Title: Robust process discovery with artificial negative events
Authors: Goedertier, Stijn ×
Martens, David
Vanthienen, Jan
Baesens, Bart #
Issue Date: Jun-2009
Publisher: MIT Press
Series Title: Journal of Machine Learning Research vol:10 pages:1305-1340
Abstract: Process discovery is the automated construction of structured process models from information system
event logs. Such event logs often contain positive examples only. Without negative examples, it is a challenge to strike the right balance between recall and specificity, and to deal with problems such as expressiveness, noise, incomplete event logs, or the inclusion of prior knowledge. In this paper, we present a configurable technique that deals with these challenges by representing process
discovery as a multi-relational classification problem on event logs supplemented with Artificially Generated Negative Events (AGNEs). This problem formulation allows using learning algorithms and evaluation techniques that are well-know in the machine learning community. Moreover, it allows users to have a declarative control over the inductive bias and language bias.
ISSN: 1532-4435
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Management Informatics (LIRIS), Leuven
× corresponding author
# (joint) last author

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