Translating Bayesian Networks into Entity Relationship Models

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9974)

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.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Global Data and Analytics, Allianz SEMunichGermany
  2. 2.Computer ScienceMartin-Luther-University Halle-WittenbergHalleGermany

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