70 citations · 75 across the 5 of their papers we have counts for
6 papers
ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Join Algorithms via Reinforcement Learning
Junxiong Wang, Immanuel Trummer, Ahmet Kara +1
The performance of worst-case optimal join algorithms depends on the order in which the join attributes are processed. Selecting good orders before query execution is hard, due to…
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
Kaiwen Wang, Junxiong Wang, Yueying Li +3
Join order selection (JOS) is the problem of ordering join operations to minimize total query execution cost and it is the core NP-hard combinatorial optimization problem of query…
Procrastinated Tree Search: Black-box Optimization with Delayed, Noisy, and Multi-Fidelity Feedback
Junxiong Wang, Debabrota Basu, Immanuel Trummer
In black-box optimization problems, we aim to maximize an unknown objective function, where the function is only accessible through feedbacks of an evaluation or simulation oracle.…
UDO: Universal Database Optimization using Reinforcement Learning
Junxiong Wang, Immanuel Trummer, Debabrota Basu
UDO is a versatile tool for offline tuning of database systems for specific workloads. UDO can consider a variety of tuning choices, reaching from picking transaction code variants…
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning
Immanuel Trummer, Junxiong Wang, Deepak Maram +3
SkinnerDB is designed from the ground up for reliable join ordering. It maintains no data statistics and uses no cost or cardinality models. Instead, it uses reinforcement learning…
A Hybrid Solution to improve Iteration Efficiency in the Distributed Learning
Junxiong Wang, Hongzhi Wang, Chenxu Zhao
Currently, many machine learning algorithms contain lots of iterations. When it comes to existing large-scale distributed systems, some slave nodes may break down or have lower eff…