A Survey on Deep Reinforcement Learning for Data Processing and Analytics

TL;DR

This survey reviews recent advances in applying deep reinforcement learning to enhance data processing and analytics across various domains, highlighting key concepts, applications, challenges, and future directions.

Contribution

It provides a comprehensive overview of how DRL is utilized in data systems and analytics, integrating recent research and identifying open challenges.

Findings

DRL improves data organization, scheduling, and indexing.

Applications span natural language processing, healthcare, and fintech.

Highlights open challenges and future research directions.

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 DRL to improve data processing and analytics. First, we present an introduction to key concepts, theories, and methods in DRL. Next, we discuss DRL 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 DRL in data processing and analytics, ranging from data preparation, natural language processing to healthcare, fintech, etc. Finally, we discuss important open challenges and future research directions of using DRL in data processing and analytics.