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Simplified Process Model Discovery Based on Role-Oriented Genetic Mining
Joint Authors
Dai, Weihui
Zhao, Weidong
Liu, Xi
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-01-29
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Zhao, Weidong& Liu, Xi& Dai, Weihui. 2014. Simplified Process Model Discovery Based on Role-Oriented Genetic Mining. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1049129
Modern Language Association (MLA)
Zhao, Weidong…[et al.]. Simplified Process Model Discovery Based on Role-Oriented Genetic Mining. The Scientific World Journal No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1049129
American Medical Association (AMA)
Zhao, Weidong& Liu, Xi& Dai, Weihui. Simplified Process Model Discovery Based on Role-Oriented Genetic Mining. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1049129
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049129