Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/138223
Title: CROWDSOURCING UNCERTAINTIES AND OPTIMIZATION
Authors: LIU QING
Keywords: uncertainty,ranking,top-k queries,spatial task assignment,pricing,team formation
Issue Date: 14-Aug-2017
Source: LIU QING (2017-08-14). CROWDSOURCING UNCERTAINTIES AND OPTIMIZATION. ScholarBank@NUS Repository.
Abstract: 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.
URI: http://scholarbank.nus.edu.sg/handle/10635/138223
Appears in Collections:Ph.D Theses (Open)

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