Generic and Concurrent Computation of Belief Combination Rules

  • Frédéric DambrevilleEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 737)


As a form of random set, belief functions come with specific semantic and combination rule able to perform the representation and the fusion of uncertain and imprecise informations. The development of new combination rules able to manage conflict between data now offers a variety of tools for robust combination of piece of data from a database. The computation of multiple combinations from many querying cases in a database make necessary the development of efficient approach for concurrent belief computation. The approach should be generic in order to handle a variety of fusion rules. We present a generic implementation based on a map-reduce paradigm. An enhancement of this implementation is then proposed by means of a Markovian decomposition of the rule definition. At last, comparative results are presented for these implementations within the frameworks Apache Spark and Apache Flink.


Map-reduce Distributed data processing Belief functions Combination rules Statistics 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.DGA MI/Lab-STICC, UMR CNRS 6285, Ensta BretagneBrestFrance

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