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HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning

Summary: HMAB: a self-driving hierarchical multi-armed-bandit framework that jointly selects indices and materialized views, avoiding full combinatorial search via hierarchical pruning and strategic exploration using direct performance observations. Offers provable expected-performance guarantees and achieves up to 96% improvement over a state-of-the-art commercial physical-design tool on industrial benchmarks. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
had41d196f655a6e7
Venue
VLDB
Year
2023
Pagerank
6.0430252e-05
Overall Rank
5,674 | 61.86%
DOI
10.14778/3565816.3565824

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{perera_vldb23,
        title = {{HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning}},
        author = {Perera, R. Malinga and Oetomo, Bastian and Rubinstein, Benjamin I. P. and Borovica-Gajic, Renata},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {2},
        pages = {216--229},
        doi = {10.14778/3565816.3565824},
        url = {https://doi.org/10.14778/3565816.3565824},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

Rank Citing Paper Year Venue Pagerank
7,217 Refactoring Index Tuning Process with Benefit Estimation 2024 VLDB 5.5834823e-05
7,977 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.4142519e-05
9,520 UniView: A Unified Autonomous Materialized View Management System for Various Databases 2024 VLDB 5.1673153e-05
9,790 Wii: Dynamic Budget Reallocation In Index Tuning 2024 SIGMOD 5.1260323e-05
10,532 Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads 2026 SIGMOD 4.9793485e-05
10,604 Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] 2026 SIGMOD 4.9793485e-05
11,224 Esc: An Early-Stopping Checker for Budget-aware Index Tuning 2025 VLDB 4.9793485e-05
11,568 SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads 2024 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 32 of 32 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 0.00061066921
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046284649
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035864347
88 Automated Selection of Materialized Views and Indexes for SQL Databases 2000 VLDB 0.00035351639
151 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028672526
187 DB2 Design Advisor: Integrated Automatic Physical Database Design 2004 VLDB 0.0002592488
224 Self-Driving Database Management Systems 2017 CIDR 0.00024013745
253 Database Cracking 2007 CIDR 0.00023042111
362 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019989474
378 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00019549382
460 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00017842695
479 The Making of TPC-DS 2006 VLDB 0.00017622471
529 Automatic SQL Tuning in Oracle 10g 2004 VLDB 0.00016866092
553 Optimizing Queries Using Materialized Views: A Practical, Scalable Solution 2001 SIGMOD 0.0001652591
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016403151
751 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.0001425375
779 FITing-Tree: A Data-aware Index Structure 2019 SIGMOD 0.00014030069
923 Materialized View Selection and Maintenance Using Multi-Query Optimization 2001 SIGMOD 0.00013064753
1,069 NoDB: Efficient Query Execution on Raw Data Files 2012 SIGMOD 0.00012185253
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011226878
1,397 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010789242
1,516 Automatically Indexing Millions of Databases in Microsoft Azure SQL Database 2019 SIGMOD 0.00010402594
1,605 The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models 2018 SIGMOD 0.00010093796
1,800 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2018 VLDB 9.6093317e-05
1,815 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5791737e-05
2,373 To Tune or not to Tune? A Lightweight Physical Design Alerter 2006 VLDB 8.5606392e-05
2,478 Learning a Partitioning Advisor for Cloud Databases 2020 SIGMOD 8.4079121e-05
2,842 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.949193e-05
3,060 Towards a Hands-Free Query Optimizer through Deep Learning 2019 CIDR 7.6928239e-05
3,645 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.1397796e-05
5,087 Releasing Cloud Databases from the Chains of Performance Prediction Models 2017 CIDR 6.2809904e-05
5,468 QUIET: Continuous Query-driven Index Tuning 2003 VLDB 6.1192263e-05
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