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Do Petri Nets Provide the Right Representational Bias for Process Mining?
 

Summary: Do Petri Nets Provide the Right
Representational Bias for Process Mining?
(short paper)
W.M.P. van der Aalst
Department of Mathematics and Computer Science,
Technische Universiteit Eindhoven, The Netherlands.
W.M.P.v.d.Aalst@tue.nl
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

  

Source: Aalst, W.M.P.van der - Wiskunde en Informatica, Technische Universiteit Eindhoven

 

Collections: Computer Technologies and Information Sciences