Towards Context Modeling in Space and Time

One of the main problems in software development for service robots is to create systems that reliably behave as intended, even though the real field of application and the concrete user requirements are unknown during design time. Consequently, the software controlling service robots has to be aware of its environment and has to adapt its behavior accordingly. A model representing environmental data is called a context model. Appropriate context models currently lack means for modeling temporal and spatial information simultaneously. While it is important to reason about historical context data for most of the SelfAdaptive Systems, there is an increasing need for treating the temporal dimension of context models as first-class-citizen. In this paper, we propose a graphand role-based context model (GRoCoMo), which includes expressive means for describing time and location. A query language enables for reasoning on current and historical data, as well as future trends. A manipulation language enables the specification of rewrite rules for updating context models based on situations detected within the context.