DocumentCode :
2493920
Title :
Workflow process mining based on machine learning
Author :
Zhang, Shao-hua ; Gu, Nlng ; Lia, Jie-xin ; Sai-Han Li
Author_Institution :
Dept. of Comput. & Inf. Technol., Fudan Univ., Shanghai, China
Volume :
4
fYear :
2003
fDate :
2-5 Nov. 2003
Firstpage :
2319
Abstract :
This paper presents an algorithm of workflow process mining based on machine learning from the logs of business process instances, which can handle concurrence and recurrence of the business process that are the restrictions of other algorithms. Moreover workflow modeling language named flexible workflow modeling language (FWF-NET) is put forward, which can model uncertain and incomplete business process information, So the business process mined according to the algorithm can easily be transformed the FWF-NET. The prototype and experiments have proved that the algorithm mines business process effective, reduces the complexity in workflow process modeling and evolution, and evaluates performance of existing workflow model.
Keywords :
business process re-engineering; data mining; learning (artificial intelligence); workflow management software; business process; business process information; flexible workflow modeling language; machine learning; workflow modeling language; workflow process mining; Automation; Business; Collaborative work; Educational institutions; Hidden Markov models; Information technology; Machine learning; Machine learning algorithms; Prototypes; Uncertainty;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN :
0-7803-8131-9
Type :
conf
DOI :
10.1109/ICMLC.2003.1259895
Filename :
1259895
Link To Document :
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