A Workflow to Convert Live Atmospheric Sensor Data into Linked Data

Today's atmospheric data is generated swiftly as a result of the growth of IoT and sensor technologies and is available via data suppliers' RESTful APIs. However, sensor data mostly consists of live data streams including sensor observations, which are produced in a dispersed manner by several heterogeneous infrastructures, with little or no interoperability. RDF streams incorporating semantic data interoperability have arisen in last years and can be the foundation of intelligent semantic applications (e.g. semantic complex event processing). To enable semantic analysis of live atmospheric data streams, this article proposes a methodology for converting live data streams into Linked Data. The process leverages the most recent technologies for RML semantic mapping, ontology modeling, and Linked Data to extend the semantic usefulness of live atmospheric data, for example, by allowing for easy integration of atmospheric data streams with other live RDF streams.