Causal nets: A modeling language tailored towards process discovery

W.M.P. Aalst, van der; A. Adriansyah; B.F. Dongen, van

doi:10.1007/978-3-642-23217-6_3

Causal nets: A modeling language tailored towards process discovery

W.M.P. Aalst, van der, A. Adriansyah, B.F. Dongen, van

Architecture of Information Systems - SIKS

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

56 Citations (Scopus)

2 Downloads (Pure)

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 language	English
Title of host publication	CONCUR 2011 - Concurrency Theory (22nd International Conference, Aachen, Germany, September 6-9, 2011. Proceedings)
Editors	J.P. Katoen, B. König
Place of Publication	Berlin
Publisher	Springer
Pages	28-42
ISBN (Print)	978-3-642-23216-9
DOIs	https://doi.org/10.1007/978-3-642-23217-6_3
Publication status	Published - 2011

Publication series

Name	Lecture Notes in Computer Science
Volume	6901
ISSN (Print)	0302-9743

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Aalst, van der, W. M. P., Adriansyah, A., & Dongen, van, B. F. (2011). Causal nets: A modeling language tailored towards process discovery. In J. P. Katoen, & B. König (Eds.), CONCUR 2011 - Concurrency Theory (22nd International Conference, Aachen, Germany, September 6-9, 2011. Proceedings) (pp. 28-42). (Lecture Notes in Computer Science; Vol. 6901). Springer. https://doi.org/10.1007/978-3-642-23217-6_3

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title = "Causal nets: A modeling language tailored towards process discovery",

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.",

author = "{Aalst, van der}, W.M.P. and A. Adriansyah and {Dongen, van}, B.F.",

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Aalst, van der, WMP, Adriansyah, A & Dongen, van, BF 2011, Causal nets: A modeling language tailored towards process discovery. in JP Katoen & B König (eds), CONCUR 2011 - Concurrency Theory (22nd International Conference, Aachen, Germany, September 6-9, 2011. Proceedings). Lecture Notes in Computer Science, vol. 6901, Springer, Berlin, pp. 28-42. https://doi.org/10.1007/978-3-642-23217-6_3

Causal nets: A modeling language tailored towards process discovery. / Aalst, van der, W.M.P.; Adriansyah, A.; Dongen, van, B.F.

CONCUR 2011 - Concurrency Theory (22nd International Conference, Aachen, Germany, September 6-9, 2011. Proceedings). ed. / J.P. Katoen; B. König. Berlin : Springer, 2011. p. 28-42 (Lecture Notes in Computer Science; Vol. 6901).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AB - 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.

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Aalst, van der WMP, Adriansyah A, Dongen, van BF. Causal nets: A modeling language tailored towards process discovery. In Katoen JP, König B, editors, CONCUR 2011 - Concurrency Theory (22nd International Conference, Aachen, Germany, September 6-9, 2011. Proceedings). Berlin: Springer. 2011. p. 28-42. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-23217-6_3