Translating Bayesian Networks into Entity Relationship Models, Extended Version
This work addresses the integration challenge for developers building big data analytics applications, though it appears incremental by adapting existing models.
The paper tackles the lack of a common conceptual language between data management and machine learning by developing a method to translate Bayesian networks into entity relationship models, enabling practical data management tasks without probabilistic details, as demonstrated in the TopicExplorer system.
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.