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ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning

Summary: Adaptive query engine for worst-case optimal joins that searches attribute-order space (not relation order) via episodic trials and reinforcement learning to balance exploration vs. exploitation. Novel data structure reuses processed input to avoid redundant work and converge quickly to near-optimal orders under skew/correlation. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
h5499855f9e141158
Venue
VLDB
Year
2023
Pagerank
5.5411636e-05
Overall Rank
7,367 | 50.47%
DOI
10.14778/3611479.3611489

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{wang_vldb23,
        title = {{ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning}},
        author = {Wang, Junxiong and Trummer, Immanuel and Kara, Ahmet and Olteanu, Dan},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {11},
        pages = {2805--2817},
        doi = {10.14778/3611479.3611489},
        url = {https://doi.org/10.14778/3611479.3611489},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
9,950 HoneyComb: A Parallel Worst-Case Optimal Join on Multicores 2025 SIGMOD 5.1038322e-05
10,398 The Space-Time Complexity of Sum-Product Queries 2026 PODS 4.9793485e-05
10,854 Worst-Case Optimal BGPs on Temporal Graphs 2026 VLDB 4.9793485e-05
11,175 AJOSC: Adaptive Join Order Selection for Continuous Queries 2025 SIGMOD 4.9793485e-05
11,806 Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning 2023 VLDB 4.9793485e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 23 of 23 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1 Access Path Selection in a Relational Database Management System 1979 SIGMOD 0.0023947656
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061066921
34 The Design Of Postgres 1986 SIGMOD 0.00049142315
53 Eddies: Continuously Adaptive Query Processing 2000 SIGMOD 0.00040860054
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
208 EmptyHeaded: A Relational Engine for Graph Processing 2016 SIGMOD 0.00024884544
315 Worst-Case Optimal Join Algorithms: Techniques, Results, and Open Problems 2018 PODS 0.00021246
362 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019989474
437 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00018315867
466 Design and Implementation of the LogicBlox System 2015 SIGMOD 0.00017773029
521 Learning Linear Regression Models over Factorized Joins 2016 SIGMOD 0.00016929744
849 Aggregation and Ordering in Factorised Databases 2013 VLDB 0.00013504405
1,250 DB-BERT: A Database Tuning Tool that "Reads the Manual" 2022 SIGMOD 0.00011339256
1,289 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011162479
1,596 Adopting Worst-Case Optimal Joins in Relational Database Systems 2020 VLDB 0.00010127607
1,603 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019 SIGMOD 0.00010097649
3,645 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.1397796e-05
4,793 Permutable Compiled Queries: Dynamically Adapting Compiled Queries without Recompiling 2021 VLDB 6.4127583e-05
4,968 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 6.3348803e-05
8,232 Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning 2021 SIGMOD 5.3721763e-05
8,531 GRainDB: A Relational-core Graph-Relational DBMS 2022 CIDR 5.3218647e-05
8,850 SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms 2023 VLDB 5.2645282e-05
11,806 Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning 2023 VLDB 4.9793485e-05
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