• DocumentCode
    3779367
  • Title

    Runtime deduction of case ID for unlabeled business process execution events

  • Author

    Iman M. A. Helal;Ahmed Awad;Ali El Bastawissi

  • Author_Institution
    Information System Department, Faculty of Computers and Information, Cairo University, Egypt
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    Events produced from business process execution need identification of process instance. With the lack of a central execution, it is hard to correlate these events to specific cases. Monitoring business processes is useful in conformance checking, compliance enforcement, risk management, and performance analysis. However, all these techniques and approaches need a set of correlated events. We present an approach to fill the gap in real life situations, between execution of unmanaged events and the stack of techniques and approaches that need labeled events at runtime to generate further analysis. This approach works on the unlabeled events, either online (as a stream of events) or offline (as a batch file of events). It deduces the case identifier for each unlabeled event, and displays the results of possible case identifiers with their rankings. Also the generated events can be filed in different event logs with different rankings to be further analyzed by other techniques and approaches.
  • Keywords
    "Decision trees","Monitoring","Runtime","Probability","Organizations","Standards organizations"
  • Publisher
    ieee
  • Conference_Titel
    Computer Systems and Applications (AICCSA), 2015 IEEE/ACS 12th International Conference of
  • Electronic_ISBN
    2161-5330
  • Type

    conf

  • DOI
    10.1109/AICCSA.2015.7507132
  • Filename
    7507132