An architecture for processes analysis in smart factories based on big data, process mining, and machine learning techniques

Abstract :

Due to the nature of smart factories and the use of new technologies such as cyber-physical systems, cloud computing, the Internet of Things, etc. in such environments, the volume of data generated has increased exponentially. Therefore, real-time processing of large volumes of high-speed data for process analysis is a difficult and challenging problem. In this case, big data analysis technologies, as a powerful tool, can play an important role in controlling processes. In this research, an architecture based on a combination of big data, process mining, and machine learning techniques for analyzing processes in smart factories is presented, which enables accurate and real-time analysis of processes in such environments. In fact, this architecture utilizes powerful big data analysis tools and new techniques such as process mining and by employing the logistic regression algorithm as a machine learning tool, it is able to extract valuable insights from the data generated in these environments. The results of the performance evaluation of the proposed architecture show that this architecture provides high capabilities in real-time process analysis through the combination of advanced data analysis techniques

References:

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