A hybrid approach to extract business process models with high fitness and precision

Process mining (PM) aims at extracting a process model from an event log to represent the process behavior recorded in that event log. An extracted process model with high fitness and precision means it can reflect most of the process behavior recorded in the event log (fitness) and will not generat...

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Bibliographic Details
Published in:Journal of industrial and production engineering 2015-08, Vol.32 (6), p.351-359
Main Authors: Cheng, Hsin-Jung, Ou-Yang, Chao, Juan, Yeh-Chun
Format: Article
Language:English
Subjects:
Business
differential evolution
Electronic monitoring
Evolution
Filled plastics
Fitness
genetic process mining
Genetics
Logs
Mining
particle swarm optimization
precision
process mining
Software reviews
Software upgrading
Swarm intelligence
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Description
Summary:Process mining (PM) aims at extracting a process model from an event log to represent the process behavior recorded in that event log. An extracted process model with high fitness and precision means it can reflect most of the process behavior recorded in the event log (fitness) and will not generate extra behavior not recorded in the event log (precision). Most of the existing PM methods, such as the genetic process mining (GPM), focused only on the achievement of high fitness, but ignore the pursuit of high precision. This research presents a hybrid PM approach that integrates the GPM, particle swarm optimization, and differential evolution to extract process models with high fitness and precision (FP values) from event logs. The results show that the proposed approach achieves improvement in extracting process models from event logs with higher FP values.
ISSN:2168-1015
2168-1023
DOI:10.1080/21681015.2015.1065519