Machine Learning over Static and Dynamic Relational Data

TL;DR

This tutorial discusses recent methods for training machine learning models on relational data, emphasizing exploiting data structure and database techniques to enhance performance, and outlines future research directions.

Contribution

It synthesizes recent advances in applying database principles to machine learning over relational data, highlighting three key strategies for performance improvement.

Findings

Relational data structure can be exploited to improve ML performance

Transforming ML problems into database problems enhances efficiency

Database engineering tools are valuable for ML over relational data

Abstract

This tutorial overviews principles behind recent works on training and maintaining machine learning models over relational data, with an emphasis on the exploitation of the relational data structure to improve the runtime performance of the learning task. The tutorial has the following parts: 1) Database research for data science 2) Three main ideas to achieve performance improvements 2.1) Turn the ML problem into a DB problem 2.2) Exploit structure of the data and problem 2.3) Exploit engineering tools of a DB researcher 3) Avenues for future research