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Domain-specific diagrammatic modelling: a source of machine-readable semantics for the Internet of Things

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Abstract

The Internet of Things (IoT) must address not only the data-level communication across networks of sensors and cyber-physical systems, but also the machine-readable semantics that can enrich and elevate sensor data to superior layers of abstraction and interoperability. In this respect, IoT may benefit from the technological space established by the Semantic Web paradigm for the desideratum of semantic interoperability. Another complementary ingredient, proposed in this paper, is the domain-specific knowledge captured in diagrammatic models that describe complex IoT environments with dedicated modelling languages. Such models are traditionally employed to support communication and sense-making among business analysts, or to support software design tasks. The paper at hand advocates a novel role of diagrammatic models—their underlying graph nature combined with an agile approach to modelling semantics are harnessed in order to semantically lift sensor data in a Linked Data environment, consequently enabling a richer back-end to IoT client applications.

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Authors and Affiliations

  1. Business Informatics Research Center, Babes-Bolyai University, Str. Teodor Mihali 58-60, 400591, Cluj Napoca, Romania

    Robert Andrei Buchmann

  2. Knowledge Engineering Research Group, Faculty of Computer Science, University of Vienna, Waehringerstr. 29, Vienna, 1090, Austria

    Dimitris Karagiannis

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  1. Robert Andrei Buchmann
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  2. Dimitris Karagiannis
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Correspondence to Robert Andrei Buchmann.

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Buchmann, R.A., Karagiannis, D. Domain-specific diagrammatic modelling: a source of machine-readable semantics for the Internet of Things. Cluster Comput 20, 895–908 (2017). https://doi.org/10.1007/s10586-016-0695-1

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