Representing situational knowledge acquired from sensor data for atmospheric phenomena

Abstract

A recurrent problem in applications that build on environmental sensor networks is that of sensor data organization and interpretation. Organization focuses on, for instance, resolving the syntactic and semantic heterogeneity of sensor data. The distinguishing factor between organization and interpretation is the abstraction from sensor data with information acquired from sensor data. Such information may be situational knowledge for environmental phenomena. We discuss a generic software framework for the organization and interpretation of sensor data and demonstrate its application to data of a large scale sensor network for the monitoring of atmospheric phenomena. The results show that software support for the organization and interpretation of sensor data is valuable to scientists in scientific computing workflows. Explicitly represented situational knowledge is also useful to client software systems as it can be queried, integrated, reasoned, visualized, or annotated.