Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture.
1 In a seminal work published in 1952, "The chemical basis of morphogenesis"-considered as the true start point of the modern theoretical biology-, A. M. Turing established the core of what today we call "natural computation" in biological systems, intended as self-organizing dynamic systems. In this contribution we show that the "intentionality", i.e., the "relation-to-object" characterizing biological morphogenesis and cognitive intelligence, as far as it is formalized in the appropriate ontological interpretation of the modal calculus (formal ontology), can suggest a solution of the reference problem that formal semantics is in principle unable to offer, because of Gödel and Tarski theorems. Such a solution , that is halfway between the "descriptive" (Frege) and the "causal" (Kripke) theory of reference, can be implemented only in a particular class of self-organizing dynamic systems, i.e., the dis-sipative chaotic systems characterizing the "semantic information processing" in biological and neural systems.
For many years now, the importance of semantic technologies, that provide a formal, logic based route to sharing meaning, has been recognized as offering the potential to support interoperability across multiple related applications and hence drive manufacturing competitiveness in the digital manufacturing age. However, progress in support of manufacturing enterprise interoperability has tended to be limited to fairly narrow domains of applicability. This paper presents a progression of research and understanding, culminating in the work undertaken in the recent EU FLEXINET project, to develop a comprehensive manufacturing reference ontology that can (a) support the clarification of understanding across domains, (b) support the ability to flexibly share information across interacting software systems and (c) provide the ability to readily configure company knowledge bases to support interoperable manufacturing systems.
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