In a crowdsourcing application, the multiplicity and diversity of the collected information result in uncertainty. The uncertainty gives rise to the question of how to rank items according to the uncertain information. We study top-k queries based on the uncertain scores of the items. We devise effective and efficient algorithms that compute the top items in a ranking. Spatial crowdsourcing applications are helpful in alleviating uncertainty. We study the assignment of spatial tasks to the crowd workers. We design efficient and effective task assignment strategies for the assignment of the spatial tasks. We also consider the assignment of a complex task that requires a variety of skills. We design four incentive mechanisms for selecting workers to form a valid team that can complete the complex task and for determining the payment to each worker.
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