Application of provenance in social computing: A case study

Abstract

Complex systems such as Collective Adaptive Systems that include a variety of resources, are increasingly being designed to include people in task-execution. Collectives, encapsulating human resources/services, represent one type of an application within which people with different type of skills can be engaged to solve one common problem or work on the same project. Mechanisms of managing social collectives are dependent on functional and non-functional parameters of members of social collectives. In this work, we investigate the benefits provenance can offer to social computing and trade-off implications. We show experimental results of how provenance data can help better visualize interaction and performance data during a collective's run-time. We present novel metrics that can be derived from provenance, and lastly, we discuss privacy implications. If utilized ethically, provenance can help in developing more efficient provisioning and management mechanisms in social computing.