Completion Time and Next Activity Prediction of Processes Using Sequential Pattern Mining

  • Michelangelo Ceci
  • Pasqua Fabiana Lanotte
  • Fabio Fumarola
  • Dario Pietro Cavallo
  • Donato Malerba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8777)


Process mining is a research discipline that aims to discover, monitor and improve real processing using event logs. In this paper we describe a novel approach that (i) identifies partial process models by exploiting sequential pattern mining and (ii) uses the additional information about the activities matching a partial process model to train nested prediction models from event logs. Models can be used to predict the next activity and completion time of a new (running) process instance. We compare our approach with a model based on Transition Systems implemented in the ProM5 Suite and show that the attributes in the event log can improve the accuracy of the model without decreasing performances. The experimental results show how our algorithm improves of a large margin ProM5 in predicting the completion time of a process, while it presents competitive results for next activity prediction.


Completion Time Sequence Tree Activity Prediction Process Instance Frequent Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Michelangelo Ceci
    • 1
  • Pasqua Fabiana Lanotte
    • 1
  • Fabio Fumarola
    • 1
  • Dario Pietro Cavallo
    • 1
  • Donato Malerba
    • 1
  1. 1.Dipartimento di InformaticaUniversity of Bari “Aldo Moro”BariItaly

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