View article

Best objects finding is a fundamental operation in decision support systems and applications. When numerical values of objects cannot be obtained from existing computer systems or in a machine learning manner, crowdsourcing proves a viable approach via harnessing human intelligence for data gathering. Most of existing studies ask crowds to submit pairwise preferences where a large number of crowdsourced questions are produced, thereby incurring huge monetary cost and long latency. To address this issue, we propose a framework for efficient best objects computation by leveraging crowdsourcing to provide object values. The framework employs three query operators (ie, top-k, k nn, and skyline queries) to compute best objects, and minimizes the number of crowdsourced objects by eagerly pruning non-result objects via superiority probability based ordering. We first propose the concept of superiority …