  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.
