Predictive task assignment in spatial crowdsourcing: A data-driven approach
With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the major issues in spatial crowdsourcing is task assignment, which allocates task...
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sg-smu-ink.sis_research-66552021-05-31T03:45:20Z Predictive task assignment in spatial crowdsourcing: A data-driven approach ZHAO, Yan ZHENG, Kai CUI, Yue SU, Han ZHU, Feida ZHOU, Xiaofang With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the major issues in spatial crowdsourcing is task assignment, which allocates tasks to appropriate workers. However, existing works generally assume the static offline scenarios, where the spatio-temporal information of all the workers and tasks is determined and known a priori. Ignorance of the dynamic spatio-temporal distributions of workers and tasks can often lead to poor assignment results. In this work we study a novel spatial crowdsourcing problem, namely Predictive Task Assignment (PTA), which aims to maximize the number of assigned tasks by taking into account both current and future workers/tasks that enter the system dynamically with location unknown in advance. We propose a two-phase data-driven framework. The prediction phase hybrids different learning models to predict the locations and routes of future workers and designs a graph embedding approach to estimate the distribution of future tasks. In the assignment component, we propose both greedy algorithm for large-scale applications and optimal algorithm with graph partition based decomposition. Extensive experiments on two real datasets demonstrate the effectiveness of our framework. 2020-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5652 info:doi/10.1109/ICDE48307.2020.00009 https://ink.library.smu.edu.sg/context/sis_research/article/6655/viewcontent/Predictive_task_assignment_in_spatial_crowdsourcing.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University prediction task assignment spatial crowdsourcing Databases and Information Systems Data Science |
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prediction task assignment spatial crowdsourcing Databases and Information Systems Data Science ZHAO, Yan ZHENG, Kai CUI, Yue SU, Han ZHU, Feida ZHOU, Xiaofang Predictive task assignment in spatial crowdsourcing: A data-driven approach |
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With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the major issues in spatial crowdsourcing is task assignment, which allocates tasks to appropriate workers. However, existing works generally assume the static offline scenarios, where the spatio-temporal information of all the workers and tasks is determined and known a priori. Ignorance of the dynamic spatio-temporal distributions of workers and tasks can often lead to poor assignment results. In this work we study a novel spatial crowdsourcing problem, namely Predictive Task Assignment (PTA), which aims to maximize the number of assigned tasks by taking into account both current and future workers/tasks that enter the system dynamically with location unknown in advance. We propose a two-phase data-driven framework. The prediction phase hybrids different learning models to predict the locations and routes of future workers and designs a graph embedding approach to estimate the distribution of future tasks. In the assignment component, we propose both greedy algorithm for large-scale applications and optimal algorithm with graph partition based decomposition. Extensive experiments on two real datasets demonstrate the effectiveness of our framework. |
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text |
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ZHAO, Yan ZHENG, Kai CUI, Yue SU, Han ZHU, Feida ZHOU, Xiaofang |
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ZHAO, Yan ZHENG, Kai CUI, Yue SU, Han ZHU, Feida ZHOU, Xiaofang |
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ZHAO, Yan |
title |
Predictive task assignment in spatial crowdsourcing: A data-driven approach |
title_short |
Predictive task assignment in spatial crowdsourcing: A data-driven approach |
title_full |
Predictive task assignment in spatial crowdsourcing: A data-driven approach |
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Predictive task assignment in spatial crowdsourcing: A data-driven approach |
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Predictive task assignment in spatial crowdsourcing: A data-driven approach |
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predictive task assignment in spatial crowdsourcing: a data-driven approach |
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Institutional Knowledge at Singapore Management University |
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2020 |
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https://ink.library.smu.edu.sg/sis_research/5652 https://ink.library.smu.edu.sg/context/sis_research/article/6655/viewcontent/Predictive_task_assignment_in_spatial_crowdsourcing.pdf |
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