SQL for SRL: Structure Learning Inside a Database System

TL;DR

This paper proposes using SQL and relational algebra within database systems to facilitate scalable, reliable statistical-relational structure learning, demonstrated through the FACTORBASE system and empirical benchmarks.

Contribution

It introduces a novel approach to statistical-relational learning by integrating structure learning directly into database systems using SQL, managing models as first-class citizens.

Findings

FACTORBASE enables fast, modular model development.

Leveraging database capabilities improves scalability.

Empirical results on six benchmarks show effective structure learning.

Abstract

The position we advocate in this paper is that relational algebra can provide a unified language for both representing and computing with statistical-relational objects, much as linear algebra does for traditional single-table machine learning. Relational algebra is implemented in the Structured Query Language (SQL), which is the basis of relational database management systems. To support our position, we have developed the FACTORBASE system, which uses SQL as a high-level scripting language for statistical-relational learning of a graphical model structure. The design philosophy of FACTORBASE is to manage statistical models as first-class citizens inside a database. Our implementation shows how our SQL constructs in FACTORBASE facilitate fast, modular, and reliable program development. Empirical evidence from six benchmark databases indicates that leveraging database system capabilities achieves scalable model structure learning.