research-article

Towards matching improvement between spatio-temporal tasks and workers in mobile crowdsourcing market systems

Authors:

André Sales Fonteles,

Sylvain Bouveret,

Jérôme GenselAuthors Info & Claims

MobiGIS '14: Proceedings of the Third ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

Pages 43 - 50

https://doi.org/10.1145/2675316.2675319

Published: 04 November 2014 Publication History

Abstract

Crowdsourcing market systems (CMS) are platforms that enable one to publish tasks that others are intended to accomplished. Usually, these are systems where users, called workers, perform tasks using desktop computers. Recently, mobile CMSs have appeared with tasks that exploit the mobility and the location of workers. For example, if a third party system requires a picture of a given place, it may publish a task asking for some worker to go there, take this picture and upload it. One problem of CMSs is that the more tasks they have, the harder it is for workers to find and choose one they are interested in. Besides, workers who accomplish tasks may have no particular experience and consequently provide bad results for tasks. In order to improve the matching between workers and spatio-temporal tasks in mobile CMSs, we propose a conceptual framework that consists of two mechanisms. One considers the requirements of a task for selecting suitable workers, while the other recommends tasks for a worker according to his preferences and skills. As a result, workers spend less time searching tasks, more working on it, providing results with higher quality.

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Cited By

Xie YWang YLi KZhou XLiu ZLi K(2023)Satisfaction-aware Task Assignment in Spatial CrowdsourcingInformation Sciences10.1016/j.ins.2022.11.081622(512-535)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.11.081
Aloufi EAlharthi RZohdy MAlsulami DAlrashdi IOlawoyin R(2020)An Efficient Approach for Task Assignment in Spatial Crowdsourcing2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)10.1109/IEMTRONICS51293.2020.9216447(1-5)Online publication date: Sep-2020
https://doi.org/10.1109/IEMTRONICS51293.2020.9216447
Park SKwon SLee U(2018)CampusWatchProceedings of the ACM on Human-Computer Interaction10.1145/32744032:CSCW(1-25)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1145/3274403
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Index Terms

Towards matching improvement between spatio-temporal tasks and workers in mobile crowdsourcing market systems
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Conferences

MobiGIS '14: Proceedings of the Third ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems

November 2014

11 pages

ISBN:9781450331425

DOI:10.1145/2675316

Editors:
Shashi Shekhar
University of Minnesota - Twin Cities
,
Chi-Yin Chow
City University of Hong Kong, Hong Kong

Copyright © 2014 ACM.

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Published: 04 November 2014

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SIGSPATIAL '14: 22nd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 4 - 7, 2014

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Cited By

Xie YWang YLi KZhou XLiu ZLi K(2023)Satisfaction-aware Task Assignment in Spatial CrowdsourcingInformation Sciences10.1016/j.ins.2022.11.081622(512-535)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.11.081
Aloufi EAlharthi RZohdy MAlsulami DAlrashdi IOlawoyin R(2020)An Efficient Approach for Task Assignment in Spatial Crowdsourcing2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)10.1109/IEMTRONICS51293.2020.9216447(1-5)Online publication date: Sep-2020
https://doi.org/10.1109/IEMTRONICS51293.2020.9216447
Park SKwon SLee U(2018)CampusWatchProceedings of the ACM on Human-Computer Interaction10.1145/32744032:CSCW(1-25)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1145/3274403
Zhang LXiong PRen WZhu T(2018)A differentially private method for crowdsourcing data submissionConcurrency and Computation: Practice and Experience10.1002/cpe.510031:19Online publication date: 21-Dec-2018
https://doi.org/10.1002/cpe.5100
Yang JCantador INurbakova DCortés-Cediel MBozzon AYang JSun ZBozzon AZhang JLarson M(2017)Recommender systems for citizensProceedings of the International Workshop on Recommender Systems for Citizens10.1145/3127325.3177871(1-4)Online publication date: 31-Aug-2017
https://dl.acm.org/doi/10.1145/3127325.3177871
Fonteles ABouveret SGensel J(2015)Opportunistic Trajectory Recommendation for Task Accomplishment in Crowdsourcing SystemsWeb and Wireless Geographical Information Systems10.1007/978-3-319-18251-3_11(178-190)Online publication date: 23-Apr-2015
https://doi.org/10.1007/978-3-319-18251-3_11

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