FactorBase: SQL for Learning A Multi-Relational Graphical Model

TL;DR

FactorBase is an SQL-based framework that enables scalable learning of multi-relational Bayesian network models directly within relational databases, supporting complex data analysis tasks.

Contribution

It introduces a novel SQL-based system supporting multi-relational model learning, extending previous inference-focused systems to include model discovery capabilities.

Findings

Supports learning a first-order Bayesian network for entire databases

Facilitates fast and scalable model discovery using SQL constructs

Demonstrates effectiveness on six benchmark databases

Abstract

We describe FactorBase, a new SQL-based framework that leverages a relational database management system to support multi-relational model discovery. A multi-relational statistical model provides an integrated analysis of the heterogeneous and interdependent data resources in the database. We adopt the BayesStore design philosophy: statistical models are stored and managed as first-class citizens inside a database. Whereas previous systems like BayesStore support multi-relational inference, FactorBase supports multi-relational learning. A case study on six benchmark databases evaluates how our system supports a challenging machine learning application, namely learning a first-order Bayesian network model for an entire database. Model learning in this setting has to examine a large number of potential statistical associations across data tables. Our implementation shows how the SQL constructs in FactorBase facilitate the fast, modular, and reliable development of highly scalable model learning systems.