Translating Bayesian Networks into Entity Relationship Models, Extended Version

TL;DR

This paper introduces a method to translate Bayesian networks into entity relationship models, facilitating integration of probabilistic graphical models with data management systems, exemplified by the TopicExplorer system.

Contribution

It provides a novel translation approach from Bayesian networks to ER models, bridging probabilistic models and data management for big data analytics.

Findings

Effective translation preserves data management information

Application demonstrated with the TopicExplorer system

Framework supports machine learning development tasks

Abstract

Big data analytics applications drive the convergence of data management and machine learning. But there is no conceptual language available that is spoken in both worlds. The main contribution of the paper is a method to translate Bayesian networks, a main conceptual language for probabilistic graphical models, into usable entity relationship models. The transformed representation of a Bayesian network leaves out mathematical details about probabilistic relationships but unfolds all information relevant for data management tasks. As a real world example, we present the TopicExplorer system that uses Bayesian topic models as a core component in an interactive, database-supported web application. Last, we sketch a conceptual framework that eases machine learning specific development tasks while building big data analytics applications.