A Survey on Deep Reinforcement Learning for Data Processing and
Analytics
- URL: http://arxiv.org/abs/2108.04526v2
- Date: Wed, 11 Aug 2021 12:22:36 GMT
- Title: A Survey on Deep Reinforcement Learning for Data Processing and
Analytics
- Authors: Qingpeng Cai, Can Cui, Yiyuan Xiong, Wei Wang, Zhongle Xie and Meihui
Zhang
- Abstract summary: We provide a review of recent works focusing on utilizing deep reinforcement learning to improve data processing and analytics.
First, we present an introduction to key concepts, theories, and methods in deep reinforcement learning.
Next, we discuss deep reinforcement learning deployment on database systems, facilitating data processing and analytics.
- Score: 14.88856391719732
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Data processing and analytics are fundamental and pervasive. Algorithms play
a vital role in data processing and analytics where many algorithm designs have
incorporated heuristics and general rules from human knowledge and experience
to improve their effectiveness. Recently, reinforcement learning, deep
reinforcement learning (DRL) in particular, is increasingly explored and
exploited in many areas because it can learn better strategies in complicated
environments it is interacting with than statically designed algorithms.
Motivated by this trend, we provide a comprehensive review of recent works
focusing on utilizing deep reinforcement learning to improve data processing
and analytics. First, we present an introduction to key concepts, theories, and
methods in deep reinforcement learning. Next, we discuss deep reinforcement
learning deployment on database systems, facilitating data processing and
analytics in various aspects, including data organization, scheduling, tuning,
and indexing. Then, we survey the application of deep reinforcement learning in
data processing and analytics, ranging from data preparation, natural language
interface to healthcare, fintech, etc. Finally, we discuss important open
challenges and future research directions of using deep reinforcement learning
in data processing and analytics.
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