• DocumentCode
    3008163
  • Title

    Business models enhancement through discovery of roles

  • Author

    Burattin, Andrea ; Sperduti, Alessandro ; Veluscek, Marco

  • Author_Institution
    Dept. of Math., Univ. of Padua, Padua, Italy
  • fYear
    2013
  • fDate
    16-19 April 2013
  • Firstpage
    103
  • Lastpage
    110
  • Abstract
    Control flow discovery algorithms are able to reconstruct the workflow of a business process from a log of performed activities. These algorithms, however, do not pay attention to the reconstruction of roles, i.e. they do not group activities according to the skills required to perform them. Information about roles in business processes is commonly considered important and explicitly integrated into the process representation, e.g. as swimlanes in BPMN diagrams. This work proposes an approach to enhance a business process model with information on roles. Specifically, the identification of roles is based on the detection of handover of roles. On the basis of candidates for roles handover, the set of activities is first partitioned and then subsets of activities which are performed by the same originators are merged, so to obtain roles. All significant partitions of activities are automatically generated. Experimental results on several logs show that the set of generated roles is not too large and it always contains the correct definition of roles. We also propose an entropy based measure to rank the candidate roles which returns promising experimental results.
  • Keywords
    business process re-engineering; data mining; entropy; organisational aspects; workflow management software; BPMN diagrams; business model enhancement; business process model; business process workflow; control flow discovery algorithms; entropy; process representation; role discovery; Business; Data mining; Handover; Measurement; Partitioning algorithms; Social network services; organizational mining; process enhancement; process mining; social network analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Data Mining (CIDM), 2013 IEEE Symposium on
  • Conference_Location
    Singapore
  • Type

    conf

  • DOI
    10.1109/CIDM.2013.6597224
  • Filename
    6597224