In-Machine-Learning Database: Reimagining Deep Learning with Old-School SQL

TL;DR

This paper explores implementing deep learning algorithms using traditional SQL, viewing database tables as array-like structures, and leveraging database techniques to inspire new models and approaches in machine learning.

Contribution

It demonstrates how SQL can be used to express deep learning operations, bridging database methods with neural network computations for novel model development.

Findings

SQL can represent deep learning operations effectively

Database sparsity concepts can be applied to neural networks

Potential for new models inspired by database techniques

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, in a sense 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 generalisations 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, 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 integerating machine learning into databases.