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.