OASSIS: query driven crowd mining

Crowd data sourcing is increasingly used to gather infor-mation from the crowd and to obtain recommendations. In this paper, we explore a novel approach that broadens crowd data sourcing by enabling users to pose general questions, to mine the crowd for potentially relevant data, and to re-ceive concise, relevant answers that represent frequent, sig-nificant data patterns. Our approach is based on (1) a sim-ple generic model that captures both ontological knowledge as well as the individual history or habits of crowd mem-bers from which frequent patterns are mined; (2) a query language in which users can declaratively specify their in-formation needs and the data patterns of interest; (3) an efficient query evaluation algorithm, which enables mining semantically concise answers while minimizing the number of questions posed to the crowd; and (4) an implementa-tion of these ideas that mines the crowd through an interac-tive user interface. Experimental results with both real-life crowd and synthetic data demonstrate the feasibility and effectiveness of the approach