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ScientificWorldJournal. 2014 Jan 29;2014:298592. doi: 10.1155/2014/298592. eCollection 2014.

Simplified process model discovery based on role-oriented genetic mining.

Author information

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

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.

PMID:
24616618
PMCID:
PMC3926309
DOI:
10.1155/2014/298592
[Indexed for MEDLINE]
Free PMC Article

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