Summary: Do Petri Nets Provide the Right
Representational Bias for Process Mining?
W.M.P. van der Aalst
Department of Mathematics and Computer Science,
Technische Universiteit Eindhoven, The Netherlands.
Abstract. Process discovery is probably the most challenging process
mining task. Given an event log, i.e., a set of example traces, it is diffi-
cult to automatically construct a process model explaining the behavior
seen in the log. Many process discovery techniques use Petri nets as a
language to describe the discovered model. This implies that the search
space--often referred to as the representational bias--includes many in-
consistent models (e.g., models with deadlocks and livelocks). Moreover,
the low-level nature of Petri nets does not help in finding a proper bal-
ance between overfitting and underfitting. Therefore, we advocate a new
representation more suitable for process discovery: causal nets. Causal
nets are related to the representations used by several process discovery
techniques (e.g., heuristic mining, fuzzy mining, and genetic mining).
However, unlike existing approaches, C-nets use declarative semantics