Designing and Evaluating an Interpretable Predictive Modeling Technique for Business Processes

Breuker Dominic, Delfmann Patrick, Matzner Martin, Becker Jörg


Zusammenfassung
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.

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



Publikationstyp
Buchbeitrag (Sammel-, Herausgeberband)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2015

Buchtitel
BPM 2014 International Workshops

Herausgeber
Fournier Fabiana, Mendling Jan

Seiten
541-553

Band
202

Reihe
Lecture Notes in Business Information Processing

Verlag
Springer

Ort
Berlin

Sprache
Englisch

ISBN
978-3-319-15894-5

DOI

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