Abstract
Application of machine learning techniques for data-driven modeling of value-creating processes promises significant economic benefits. These applications include process monitoring, process configuration, process control and process optimization (process-X). However, similarities and distinguishing features between established approaches to process-X compared to machine learning are often unclear. This paper sheds light on this issue by deriving a taxonomy of process-X approaches that sharpens the role of machine learning in these applications. Moreover, the taxonomy and discussion identifies future research directions for applied machine learning in cyber-physical systems.
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Reinhart, F., von Enzberg, S., Kühn, A., Dumitrescu, R. (2020). Machine Learning for Process-X: A Taxonomy. In: Beyerer, J., Maier, A., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 11. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-59084-3_4
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DOI: https://doi.org/10.1007/978-3-662-59084-3_4
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