Managing general and individual knowledge in crowd mining applications

Crowd mining frameworks combine general knowledge, which can refer to an ontology or information in a database, with individual knowledge obtained from the crowd, which captures habits and preferences. To account for such mixed knowledge, along with user interaction and optimization issues, such frameworks must employ a complex process of reasoning, automatic crowd task generation and result analysis. In this paper, we describe a generic architecture for crowd mining applications. This architecture allows us to examine and compare the components of existing crowdsourcing systems and point out extensions required by crowd mining. It also highlights new research challenges and potential reuse of existing techniques/components. We exemplify this for the OASSIS project and for other prominent crowdsourcing frameworks.