Using mapreduce to scale events correlation discovery for business processes mining

Authors:
Hicham Reguieg;Farouk Toumani;Hamid Reza Motahari-Nezhad;Boualem Benatallah
Affiliations:
LIMOS, CNRS, Blaise Pascal University, Clermont-Ferrand, France;LIMOS, CNRS, Blaise Pascal University, Clermont-Ferrand, France;HP Labs, Palo Alto;CSE, UNSW, Sydney, Australia
Venue:
BPM'12 Proceedings of the 10th international conference on Business Process Management
Year:
2012

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Abstract

In this paper, we present a scalable data analysis technique to support efficient event correlation for mining business processes. We propose a two-stages approach to compute correlation conditions and their entailed process instances from event logs using MapReduce framework. The experimental results show that the algorithm scales well to large datasets.