Machine learning over static and dynamic 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