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Predictive Business Process Monitoring Framework with Hyperparameter Optimization

  • Chiara Di Francescomarino
  • Marlon Dumas
  • Marco Federici
  • Chiara Ghidini
  • Fabrizio Maria Maggi
  • Williams Rizzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9694)

Abstract

Predictive business process monitoring exploits event logs to predict how ongoing (uncompleted) traces will unfold up to their completion. A predictive process monitoring framework collects a range of techniques that allow users to get accurate predictions about the achievement of a goal for a given ongoing trace. These techniques can be combined and their parameters configured in different framework instances. Unfortunately, a unique framework instance that is general enough to outperform others for every dataset, goal or type of prediction is elusive. Thus, the selection and configuration of a framework instance needs to be done for a given dataset. This paper presents a predictive process monitoring framework armed with a hyperparameter optimization method to select a suitable framework instance for a given dataset.

Keywords

Predictive process monitoring Hyperparameter optimization Linear temporal logic 

Notes

Acknowledgments

This research is funded by the EU FP7 Programme under grant agreement 609190 (Subject-Orientation for People-Centred Production) and by the Estonian Research Council (grant IUT20-55).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Chiara Di Francescomarino
    • 2
  • Marlon Dumas
    • 1
  • Marco Federici
    • 3
  • Chiara Ghidini
    • 2
  • Fabrizio Maria Maggi
    • 1
  • Williams Rizzi
    • 3
  1. 1.University of TartuTartuEstonia
  2. 2.FBK-IRSTTrentoItaly
  3. 3.University of TrentoTrentoItaly

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