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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Main Authors: ZHAO, Yan, ZHENG, Kai, CUI, Yue, SU, Han, ZHU, Feida, ZHOU, Xiaofang
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Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access: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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spelling 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
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic prediction
task assignment
spatial crowdsourcing
Databases and Information Systems
Data Science
spellingShingle 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
description 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.
format text
author ZHAO, Yan
ZHENG, Kai
CUI, Yue
SU, Han
ZHU, Feida
ZHOU, Xiaofang
author_facet ZHAO, Yan
ZHENG, Kai
CUI, Yue
SU, Han
ZHU, Feida
ZHOU, Xiaofang
author_sort 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
title_fullStr Predictive task assignment in spatial crowdsourcing: A data-driven approach
title_full_unstemmed Predictive task assignment in spatial crowdsourcing: A data-driven approach
title_sort predictive task assignment in spatial crowdsourcing: a data-driven approach
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url 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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