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International Conference on Conceptual Modeling

ER 2016: Conceptual Modeling pp 65–72Cite as

Translating Bayesian Networks into Entity Relationship Models

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Part of the book series: Lecture Notes in Computer Science ((LNISA,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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Author information

Authors and Affiliations

  1. Global Data and Analytics, Allianz SE, Munich, Germany

    Frank Rosner

  2. Computer Science, Martin-Luther-University Halle-Wittenberg, Halle, Germany

    Alexander Hinneburg

Authors
  1. Frank Rosner
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  2. Alexander Hinneburg
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Corresponding authors

Correspondence to Frank Rosner or Alexander Hinneburg .

Editor information

Editors and Affiliations

  1. CEDRIC-CNAM & ESSEC Business School, Cergy-Pontoise, France

    Isabelle Comyn-Wattiau

  2. Kyoto University , Kyoto, Japan

    Katsumi Tanaka

  3. Drexel University , Philadelphia, Pennsylvania, USA

    Il-Yeol Song

  4. Nagoya University , Nagoya, Japan

    Shuichiro Yamamoto

  5. Tokyo Institute of Technology , Tokyo, Japan

    Motoshi Saeki

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© 2016 Springer International Publishing AG

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Cite this paper

Rosner, F., Hinneburg, A. (2016). Translating Bayesian Networks into Entity Relationship Models. In: Comyn-Wattiau, I., Tanaka, K., Song, IY., Yamamoto, S., Saeki, M. (eds) Conceptual Modeling. ER 2016. Lecture Notes in Computer Science(), vol 9974. Springer, Cham. https://doi.org/10.1007/978-3-319-46397-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-46397-1_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46396-4

  • Online ISBN: 978-3-319-46397-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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