An abstract model for identifying potential teams and communities

The Abstract Computational Model of Awareness for Community Identification (AMACI) is based on the contents of resources created or used by users, which allows noticing others that perform or have performed activities in similar contexts, thereby identifying potential communities and teams. The model was evaluated through experiments using data from a sample of teachers and students at two universities. In order to evaluate the performance of the proposed model in community identification, two metrics were proposed: local and global performance. As a main result of experimentation, different uses for the model were found by combining the possible values for the local and global metrics. Such uses are the identification of new communities, identification of existing communities, expansion of existing communities, and the identification of teams. One of the main benefits of the model is providing an architectural pattern for developing this kind of application. Future works include the elimination of some manual steps in order to analyze larger communities than those observed ones; to look for other clues in e-texts and other types of resources that can help in identifying members of a community, make a temporal analysis of communities what can provide important information on the intellectual capital of an organization.