• 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