Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/646913
Title: Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing
Authors: Peng Cheng;Xiang Lian;Lei Chen;Jinsong Han;Jizhong Zhao
subject: g-divide-and-conquer algorithm|Multi-skill spatial crowdsourcing|cost-model-based adaptive algorithm|greedy algorithm
Year: 2016
Publisher: IEEE
Abstract: 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</italic> (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers&#x2019; 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, $g$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="chen-ieq1-2550041.gif"/></alternatives></inline-formula>-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.
Description: 
URI: http://localhost/handle/Hannan/185859
http://localhost/handle/Hannan/646913
ISSN: 1041-4347
volume: 28
issue: 8
Appears in Collections:2016

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Title: Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing
Authors: Peng Cheng;Xiang Lian;Lei Chen;Jinsong Han;Jizhong Zhao
subject: g-divide-and-conquer algorithm|Multi-skill spatial crowdsourcing|cost-model-based adaptive algorithm|greedy algorithm
Year: 2016
Publisher: IEEE
Abstract: 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</italic> (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers&#x2019; 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, $g$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="chen-ieq1-2550041.gif"/></alternatives></inline-formula>-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.
Description: 
URI: http://localhost/handle/Hannan/185859
http://localhost/handle/Hannan/646913
ISSN: 1041-4347
volume: 28
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7446292.pdf1.48 MBAdobe PDFThumbnail
Preview File
Title: Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing
Authors: Peng Cheng;Xiang Lian;Lei Chen;Jinsong Han;Jizhong Zhao
subject: g-divide-and-conquer algorithm|Multi-skill spatial crowdsourcing|cost-model-based adaptive algorithm|greedy algorithm
Year: 2016
Publisher: IEEE
Abstract: 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</italic> (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers&#x2019; 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, $g$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="chen-ieq1-2550041.gif"/></alternatives></inline-formula>-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.
Description: 
URI: http://localhost/handle/Hannan/185859
http://localhost/handle/Hannan/646913
ISSN: 1041-4347
volume: 28
issue: 8
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7446292.pdf1.48 MBAdobe PDFThumbnail
Preview File