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The Scientific World Journal
Volume 2014, Article ID 298592, 8 pages
http://dx.doi.org/10.1155/2014/298592
Research Article

Simplified Process Model Discovery Based on Role-Oriented Genetic Mining

1Software School, Fudan University, No. 220 Handan Road, Shanghai 200433, China
2School of Management, Fudan University, No. 220 Handan Road, Shanghai 200433, China

Received 8 August 2013; Accepted 14 November 2013; Published 29 January 2014

Academic Editors: J. Shu and F. Yu

Copyright © 2014 Weidong Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Process mining is automated acquisition of process models from event logs. Although many process mining techniques have been developed, most of them are based on control flow. Meanwhile, the existing role-oriented process mining methods focus on correctness and integrity of roles while ignoring role complexity of the process model, which directly impacts understandability and quality of the model. To address these problems, we propose a genetic programming approach to mine the simplified process model. Using a new metric of process complexity in terms of roles as the fitness function, we can find simpler process models. The new role complexity metric of process models is designed from role cohesion and coupling, and applied to discover roles in process models. Moreover, the higher fitness derived from role complexity metric also provides a guideline for redesigning process models. Finally, we conduct case study and experiments to show that the proposed method is more effective for streamlining the process by comparing with related studies.