Discovering branching conditions from business process execution logs

M. Leoni, de; M. Dumas; L. García-Bañuelos

doi:10.1007/978-3-642-37057-1_9

Discovering branching conditions from business process execution logs

M. Leoni, de, M. Dumas, L. García-Bañuelos

Process Analytics

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

37 Citations (Scopus)

Abstract

Process mining is a family of techniques to discover business process models and other knowledge of business processes from event logs. Existing process mining techniques are geared towards discovering models that capture the order of execution of tasks, but not the conditions under which tasks are executed – also called branching conditions. One existing process mining technique, namely ProM’s Decision Miner, applies decision tree learning techniques to discover branching conditions composed of atoms of the form "v op c" where "v" is a variable, "op" is a comparison predicate and "c" is a constant. This paper puts forward a more general technique to discover branching conditions where the atoms are linear equations or inequalities involving multiple variables and arithmetic operators. The proposed technique combine invariant discovery techniques embodied in the Daikon system with decision tree learning techniques.

Original language	English
Title of host publication	Fundamental Approaches to Software Engineering (16th International Conference, FASE 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013. Proceedings)
Editors	V. Cortellessa, D. Varro
Place of Publication	Berlin
Publisher	Springer
Pages	114-129
ISBN (Print)	978-3-642-37056-4
DOIs	https://doi.org/10.1007/978-3-642-37057-1_9
Publication status	Published - 2013
Event	16th International Conference on Fundamental Approaches to Software Engineering (FASE 2013) - Rome, Italy Duration: 20 Mar 2013 → 22 Mar 2013 Conference number: 16

Publication series

Name	Lecture Notes in Computer Science
Volume	7793
ISSN (Print)	0302-9743

Conference

Conference	16th International Conference on Fundamental Approaches to Software Engineering (FASE 2013)
Abbreviated title	FASE 2013
Country/Territory	Italy
City	Rome
Period	20/03/13 → 22/03/13
Other	Conference held as part of the 16th European Joint Conferences on Theory and Practice of Software (ETAPS 2013)

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10.1007/978-3-642-37057-1_9

Cite this

Leoni, de, M., Dumas, M., & García-Bañuelos, L. (2013). Discovering branching conditions from business process execution logs. In V. Cortellessa, & D. Varro (Eds.), Fundamental Approaches to Software Engineering (16th International Conference, FASE 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013. Proceedings) (pp. 114-129). (Lecture Notes in Computer Science; Vol. 7793). Springer. https://doi.org/10.1007/978-3-642-37057-1_9

Leoni, de, M. ; Dumas, M. ; García-Bañuelos, L. / Discovering branching conditions from business process execution logs. Fundamental Approaches to Software Engineering (16th International Conference, FASE 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013. Proceedings). editor / V. Cortellessa ; D. Varro. Berlin : Springer, 2013. pp. 114-129 (Lecture Notes in Computer Science).

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Leoni, de, M, Dumas, M & García-Bañuelos, L 2013, Discovering branching conditions from business process execution logs. in V Cortellessa & D Varro (eds), Fundamental Approaches to Software Engineering (16th International Conference, FASE 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013. Proceedings). Lecture Notes in Computer Science, vol. 7793, Springer, Berlin, pp. 114-129, 16th International Conference on Fundamental Approaches to Software Engineering (FASE 2013), Rome, Italy, 20/03/13. https://doi.org/10.1007/978-3-642-37057-1_9

Discovering branching conditions from business process execution logs. / Leoni, de, M.; Dumas, M.; García-Bañuelos, L.
Fundamental Approaches to Software Engineering (16th International Conference, FASE 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013. Proceedings). ed. / V. Cortellessa; D. Varro. Berlin: Springer, 2013. p. 114-129 (Lecture Notes in Computer Science; Vol. 7793).

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

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AB - Process mining is a family of techniques to discover business process models and other knowledge of business processes from event logs. Existing process mining techniques are geared towards discovering models that capture the order of execution of tasks, but not the conditions under which tasks are executed – also called branching conditions. One existing process mining technique, namely ProM’s Decision Miner, applies decision tree learning techniques to discover branching conditions composed of atoms of the form "v op c" where "v" is a variable, "op" is a comparison predicate and "c" is a constant. This paper puts forward a more general technique to discover branching conditions where the atoms are linear equations or inequalities involving multiple variables and arithmetic operators. The proposed technique combine invariant discovery techniques embodied in the Daikon system with decision tree learning techniques.

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Leoni, de M, Dumas M, García-Bañuelos L. Discovering branching conditions from business process execution logs. In Cortellessa V, Varro D, editors, Fundamental Approaches to Software Engineering (16th International Conference, FASE 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013. Proceedings). Berlin: Springer. 2013. p. 114-129. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-37057-1_9

Discovering branching conditions from business process execution logs

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