Sensor Metadata Management and Its Application in Collaborative Environmental Research

This paper considers metadata generation and tracking in a collaborative environment where users publish raw sensor data in the form of virtual sensors and post-process data by means of filtering, modeling, or query processing techniques. In the metadata system described, data from different sources with different provenance will be enriched with further metadata at each processing step to describe the processing implemented and/or observations which may explain anomalies in the data. The management of this data is the subject of this paper. In the context of sensor data processing, in particular in the environmental sciences, there is still a large gap between data acquisition and metadata gathering, further complicated by the problem of combining both. In this paper, an attempt is made to bridge the gap between data management and semantic annotation. This paper describes a user friendly, easily deployable system for gathering sensor metadata and capturing semantics behind higher level data processing steps. These semantics are particularly useful in understanding data processing workflows. Furthermore, different methods of querying, exporting and importing gathered data from and to higher level applications are examined.