2016/07/08 by Frank Rosner, Rosner, Frank, Alexander Hinneburg +1
Computer Science · #Artificial Intelligence (cs.AI) #Databases (cs.DB) #FOS: Computer and information sciences #cs.AI #cs.DB
paper · pdf · doi:10.48550/arxiv.1607.02399
This is an extended version of a short paper published in the Proceedings of the 35th International Conference on Conceptual Modeling, ER 2016. In addition to a more detailed discussion of the method, this extended version describes a case study that applies the method as well as first ideas of a conceptual framework for developing big data analytics applications
arxiv created 2016/07/08 · arxiv updated 2016/07/11
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.