Leveraging Dynamic and Heterogeneous Workload Knowledge to Boost the Performance of Index Advisors
Summary: BALANCE handles dynamic, heterogeneous workloads by training lightweight index advisors (LIAs) on sequential similar-workload chunks, using policy-transfer to reuse policies and self-supervised contrastive embeddings for compact workload representations. Improves SWIRL by 10.03% while reducing training overhead by 35.7%. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zijia Wang
- 2. Haoran Liu
- 3. Chen Lin
- 4. Zhifeng Bao
- 5. Guoliang Li
- 6. Tianqing Wang
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,618 | A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning Enhanced Approach | 2025 | SIGMOD | 4.3173366e-05 |
| 10,032 | Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads | 2026 | SIGMOD | 4.1945683e-05 |
| 10,205 | RIB: Robust Learning-based Index Benefit Estimation | 2026 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 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 | 0.00059038975 |
| 237 | An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server | 1997 | VLDB | 0.00031726304 |
| 516 | AutoAdmin "What-if" Index Analysis Utility | 1998 | SIGMOD | 0.00021196031 |
| 659 | The Making of TPC-DS | 2006 | VLDB | 0.00018500853 |
| 716 | Query-based Workload Forecasting for Self-Driving Database Management Systems | 2018 | SIGMOD | 0.00017723171 |
| 874 | Index Selection in a Self-Adaptive Data Base Management System | 1976 | SIGMOD | 0.00015728533 |
| 1,017 | Automatic Physical Database Tuning: A Relaxation-based Approach | 2005 | SIGMOD | 0.00014634307 |
| 2,020 | Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms | 2020 | VLDB | 9.762624e-05 |
| 5,686 | Budget-aware Index Tuning with Reinforcement Learning | 2022 | SIGMOD | 5.3712312e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,748 | Benchmarking Adaptive Multidimensional Indices | 2025 | VLDB | 4.1945683e-05 |
| 5,074 | Learned Index: A Comprehensive Experimental Evaluation | 2023 | VLDB | 5.7175726e-05 |
| 6,819 | Workload-Aware Indexing of Continuously Moving Objects | 2009 | VLDB | 4.9158166e-05 |
| 4,128 | Are Updatable Learned Indexes Ready? | 2022 | VLDB | 6.4292373e-05 |
| 1,460 | Benchmarking Learned Indexes | 2021 | VLDB | 0.00011887068 |
| 5,686 | Budget-aware Index Tuning with Reinforcement Learning | 2022 | SIGMOD | 5.3712312e-05 |
| 10,172 | HIRE: A Hybrid Learned Index for Robust and Efficient Performance under Mixed Workloads | 2026 | SIGMOD | 4.1945683e-05 |
| 10,087 | High Performance or Low Memory? An Updatable Learned Index Framework for Time-Space Tradeoff | 2026 | SIGMOD | 4.1945683e-05 |
| 5,337 | Learned Index Benefits: Machine Learning Based Index Performance Estimation | 2022 | VLDB | 5.5635208e-05 |
| 6,750 | Breaking It Down: An In-depth Study of Index Advisors | 2024 | VLDB | 4.9392771e-05 |