinalities: Estimating Correlated Joins with Deep Learning |
2019 |
CIDR |
0.00034868567 |
| 258 |
DB2 Design Advisor: Integrated Automatic Physical Database Design |
2004 |
VLDB |
0.00030196528 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 517 |
AutoAdmin "What-if" Index Analysis Utility |
1998 |
SIGMOD |
0.00021193179 |
| 634 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018844568 |
| 659 |
The Making of TPC-DS |
2006 |
VLDB |
0.00018514913 |
| 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 |
| 1,756 |
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 |
| 2,050 |
Automatically Indexing Millions of Databases in Microsoft Azure SQL Database |
2019 |
SIGMOD |
9.6883066e-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 |
| 3,402 |
Query Optimizers: Time to Rethink the Contract? |
2009 |
SIGMOD |
7.134261e-05 |
| 3,661 |
Database Tuning Advisor for Microsoft SQL Server 2005: Demo |
2005 |
SIGMOD |
6.8680306e-05 |
| 5,055 |
Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications |
2009 |
VLDB |
5.72317e-05 |
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