Machine Learning for Databases: Foundations, Paradigms, and Open problems

Abstract

This tutorial delves into the burgeoning field of Machine Learning for Databases (ML4DB), highlighting its recent progress and the challenges impeding its integration into industrial-grade database management systems. We systematically explore three key themes: the exploration of foundations in ML4DB and their potential for diverse applications, the two paradigms in ML4DB, i.e., using machine learning as a replacement versus enhancement of traditional database components, and the critical open challenges such as improving model efficiency and addressing data shifts. Through an in-depth analysis, including a survey of recent works in major database conferences, this tutorial encapsulates the current state of ML4DB, as well as charts a roadmap for its future development and wider adoption in practical database environments.