An automatized data extraction approach for process mining and business process analysis

Abstract : In this paper we explain an abstracted version of our approach to provide a rapid business process modelling, and diagnosis for operational processes within organizations. This approach is oriented toward designing a set of tools which has three main functions; Tracking, Modelling, and Assessing. As a result, first we would gather the events automatically (Tracking), thanks to an Indoor Positioning Systems. Second, by using Process Mining we would be able to get the business process models (Modelling). Third, by using case-based, or heuristics algorithms and Discrete Event Simulation we want to propose a tool for process diagnosis and improvements (Assessing). This research project targeted three main fields which are Healthcare, Supply Chain Management, and Sport. This project is subjected as a doctoral thesis in a collaboration between Industrial Engineering Center of Ecole des Mines d’Albi-Carmaux and Maple High Tech in France.
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Submitted on : Thursday, November 15, 2018 - 3:57:08 PM
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Sina Namaki Araghi, Franck Fontanili, Elyes Lamine, Frederick Benaben. An automatized data extraction approach for process mining and business process analysis. IFAC 2017 - 20th World congress of the International Federation of Automatic Control, IFAC, Jul 2017, Toulouse, France. p.9580-9584. ⟨hal-01924018⟩

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