Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning

Summary: ADOPT uses episodic execution plus reinforcement learning to pick attribute orders for worst-case optimal joins. A shared processed-data index prevents redundant work across episodes, enabling fast convergence to near-optimal orders and outperforming WCOJ baselines on complex/skewed queries. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 13274
Venue: VLDB
Year: 2023
Pagerank: 4.1945683e-05
Overall Rank: 11,298 | 21.41%
DOI: 10.14778/3611540.3611629

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Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,026	ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning	2023	VLDB	4.6030518e-05

Outgoing Citations (Sorted by Pagerank)

Showing 5 of 5 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
44	The Design Of Postgres	1986	SIGMOD	0.00071838587
342	EmptyHeaded: A Relational Engine for Graph Processing	2016	SIGMOD	0.00026795977
613	Design and Implementation of the LogicBlox System	2015	SIGMOD	0.00019181325
2,219	SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning	2019	SIGMOD	9.2623533e-05
8,026	ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning	2023	VLDB	4.6030518e-05

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