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.
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