Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 28, Issue 8, Pages 2201-2215Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2016.2550041
Keywords
Multi-skill spatial crowdsourcing; greedy algorithm; g-divide-and-conquer algorithm; cost-model-based adaptive algorithm
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Funding
- Hong Kong RGC Project [N HKUST637/13]
- NSFC Guang Dong Grant [U1301253]
- National Grand Fundamental Research 973 Program of China [2014CB340303]
- Microsoft Research Asia Gift Grant
- Google Faculty Award 2013
- NSFC [61325013, 61572396, 61373175]
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With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, the spatial crowdsourcing refers to sending location-based requests to workers, based on their current positions. In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. Under this scenario, we will study an important problem, namely multi-skill spatial crowdsourcing (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers' benefits are maximized under the budget constraint. We prove that the MS-SC problem is NP-hard and intractable. Therefore, we propose three effective heuristic approaches, including greedy, g-divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.
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