Summary: Causal Nets: A Modeling Language Tailored
Towards Process Discovery
W.M.P. van der Aalst, A. Adriansyah, and B.F. van Dongen
Department of Mathematics and Computer Science,
Technische Universiteit Eindhoven, The Netherlands.
Abstract. Process discovery--discovering a process model from exam-
ple behavior recorded in an event log--is one of the most challenging
tasks in process mining. The primary reason is that conventional model-
ing languages (e.g., Petri nets, BPMN, EPCs, and ULM ADs) have dif-
ficulties representing the observed behavior properly and/or succinctly.
Moreover, discovered process models tend to have deadlocks and live-
locks. Therefore, we advocate a new representation more suitable for
process discovery: causal nets. Causal nets are related to the representa-
tions used by several process discovery techniques (e.g., heuristic mining,
fuzzy mining, and genetic mining). However, unlike existing approaches,
we provide declarative semantics more suitable for process mining. To
clarify these semantics and to illustrate the non-local nature of this new
representation, we relate causal nets to Petri nets.