Integrating linked sensor data for on-line analytical processing on-the-fly

Sensor networks are gaining more and more attention in the current technology landscape. It is undeniable that their use allows a better monitoring of events that occur in the real world. Many sensors have been deployed for monitoring applications such as environmental monitoring, and traffic monitoring. A number of governments, corporates, and academic organizations or agencies hold independently sensor systems that generate a large amount of dynamic information from data sources with various formats of schemas and data. They are making this sensor data openly accessible by publishing it as Linked Sensor Data (LSD) on the Linked Open Data (LOD) cloud. LSD is the concept that defines the publication of public or private organization sensor data without restrictions. This is achieved by transforming raw sensor observations to RDF format and by linking it with other datasets on the LOD cloud. The seamless integration of LSD sources from multiple providers is a great challenge. In this paper, we investigate the possibility of integrating diverse LSD sources using the hybrid ontology approach for on-line analytical processing (OLAP) on-the-fly. With such an ontology-based integration framework, organizations or individuals will have greater opportunity to make their respective analysis based on a large amount of sensor data openly accessible on the Web.