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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
13256
Venue
VLDB
Year
2023
Pagerank
6.1749098e-05
Overall Rank
5,558 | 61.87%
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
7,076 Refactoring Index Tuning Process with Benefit Estimation 2024 VLDB 5.7098893e-05
7,846 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.5331459e-05
9,615 Wii: Dynamic Budget Reallocation In Index Tuning 2024 SIGMOD 5.2436464e-05
9,949 UniView: A Unified Autonomous Materialized View Management System for Various Databases 2024 VLDB 5.1915905e-05
10,327 Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads 2026 SIGMOD 5.093636e-05
10,413 Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] 2026 SIGMOD 5.093636e-05
10,815 Esc: An Early-Stopping Checker for Budget-aware Index Tuning 2025 VLDB 5.093636e-05
11,232 SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads 2024 VLDB 5.093636e-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
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
43 The Case for Learned Index Structures 2018 SIGMOD 0.00046060254
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
87 Automated Selection of Materialized Views and Indexes for SQL Databases 2000 VLDB 0.00035281619
156 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028636811
184 DB2 Design Advisor: Integrated Automatic Physical Database Design 2004 VLDB 0.00026256101
234 Self-Driving Database Management Systems 2017 CIDR 0.00023810722
259 Database Cracking 2007 CIDR 0.00023119313
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
387 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00019442332
461 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00018068441
476 The Making of TPC-DS 2006 VLDB 0.00017860667
524 Automatic SQL Tuning in Oracle 10g 2004 VLDB 0.00017120666
559 Optimizing Queries Using Materialized Views: A Practical, Scalable Solution 2001 SIGMOD 0.00016528822
563 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001650812
768 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.00014173242
790 FITing-Tree: A Data-aware Index Structure 2019 SIGMOD 0.0001401445
929 Materialized View Selection and Maintenance Using Multi-Query Optimization 2001 SIGMOD 0.00013152103
1,070 NoDB: Efficient Query Execution on Raw Data Files 2012 SIGMOD 0.0001232307
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011361878
1,481 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010644613
1,548 Automatically Indexing Millions of Databases in Microsoft Azure SQL Database 2019 SIGMOD 0.00010392475
1,616 The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models 2018 SIGMOD 0.00010213691
1,815 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2018 VLDB 9.6894541e-05
1,904 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5040429e-05
2,364 To Tune or not to Tune? A Lightweight Physical Design Alerter 2006 VLDB 8.6869645e-05
2,499 Learning a Partitioning Advisor for Cloud Databases 2020 SIGMOD 8.4993549e-05
2,812 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 8.0979597e-05
3,051 Towards a Hands-Free Query Optimizer through Deep Learning 2019 CIDR 7.8121919e-05
3,926 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.0128068e-05
4,987 Releasing Cloud Databases from the Chains of Performance Prediction Models 2017 CIDR 6.4112736e-05
5,359 QUIET: Continuous Query-driven Index Tuning 2003 VLDB 6.2492097e-05
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