Designing and Evaluating an Interpretable Predictive Modeling Technique for Business Processes

Breuker Dominic, Delfmann Patrick, Matzner Martin, Becker Jörg


Abstract
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.

Keywords
Data mining; process mining; grammatical inference; predictive modeling



Publication type
Chapter in Book

Peer reviewed
Yes

Publication status
Published

Year
2015

Book title
BPM 2014 International Workshops

Editor
Fournier Fabiana, Mendling Jan

Pages range
541-553

Volume
202

Title of series
Lecture Notes in Business Information Processing

Publisher
Springer

Place
Berlin

Language
English

ISBN
978-3-319-15894-5

DOI

Full text