@conference {Khannat202020,
	title = {Towards mining semantically enriched configurable process models},
	booktitle = {ACM International Conference Proceeding Series},
	year = {2020},
	note = {cited By 0},
	pages = {20-24},
	abstract = {Providing configurable process model with high quality is a primary objective to derive process variants with better accuracy and facilitate process model reuse. For this purpose, many research works have been interested in configurable process mining techniques to discover and configure processes from event logs. Moreover, to use the knowledge captured by event logs when mining processes, the concept of semantic process mining is introduced. It allows for combining semantic technologies with process mining. Despite the diversity of works in mining and customizing configurable process models, the application of these techniques is still limited to use semantics in minimizing the complexity of discovered processes. However, it seems to be pertinent to discover semantically enriched configurable process models directly from event logs. Consequently, this can facilitate using semantic in configuring, verifying conformance or enhancing discovered configurable processes. In this paper, we present a comparative study of existing works that focus on mining configurable process models with respect to semantic technologies. Our aim is to propose a new framework to automatically discover semantically enriched configurable processes. {\textcopyright} 2020 ACM.},
	keywords = {Comparative studies, Configurable process models, Data mining, Intelligent systems, Mining process, Primary objective, Process mining, Process Modeling, Process variants, Semantic technologies, Semantic Web, Semantics},
	doi = {10.1145/3419604.3419797},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096408590\&doi=10.1145\%2f3419604.3419797\&partnerID=40\&md5=a77606eacf64646ccd3459e57f5cabdf},
	author = {Khannat, A. and Sba{\"\i}, H. and Kjiri, L.}
}
