Abstract
Process mining mainly focuses on the control flow perspective at present. In comparison, role-based process mining stresses the importance of roles in business processes and their interactive relationships. Though some scholars come to pay attention to role identification, their studies are not sufficient in the analysis of role complexity. In this paper, a role coupling complexity metric based on information flow in the process is proposed, and the design structure matrix (DSM) is used for role identification in business processes. Then, some typical process logs are mined by an improved particle swarm optimization method. As the coupling complexity between roles is increasingly reduced, our method can recognize roles with lower complexity. Finally, experiments are performed to verify the effectiveness of the method.
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Zhao, W., Liu, H., Liu, X. (2013). Role Identification Based on the Information Dependency Complexity. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_25
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DOI: https://doi.org/10.1007/978-3-642-53917-6_25
Publisher Name: Springer, Berlin, Heidelberg
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