Abstract
Traditional role-oriented process modeling seems to be subjective in identifying roles. To solve the problem, the similarity of activities is used in this paper. Sub-processes with high similarity are recognized as the process undertaken by a certain role. In this way, a relatively objective role identification approach is proposed, which determines the interaction between roles and establishes the role-activity diagram. Furthermore, by analyzing the interaction between roles, genetic algorithm is used to introduce multiple factors to optimize the identification. Therefore, an optimized role-oriented process modeling approach is established and an example is presented to show the feasibility of this approach.
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Zhao, W., Lin, Q., Shi, Y., Fang, X. (2012). Mining the Role-Oriented Process Models Based on Genetic Algorithm. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30976-2_48
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DOI: https://doi.org/10.1007/978-3-642-30976-2_48
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