Ontology-Based Data Integration for the Internet-of-Things in a Scientific Software Ecosystem

AbstractThe Internet-of-Things (IoT) enables a smart observation of the environment, producing a large amount of heterogeneous data. On the one hand, it allows the remote collection of data, either providing a readily field dataset compilation or serving as a secondary source of information to better analyze the research context. On the other hand, all the disparate sensor generated raw data need to be integrated in order to leverage the power of IoT in scientific experiments. This paper proposes an ontology-based data integration architecture that allows data from different sources, formats, and semantics to be integrated and organized by a mediated ontology that provides knowledge inference. The architecture is thus evaluated as a use case testing in a scientific software ecosystem that supports all stages of the experiment life cycle.


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