DocumentCode :
3008163
Title :
Business models enhancement through discovery of roles
Author :
Burattin, Andrea ; Sperduti, Alessandro ; Veluscek, Marco
Author_Institution :
Dept. of Math., Univ. of Padua, Padua, Italy
fYear :
2013
fDate :
16-19 April 2013
Firstpage :
103
Lastpage :
110
Abstract :
Control flow discovery algorithms are able to reconstruct the workflow of a business process from a log of performed activities. These algorithms, however, do not pay attention to the reconstruction of roles, i.e. they do not group activities according to the skills required to perform them. Information about roles in business processes is commonly considered important and explicitly integrated into the process representation, e.g. as swimlanes in BPMN diagrams. This work proposes an approach to enhance a business process model with information on roles. Specifically, the identification of roles is based on the detection of handover of roles. On the basis of candidates for roles handover, the set of activities is first partitioned and then subsets of activities which are performed by the same originators are merged, so to obtain roles. All significant partitions of activities are automatically generated. Experimental results on several logs show that the set of generated roles is not too large and it always contains the correct definition of roles. We also propose an entropy based measure to rank the candidate roles which returns promising experimental results.
Keywords :
business process re-engineering; data mining; entropy; organisational aspects; workflow management software; BPMN diagrams; business model enhancement; business process model; business process workflow; control flow discovery algorithms; entropy; process representation; role discovery; Business; Data mining; Handover; Measurement; Partitioning algorithms; Social network services; organizational mining; process enhancement; process mining; social network analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Data Mining (CIDM), 2013 IEEE Symposium on
Conference_Location :
Singapore
Type :
conf
DOI :
10.1109/CIDM.2013.6597224
Filename :
6597224
Link To Document :
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