Pervasive computing applications bring together heterogeneous network-connected devices, services and resources to enable context-aware information integration. The increasing adoption of pervasive computing technology in the healthcare domain offers a healthcare model that delivers high quality service with fewer resources. In this paper, we briefly review the existing pervasive healthcare solutions and propose a novel provenance-aware system design that can enhance the performance of such solutions by means of including provenance capture functionality. We argue that our system architecture can improve quality of clinical data, efficiency of its collection, and its integrating ability with other data sources. To demonstrate our system and explain its provenance capacity, we use a clinical research example in which a patient's condition is closely monitored in order to assess the safety and efficacy of medications and treatments prescribed to him.

We report on two experiments performed to test the importance of Term Order in automatic summarisation. Experiment one was undertaken as part of DUC 2004 to which three systems were submitted, each with a different summarisation approach. The system that used document Term Order outperformed those that did not use Term Order in the ROUGE evaluation. Experiment two made use of human evaluations of search engine results, comparing our Query Term Order summaries with a simulation of current Google search engine result summaries in terms of summary quality. Our QTO system's summaries aided users' relevance judgements to a significantly greater extent than Google's.