Highly sensorised systems present two parallel challenges: how to design a sensor suite that can efficiently and cost-effectively support the needs of given services; and to extract the semantically relevant interpretations, or ldquosituationsrdquo, from the flood of context data collected by the sensors. We describe mathematical structures called situation lattices that can be used to address these two problems simultaneously, allowing designers to both design and refine situation identification whilst offering insights into the design of sensor suites. We validate the accuracy and efficiency of our technique against a third-party data set and demonstrate how it can be used to evaluate sensor suite designs.

Construct differs from other pervasive systems platforms in a number of key respects. It is completely standards-based, using RDF as its data exchange model and ZeroConf for resource discovery. It supports a knowledge-centric model of interaction where clients' actions are driven by queries and triggers about the context of the system. It uses gossiping to maintain a consistent state across a distributed data structure, which maximises robustness and scalability and avoids many problems with hot-spots and hot-paths in communications. Finally, it treats all information sources uniformly as sensors acting as inputs to uncertain reasoning algorithms.

Group recommender systems introduce a whole set of new challenges for recommender systems research. The notion of generating a set of recommendations that will satisfy a group of users, with potentially competing interests, is challenging in itself. In addition to this we must consider how to record and combine the preferences of many different users as they engage in simultaneous recommendation dialogs. In this paper we introduce a group recommender system that is designed to provide assistance to a group of friends trying to plan a skiing vacation.