A Framework of Business Process Monitoring and Prediction Techniques

Frederik Wolf (), Jens Brunk () and Jörg Becker ()
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Frederik Wolf: University of Münster – ERCIS
Jens Brunk: University of Münster – ERCIS
Jörg Becker: University of Münster – ERCIS

A chapter in Innovation Through Information Systems, 2021, pp 714-724 from Springer

Abstract: Abstract The digitization of businesses provides huge amounts of data that can be leveraged by modern Business Process Management methods. Predictive Business Process Monitoring (PBPM) represents techniques which deal with real-time analysis of currently running process instances and also with the prediction of their future behavior. While many different prediction techniques have been developed, most of the early techniques base their predictions solely on the control­fow characteristic of a business process. More recently, researchers attempt to incorporate additional process-related information, also known as the process context, into their predictive models. In 2018, Di Francescomarino et al. published a framework of existing prediction techniques. Since the young field has evolved greatly since then and context information continue to play a greater role in predictive techniques, this paper describes the process and outcome of updating and extending the framework to include process context dimensions by replicating the literature review of the initial authors.

Keywords: Business process; Prediction; Techniques; Predictive Business Process Monitoring (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/978-3-030-86797-3_47

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