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Designing And Evaluating An Interpretable Predictive Modeling Technique For Business Processes

Dominic Breuker, Patrick Delfmann, M. Matzner, J. Becker
Published 2014 · Computer Science

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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.
This paper references
Maximum Likelihood from Incomplete Data via the EM Algorithm
A. D. E. Altri (1977)
Petrify: A Tool for Manipulating Concurrent Specifications and Synthesis of Asynchronous Controllers (Special Issue on Asynchronous Circuit and System Design)
J. Cortadella (1996)
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Vojtech Huser (2012)
Information Theory and an Extension of the Maximum Likelihood Principle
H. Akaike (1973)
A New Framework for Machine Learning
Charles M. Bishop (2008)
A bibliographical study of grammatical inference
C. D. L. Higuera (2005)
Intelligent Agents for the Game of Go
Jean-Baptiste Hoock (2010)
PAutomaC: a PFA/HMM Learning Competition
Sicco Verwer (2012)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
T. Hastie (2009)
Pattern Recognition and Machine Learning
R. Neal (2007)
Bayesian reasoning and machine learning
D. Barber (2012)
Time prediction based on process mining
W. V. Aalst (2011)
Mining process performance from event logs : the BPI Challenge 2012 case study
A. Adriansyah (2012)
BPI Challenge 2015
Boudewijn F. van Dongen (2015)
Marginalizing Out Transition Probabilities for Several Subclasses of PFAs
C. Shibata (2012)
Probabilistic finite-state machines - part I
E. Vidal (2005)
Predictive Analytics in Information Systems Research
Galit Shmueli (2011)
Constructing Decision Trees from Process Logs for Performer Recommendation
Aekyung Kim (2013)
On the Dirichlet Prior and Bayesian Regularization
H. Steck (2002)
Probabilistic Graphical Models - Principles and Techniques
D. Koller (2009)
The elements of statistical learning: data mining, inference and prediction
J. Franklin (2005)
A Tutorial on Hidden Markov Models and Selected Applications
L. Rabiner (1989)
Treba: Efficient Numerically Stable EM for PFA
M. Hulden (2012)
Mining Process Performance from Event Logs
A. Adriansyah (2012)
The Application of Petri Nets to Workflow Management
W. V. Aalst (1998)
Process mining: a two-step approach to balance between underfitting and overfitting
W. V. Aalst (2008)
Machine learning - a probabilistic perspective
K. Murphy (2012)
Beyond Process Mining: From the Past to Present and Future
W. V. Aalst (2010)
Results of the PAutomaC Probabilistic Automaton Learning Competition
S. Verwer (2012)
Grammatical Inference: Informed learners
Colin de la Higuera (2010)
Process Discovery: Capturing the Invisible
W. V. Aalst (2010)
The expectation-maximization algorithm
T. Moon (1996)
Process Mining - Discovery, Conformance and Enhancement of Business Processes
W. V. Aalst (2011)
Process Mining Manifesto
W. V. Aalst (2011)

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