Generic and massively concurrent computation of belief combination rules – a MapReduce approach
Résumé
This paper presents a generic and versatile approach for implementing combining rules on preprocessed belief functions, issuing from a large population of information sources. In this paper, we address two issues, which are the intrinsic complexity of the rules processing, and the possible large amount of requested combinations. We present a fully distributed approach, based on a MapReduce scheme. This approach is generic. In particular, we compare two implementations of three sources Dubois & Prade rule within framework Apache Spark and Apache Flink.