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Predictive Process Monitoring Methods: Which One Suits Me Best?

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Book cover Business Process Management (BPM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11080))

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Abstract

Predictive process monitoring has recently gained traction in academia and is maturing also in companies. However, with the growing body of research, it might be daunting for data analysts to navigate through this domain in order to find, provided certain data, what can be predicted and what methods to use. The main objective of this paper is developing a value-driven framework for classifying predictive process monitoring methods. This objective is achieved by systematically reviewing existing work in this area. Starting from about 780 papers retrieved through a keyword-based search from electronic libraries and filtering them according to some exclusion criteria, 55 papers have been finally thoroughly analyzed and classified. Then, the review has been used to develop the value-driven framework that can support researchers and practitioners to navigate through the predictive process monitoring field and help them to find value and exploit the opportunities enabled by these analysis techniques.

F. M. Maggi and F. Milani—This research is supported by the Estonian Research Council Grant IUT20-55.

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Notes

  1. 1.

    The queries were run on October 20, 2017.

  2. 2.

    The data extracted was entered into an excel sheet and is available for download at https://docs.google.com/spreadsheets/d/1l1enKhKWx_3KqtnUgggrPl1aoJMhvmy9TF9jAM3snas/edit#gid=959800788.

  3. 3.

    For space limitations, in this article, an abridged version of the framework is presented. The complete version of the framework includes additional data and is available for download at https://docs.google.com/spreadsheets/d/1l1enKhKWx_3KqtnUgggrPl1aoJMhvmy9TF9jAM3snas/edit#gid=959800788.

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Authors and Affiliations

  1. FBK-IRST, Via Sommarive 18, 38050, Trento, Italy

    Chiara Di Francescomarino & Chiara Ghidini

  2. University of Tartu, Liivi 2, 50409, Tartu, Estonia

    Fabrizio Maria Maggi & Fredrik Milani

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  1. Chiara Di Francescomarino
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  1. Hasso-Plattner-Institute, University of Potsdam, Potsdam, Germany

    Mathias Weske

  2. Free University of Bozen Bolzano, Bolzano, Italy

    Marco Montali

  3. Data61, CSIRO, Eveleigh, NSW, Australia

    Ingo Weber

  4. University of Liechtenstein, Vaduz, Liechtenstein

    Jan vom Brocke

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Di Francescomarino, C., Ghidini, C., Maggi, F.M., Milani, F. (2018). Predictive Process Monitoring Methods: Which One Suits Me Best?. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds) Business Process Management. BPM 2018. Lecture Notes in Computer Science(), vol 11080. Springer, Cham. https://doi.org/10.1007/978-3-319-98648-7_27

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