Serverless GEO Labels for the Semantic Sensor Web

Graupner Anika, Nüst Daniel

Abstract

With the increasing amount of sensor data available online, it is becoming more difficult for users to identify useful datasets. Semantic Web technologies can improve such discovery via meaningful ontologies, but the decision of whether a dataset is suitable remains with the users. Users can be aided in this process through the GEO label, which provides a visual summary of the standardised metadata. However, the GEO label is not yet available for the Semantic Sensor Web. This work presents novel rules for deriving the information for the GEO label's multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. Thereby, this work enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. Further, the prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. Nonetheless, more real-world semantic sensor descriptions, an analysis of requirements for GEO label facets specific to the Semantic Sensor Web, and an integration into large-scale discovery platforms are needed.

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