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Configurable Process Mining: Semantic Variability in Event Logs
| Titre | Configurable Process Mining: Semantic Variability in Event Logs |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Khannat, A, Sbaï, H, Kjiri, L |
| Conference Name | International Conference on Enterprise Information Systems, ICEIS - Proceedings |
| Mots-clés | Business Process, Configurable process model discovery, Configurable process models, Data mining, Event logs, Information systems, Model discoveries, Ontology, Ontology's, Process Discovery, Process mining, Process variants, Reference modeling, Reusability, Semantics |
| Abstract | Configurable process model represents a reference model regrouping multiple business process variants. The configurable process models offer various benefits like reusability and more flexibility when compared to business process models. The challenges encountered while managing this type of models are related to the creation and the configuration. Recently, process mining offers techniques to discover, check conformance of models, and enhance configurable process models using a collection of event logs, that captures traces during the execution of process variants. However, existing works in configurable process discovery lack the incorporation of semantics in the resulting model. Historically, semantic process mining has been applied to event logs to improve process discovery with respect to semantic. Furthermore, from the best of our knowledge, configurable process mining approaches do not fully support semantics. In this paper, we propose a novel method to enrich the collection of event logs with configurable process ontology concepts by introducing semantic annotations that capture variability of elements present in the logs. This is a first step towards discovering a semantically enriched configurable process. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.
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| URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137949536&partnerID=40&md5=dd49bcf12987200729d1f5e491d2994b |
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