Obiettivo
Knowledge Workers are increasingly involved in new kinds of organizational structures and work interaction patterns that require highly dynamic forms of collaboration, ranging from Nimble (short-lived) to Virtual and Mobile/Nomadic Teams. Teams morph from one kind of organizational structure to another. In most cases, workers engage in many such teams simultaneously and require support from adequate software services.
To meet the requirements of dynamic, multiform team working, current Internet-based Collaboration Working Environments must evolve towards large-scale, loosely-coupled, trusted service-oriented systems, with increased emphasis on P2P capabilities.
inContext will develop a novel scientific approach focused on a new blend of human collaboration and service-oriented systems that explores two basic research strands: efficient and effective support for human interactions and collaboration in various teams through dynamically aggregated software services; use of human-to-human or human-to-service interactions in applying intelligent mining and learning algorithms that can detect interaction patterns for pro-active service aggregation.
In addressing these issues, inContext will explore novel techniques and algorithms for mining human activities and providing context-relevant services, at the right time and granularity, to human interaction partners in those various team forms. To this end, relevance-based context representation models and autonomic service adaptation methods for context-coupling and enrichment will be developed.
Universities, corporate research centres and SMEs from six countries will work together to develop the research that will deliver the new technology. Two end-user partners - a worldwide manufacturing corporate and an association of public administration - will provide the reference environments for investigation and concrete validation of the outcomes of the project.