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Mining Predictive Process Models out of Low-level Multidimensional Logs

  • Francesco Folino
  • Massimo Guarascio
  • Luigi Pontieri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8484)

Abstract

Process Mining techniques have been gaining attention, especially as concerns the discovery of predictive process models. Traditionally focused on workflows, they usually assume that process tasks are clearly specified, and referred to in the logs. This limits however their application to many real-life BPM environments (e.g. issue tracking systems) where the traced events do not match any predefined task, but yet keep lots of context data. In order to make the usage of predictive process mining to such logs more effective and easier, we devise a new approach, combining the discovery of different execution scenarios with the automatic abstraction of log events. The approach has been integrated in a prototype system, supporting the discovery, evaluation and reuse of predictive process models. Tests on real-life data show that the approach achieves compelling prediction accuracy w.r.t. state-of-the-art methods, and finds interesting activities’ and process variants’ descriptions.

Keywords

Business Process Analysis Data Mining Prediction 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Francesco Folino
    • 1
  • Massimo Guarascio
    • 1
  • Luigi Pontieri
    • 1
  1. 1.National Research Council of Italy (CNR)RendeItaly

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