Database Meets Deep Learning: Challenges and Opportunities

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...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2020-01
Main Authors: Wang, Wei, Zhang, Meihui, Chen, Gang, Jagadish, H V, Beng Chin Ooi, Kian-Lee, Tan
Format: Article
Language:English
Subjects:
Deep learning
Image classification
Machine learning
Object recognition
Speech recognition
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1906.08986