Title: Chaotic activities recognising during the pre-processing event data phase
Authors: Zineb Lamghari; Rajaa Saidi; Maryam Radgui; Moulay Driss Rahmani
Addresses: LRIT Associated Unit to CNRST (URAC 29), Rabat IT Center, Faculty of Sciences, Mohammed V University, Rabat, Morocco ' SI2M Laboratory, National Institute of Statistics and Applied Economics, Rabat, Morocco; LRIT Associated Unit to CNRST (URAC 29), Rabat IT Center, Faculty of Sciences, Mohammed V University, Rabat, Morocco ' SI2M Laboratory, National Institute of Statistics and Applied Economics, Rabat, Morocco; LRIT Associated Unit to CNRST (URAC 29), Rabat IT Center, Faculty of Sciences, Mohammed V University, Rabat, Morocco ' LRIT Associated Unit to CNRST (URAC 29), Rabat IT Center, Faculty of Sciences, Mohammed V University, Rabat, Morocco
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.
Keywords: pre-processing; process discovery; process mining; chaotic activity; business process intelligent; machine learning algorithms.
DOI: 10.1504/IJBIDM.2022.123213
International Journal of Business Intelligence and Data Mining, 2022 Vol.20 No.4, pp.412 - 439
Received: 06 Oct 2020
Accepted: 02 Dec 2020
Published online: 03 Jun 2022 *