Mobile Services for Enhancing Human Crowdsourcing with Computing Elements

Crowdsourcing enables one to leverage the power of the crowd. Normally, it involves utilizing humans for tasks that machines have difficulty performing. We propose a system, delivered as a mobile service, which dynamically adapts to the application domain and selects a combination of human and machine crowdsourcing components. Our work is towards the design of elastic systems that adaptively optimizes the use of human and automated software resources in order to maximize overall performance. We propose a performance model that predicts both human and machine outcomes for a certain task and then optimizes task assignment accordingly. Our experimentation shows that our proposed system significantly enhances the outcome precision of a crowdsourced task.