In the future, the Internet of Things (IoT) will deliver a growing amount of data from production plants, rendering a manual analysis of these data impossible Therefore, any implementation of the Internet of Things will require corresponding methods for data analysis and machine learning. I.e. what is needed are Big Data solutions for the field of automation. But the IoT demands, for several reasons, other solutions than classical big data scenarios: E.g. measurements are time-dependent and unsynchronized. Furtheremore, all algorithms must be suited for embedded platforms.
Cyber-physical systems propagate more distributed computing strategies, more user interactions and a higher level of autonomous decision making by the systems themselves. Future automation systems will need to incorporate these new technologies allowing them to be more adaptable in order to react to changing requirements and to new product variants.
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