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Event logs pre-processing for configurable process discovery: Ontology-based approach
| Titre | Event logs pre-processing for configurable process discovery: Ontology-based approach |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Khannat, A, Sbaï, H, Kjiri, L |
| Conference Name | Colloquium in Information Science and Technology, CIST |
| Mots-clés | Configurable process models, Data mining, Domain ontologies, New approaches, Ontology, Pre-processing, Process Discovery, Process execution, Process mining, Process variants, Semantics |
| Abstract | Nowadays, process mining offers key tools for companies to discover and develop continuously their processes. Moreover, many research works focus on the application of process mining techniques to manage configurable process models. To provide configurable process models with high quality and less complexity using process mining techniques, the preparation of event logs before discovering processes is strongly required. Therefore, various approaches have introduced semantics to derive knowledge from process execution traces. Although, this knowledge is used for discovering variability rules or deriving process variants. However, existing works lack the discovery of semantically enriched configurable process models from event logs. Since event logs are usually combined with semantics and domain ontology for optimizing process mining applications, it seems to be important to introduce a new approach of semantically enriched configurable process discovery. It will be helpful during configuration, validation, and enhancement of configurable processes that are extracted from event logs. In this paper, we present a new framework for event logs preprocessing using ontologies. Our aim is to enrich a collection of event logs with concepts from ontologies as a first step towards discovering a semantically enriched configurable process. © 2021 IEEE.
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| URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103812408&doi=10.1109%2fCiSt49399.2021.9357303&partnerID=40&md5=c1a3e5ace97f4f0e5deedd34b987c311 |
| DOI | 10.1109/CiSt49399.2021.9357303 |
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