An Optimization Approach to Group Coupling in Heterogeneous Collaborative Systems

Abstract

Recent proliferation of computing devices has brought attention to heterogeneous collaborative systems, where key challenges arise from the resource limitations and disparities. Sharing data across disparate devices makes it necessary to employ mechanisms for adapting the original data and presenting it to the user in the best possible way. However, this could represent a major problem for effective collaboration, since users may find it difficult to reach consensus with everyone working with individually tailored data. This paper presents a novel approach to controlling the coupling of heterogeneous collaborative systems by combining concepts from complex systems and data adaptation techniques. The key idea is that data must be adapted to each individual's preferences and resource capabilities. To support and promote collaboration this adaptation must be interdependent, and adaptation performed by one individual should influence the adaptation of the others. These influences are defined according to the user's roles and collaboration requirements. We model the problem as a distributed optimization problem, so that the most useful data--both for the individual and the group as a whole--is scheduled for each user, while satisfying their preferences, their resource limitations, and their mutual influences. We show how this approach can be applied in a collaborative 3D design application and how it can be extended to other applications.