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Discovering Characteristics that Affect Process Control Flow

Delias, Pavlos; Grigori, Daniela; Mouhoub, Mohamed Lamine; Tsoukiàs, Alexis (2015), Discovering Characteristics that Affect Process Control Flow, in I. Linden, S. Liu, F. Dargam, J. Hernández, Decision Support Systems IV - Information and Knowledge Management in Decision Processes: Euro Working Group Conferences, EWG-DSS 2014, Toulouse, France, June 10-13, 2014, and Barcelona, Spain, July 13-18, 2014, Revised Selected and Extended Papers, Springer : Berlin Heidelberg, p. 51-63. 10.1007/978-3-319-21536-5_5

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DiscoveringCharacteristicsthatAffectProcessControlFlowv0.5_forResearchGate.pdf (567.5Kb)
Type
Chapitre d'ouvrage
Date
2015
Book title
Decision Support Systems IV - Information and Knowledge Management in Decision Processes: Euro Working Group Conferences, EWG-DSS 2014, Toulouse, France, June 10-13, 2014, and Barcelona, Spain, July 13-18, 2014, Revised Selected and Extended Papers
Book author
I. Linden, S. Liu, F. Dargam, J. Hernández
Publisher
Springer
Published in
Berlin Heidelberg
ISBN
978-3-319-21535-8
Pages
51-63
Publication identifier
10.1007/978-3-319-21536-5_5
Metadata
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Author(s)
Delias, Pavlos
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Grigori, Daniela
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Mouhoub, Mohamed Lamine
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Tsoukiàs, Alexis cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Abstract (EN)
In flexible environments like healthcare and customer service, business processes are executed with high variability. Often, this is because cases’ characteristics vary. However, it is difficult to correlate process flow with characteristics because characteristics may refer to different perspectives, their number can be real big or even because deep domain knowledge may be required to state hypotheses. The goal of this paper is to propose an effective exploratory tool for discovering the characteristics that are causing the process variation. To this end, we propose a process mining approach. First, we apply a clustering approach based on Latent Class Analysis to identify subtypes of related cases based on the case-wise process characteristics. Then, a process model is discovered for each cluster and through a model similarity step, we are able to recommend the characteristics that mostly diversify the flow. Finally, to validate our methodology, we applied it to both simulated and real datasets.
Subjects / Keywords
process mining; decision analysis
JEL
C44 - Operations Research; Statistical Decision Theory

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