SWAT: Social Web Application for Team Recommendation

Team recommendation aids decision support, by not only identifying individuals who are experts for various aspects of a complex task, but also determining various properties of the team as a group. Several aspects such as cohesion and repetition of teams have been identified as important indicators, besides individuals' expertise, in determining how well a team performs. While such information often do not exist explicitly, digital footprint of users' activities can be harnessed to retrieve the same from diverse sources. In this work, we lay out a proof-of-concept on how to do so in the case of scientific knowledge workers, as well as demonstrate some necessary visualization, manipulation and communication tools to determine and manage multi-disciplinary teams. While the focus of our presentation is the specific application 'SWAT' for team recommendation, it also serves as a vehicle demonstrating how, in general, apparently disparate data sources can be harnessed to provide decision support guided by suitable analytics.