FactorBase: SQL for Learning A Multi-Relational Graphical Model
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.
Keywords
Cite
@article{arxiv.1508.02428,
title = {FactorBase: SQL for Learning A Multi-Relational Graphical Model},
author = {Oliver Schulte and Zhensong Qian},
journal= {arXiv preprint arXiv:1508.02428},
year = {2015}
}
Comments
14 pages, 10 figures, 10 tables, Published on 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015), Oct 19-21, 2015, Paris, France