Learning Models over Relational Data: A Brief Tutorial

TL;DR

This tutorial discusses efficient methods for learning models over relational data by leveraging database techniques to avoid costly data materialization, focusing on query-based computation and structural properties.

Contribution

It advocates for a first-principles, database-centric approach to learning over relational data, highlighting recent techniques to improve performance without full data materialization.

Findings

Transforming learning tasks into database queries reduces costs.

Structural properties like hypertree width enable optimized query execution.

Techniques such as factorized computation and parallelization enhance efficiency.

Abstract

This tutorial overviews the state of the art in learning models over relational databases and makes the case for a first-principles approach that exploits recent developments in database research. The input to learning classification and regression models is a training dataset defined by feature extraction queries over relational databases. The mainstream approach to learning over relational data is to materialize the training dataset, export it out of the database, and then learn over it using a statistical package. This approach can be expensive as it requires the materialization of the training dataset. An alternative approach is to cast the machine learning problem as a database problem by transforming the data-intensive component of the learning task into a batch of aggregates over the feature extraction query and by computing this batch directly over the input database. The tutorial highlights a variety of techniques developed by the database theory and systems communities to improve the performance of the learning task. They rely on structural properties of the relational data and of the feature extraction query, including algebraic (semi-ring), combinatorial (hypertree width), statistical (sampling), or geometric (distance) structure. They also rely on factorized computation, code specialization, query compilation, and parallelization.