Causal nets: A modeling language tailored towards process discovery

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Abstract

Process discovery—discovering a process model from example behavior recorded in an event log—is one of the most challenging tasks in process mining. The primary reason is that conventional modeling languages (e.g., Petri nets, BPMN, EPCs, and ULM ADs) have difficulties representing the observed behavior properly and/or succinctly. Moreover, discovered process models tend to have deadlocks and livelocks. 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, 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.
Original languageEnglish
Title of host publicationCONCUR 2011 - Concurrency Theory (22nd International Conference, Aachen, Germany, September 6-9, 2011. Proceedings)
EditorsJ.P. Katoen, B. König
Place of PublicationBerlin
PublisherSpringer
Pages28-42
ISBN (Print)978-3-642-23216-9
DOIs
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science
Volume6901
ISSN (Print)0302-9743

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