Abstract:
The management of business processes went through several changes. On the one hand, business intelligence (BI) is
becoming more popular among businesses as a way to cut costs, boost service quality, and enhance decision-making. On the other
hand, we use business process management in a smart environment. So these data sources produced in this environment via sensors,
actuators, and other devices are more varied and unstructured, so to apply the process mining techniques, it is necessary to transform
them into a structured format. Several works have been done in this direction, and the authors have contributed to the improvement,
but the problem is that there is not yet an approach that formalizes the transformation in a general way regardless of the type of sensor
data element. Our approach is based on a model-driven architecture (MDA), which allows us to generate source-to-target data
transformations. The main objective is to establish the MDA approach via transformation rules based on machine learning techniques.
