Aggregating linked sensor data

Abstract

Sensor observations are usually offered in relation to a specific purpose e.g. for reporting ne dust emissions following strict procedures, and spatio temporal scales. Consequently the huge amount of data gathered by today's public and private sensor networks is most often not reused outside of its initial creation context. Fostering the reusability of observations and derived applications calls for (i) spatial temporal and thematic aggregation of measured values and (ii) easy integration mechanisms with external data sources. In this paper we investigate how work on sensor observation aggregation can be incorporated into a Linked Data framework focusing on external linkage as well as provenance information. We show that Linked Data adds new aspects to the aggregation problem e.g.whether external links from one of the original observations can be preserved for the aggregate. The Stimulus Sensor Observation (SSO) ontology design pattern is extended by classes and relations necessary to model the aggregation of sensor observations.