Deep Learning Through the Lens of Classical SQL

Date
2021-01-05
Authors
Du, Len
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
6924
Ending Page
Alternative Title
Abstract
In-database machine learning has been very popular, almost being a cliche. However, can we do it the other way around? In this work, we say “yes” by applying plain old SQL to Deep Learning (DL), in a sense, hypothetically implementing deep learning algorithms with SQL. Most deep learning frameworks, as well as generic machine learning ones, share a de facto standard of multidimensional array operations, underneath fancier infrastructure such as automatic differentiation. As SQL tables can be regarded as generalizations of (multi-dimensional) arrays, we have found a way to express common deep learning operations in SQL, encouraging a different way of thinking and thus potentially novel models. In particular, one of the latest trend in deep learning was the introduction of sparsity in the name of Graph Convolutional Networks (GCNs), whereas we take sparsity almost for granted in the database world. As both databases and machine learning involve transformation of datasets, we hope this work can inspire further works utilizing the large body of existing wisdom, algorithms and technologies in the database field to advance the state-of-the-art in machine learning, rather than merely integrating machine learning into databases.
Description
Keywords
Computational Intelligence and State-of-the-Art Data Analytics, data analytics, database, deep learning, graph convolutional network, machine learning
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 54th Hawaii International Conference on System Sciences
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.