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A Deep Learning Approach for Predicting Process Behaviour at Runtime

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Business Process Management Workshops (BPM 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 281))

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Abstract

Predicting the final state of a running process, the remaining time to completion or the next activity of a running process are important aspects of runtime process management. Runtime management requires the ability to identify processes that are at risk of not meeting certain criteria in order to offer case managers decision information for timely intervention. This in turn requires accurate prediction models for process outcomes and for the next process event, based on runtime information available at the prediction and decision point. In this paper, we describe an initial application of deep learning with recurrent neural networks to the problem of predicting the next process event. This is both a novel method in process prediction, which has previously relied on explicit process models in the form of Hidden Markov Models (HMM) or annotated transition systems, and also a novel application for deep learning methods.

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Notes

  1. 1.

    http://caffe.berkelyvision.org, http://torch.ch, https://singa.incubator.apache.org, https://www.tensorflow.org.

  2. 2.

    http://joerg.evermann.ca/software.html.

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Author information

Authors and Affiliations

  1. Memorial University of Newfoundland, St. John’s, Canada

    Joerg Evermann

  2. German Research Center for Artificial Intelligence, Saarbrücken, Germany

    Jana-Rebecca Rehse & Peter Fettke

  3. Saarland University, Saarbrücken, Germany

    Jana-Rebecca Rehse & Peter Fettke

Authors
  1. Joerg Evermann
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  2. Jana-Rebecca Rehse
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  3. Peter Fettke
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Correspondence to Joerg Evermann .

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Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Marlon Dumas

  2. School of Arts, Sciences and Humanities, University of São Paulo, Sao Paulo, São Paulo, Brazil

    Marcelo Fantinato

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Evermann, J., Rehse, JR., Fettke, P. (2017). A Deep Learning Approach for Predicting Process Behaviour at Runtime. In: Dumas, M., Fantinato, M. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-319-58457-7_24

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  • DOI: https://doi.org/10.1007/978-3-319-58457-7_24

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