@article {Sikal2020183,
	title = {Configurable process model: Discovery approach from event logs},
	journal = {Advances in Science, Technology and Engineering Systems},
	volume = {5},
	number = {1},
	year = {2020},
	note = {cited By 0},
	pages = {183-190},
	abstract = {In the domain of business process management, the configurable process model is widely used to optimize time and cost of business process models design, which is known as the concept of "reuse". Using process mining techniques for process model discovery helps to provide a better view on processes and improve quality of models. The majority of existing configurable model discovery approaches work intensively on control flow discovery as main process perspective without considering other perspectives such as resources and data, and do not propose a detailed discovery of variability elements. In addition, the configurable process model creation is generally done by merging variant models not directly from event logs, which is not the optimal way to get a reliable configurable process model. This paper presents an overview of new multi-perspective variability discovery approach. The approach respects the variability of different process perspectives and allows users to create a configurable process model directly from event logs. {\textcopyright} 2020 ASTES Publishers. All rights reserved.},
	doi = {10.25046/aj050124},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081330401\&doi=10.25046\%2faj050124\&partnerID=40\&md5=716f2277e1926d108d92b8a313d3bac9},
	author = {Sikal, R. and Sba{\"\i}, H. and Kjiri, L.}
}
