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Complex Symbolic Sequence Clustering and Multiple Classifiers for Predictive Process Monitoring

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Book cover Business Process Management Workshops (BPM 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 256))

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Abstract

This paper addresses the following predictive business process monitoring problem: Given the execution trace of an ongoing case, and given a set of traces of historical (completed) cases, predict the most likely outcome of the ongoing case. In this context, a trace refers to a sequence of events with corresponding payloads, where a payload consists of a set of attribute-value pairs. Meanwhile, an outcome refers to a label associated to completed cases, like, for example, a label indicating that a given case completed “on time” (with respect to a given desired duration) or “late”, or a label indicating that a given case led to a customer complaint or not. The paper tackles this problem via a two-phased approach. In the first phase, prefixes of historical cases are encoded using complex symbolic sequences and clustered. In the second phase, a classifier is built for each of the clusters. To predict the outcome of an ongoing case at runtime given its (uncompleted) trace, we select the closest cluster(s) to the trace in question and apply the respective classifier(s), taking into account the Euclidean distance of the trace from the center of the clusters. We consider two families of clustering algorithms – hierarchical clustering and k-medoids – and use random forests for classification. The approach was evaluated on four real-life datasets.

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Notes

  1. 1.

    except for the “No clustering” approach.

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Author information

Authors and Affiliations

  1. Information Systems School, Queensland University of Technology, Brisbane, Australia

    Ilya Verenich & Marcello La Rosa

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

    Ilya Verenich, Marlon Dumas & Fabrizio Maria Maggi

  3. FBK-IRST, Trento, Italy

    Chiara Di Francescomarino

Authors
  1. Ilya Verenich
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  2. Marlon Dumas
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  3. Marcello La Rosa
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  4. Fabrizio Maria Maggi
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  5. Chiara Di Francescomarino
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Correspondence to Ilya Verenich .

Editor information

Editors and Affiliations

  1. Institute of Databases and Information Systems, University of Ulm, Ulm, Germany

    Manfred Reichert

  2. Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Hajo A. Reijers

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Verenich, I., Dumas, M., La Rosa, M., Maggi, F.M., Di Francescomarino, C. (2016). Complex Symbolic Sequence Clustering and Multiple Classifiers for Predictive Process Monitoring. In: Reichert, M., Reijers, H. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-319-42887-1_18

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  • DOI: https://doi.org/10.1007/978-3-319-42887-1_18

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