Database Meets Deep Learning: Challenges and Opportunities

TL;DR

This paper explores the intersection of databases and deep learning, discussing challenges, opportunities, and potential improvements from both perspectives to advance data-driven applications.

Contribution

It provides a comprehensive analysis of research problems and opportunities at the intersection of database systems and deep learning techniques.

Findings

Identifies key challenges in integrating databases with deep learning.

Highlights potential benefits of applying deep learning to database applications.

Suggests future research directions for combining these fields.

Abstract

Deep learning has recently become very popular on account of its incredible success in many complex data-driven applications, such as image classification and speech recognition. The database community has worked on data-driven applications for many years, and therefore should be playing a lead role in supporting this new wave. However, databases and deep learning are different in terms of both techniques and applications. In this paper, we discuss research problems at the intersection of the two fields. In particular, we discuss possible improvements for deep learning systems from a database perspective, and analyze database applications that may benefit from deep learning techniques.