Breuker D, Delfmann P, Matzner M, Becker J (2015)
Publication Language: English
Publication Type: Book chapter / Article in edited volumes
Publication year: 2015
Publisher: Springer
Edited Volumes: BPM 2014 International Workshops
Series: Lecture Notes in Business Information Processing
City/Town: Berlin
Book Volume: 202
Pages Range: 541-553
ISBN: 978-3-319-15894-5
URI: http://fluxicon.com/blog/wp-content/uploads/2015/01/DeMiMoP-2014-Predictive-BPM.pdf
DOI: 10.1007/978-3-319-15895-2_46
Process mining is a field traditionally concerned with retrospective analysis of event logs, yet interest in applying it online to running process instances is increasing. In this paper, we design a predictive modeling technique that can be used to quantify probabilities of how a running process instance will behave based on the events that have been observed so far. To this end, we study the field of grammatical inference and identify suitable probabilistic modeling techniques for event log data. After tailoring one of these techniques to the domain of business process management, we derive a learning algorithm. By combining our predictive model with an established process discovery technique, we are able to visualize the significant parts of predictive models in form of Petri nets. A preliminary evaluation demonstrates the effectiveness of our approach.
APA:
Breuker, D., Delfmann, P., Matzner, M., & Becker, J. (2015). Designing and Evaluating an Interpretable Predictive Modeling Technique for Business Processes. In Fournier Fabiana MJ, Mendling Jan (Eds.), BPM 2014 International Workshops. (pp. 541-553). Berlin: Springer.
MLA:
Breuker, Dominic, et al. "Designing and Evaluating an Interpretable Predictive Modeling Technique for Business Processes." BPM 2014 International Workshops. Ed. Fournier Fabiana MJ, Mendling Jan, Berlin: Springer, 2015. 541-553.
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