Sharing Biomedical Learning Knowledge for Social Ambient Intelligence

In this paper, we describe a Bio-SAmI system, which is a Biomedical learning system that is context-aware and responsive to mobile learners sharing information on a social network. Bio-SAmI is a Web 2.0 enabled system which employees Social Ambient Intelligence techniques. The Bio-SAmI infrastructure is based on the Actor model that treats mobile users or “actors" as the universal primitives of computation. The actor model is implemented using a combination of Java enabled APIs including SALSA, JADE, LEAP and tuPrologME. The implemented prototype enable learners to share biomedical information represented by the DICOM SR standard in relation to the notion of inflammation as well as to respond to variety of learning queries including classifying learning case studies, finding learning case studies, locating a FOAF learner and syndication and aggregation of learning case studies .