Chaotic activities recognising during the pre-processing event data phase Online publication date: Fri, 03-Jun-2022
by Zineb Lamghari; Rajaa Saidi; Maryam Radgui; Moulay Driss Rahmani
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 20, No. 4, 2022
Abstract: Process mining aims at obtaining insights into business processes by extracting knowledge from event data. Indeed, the quality of events is a crucial element for generating process models, to reflect business process reality. To do so, pre-processing methods are appeared, to clean events from deficiencies (noise, incompleteness and infrequent behaviours) in the limit of chaotic activities' emergence. Chaotic activities are executed arbitrarily in the process and impact the quality of discovered models. Beyond, a supervised learning approach has been proposed, using labelled samples to detect chaotic activities. This puts forward the difficulty of defining chaotic activities in the case of no ground knowledge on which activities are truly chaotic. To that end, we develop an approach for recognising chaotic activities without having labelling training data, using unsupervised learning techniques.
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