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In-route task selection in crowdsourcing

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Mario A. NascimentoAuthors Info & Claims

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 524 - 527

https://doi.org/10.1145/3274895.3274981

Published: 06 November 2018 Publication History

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Abstract

We consider a spatial crowdsourcing scenario where (1) a worker is traveling on a preferred/typical path within a road network where (2) there is a set of tasks, each associated with a positive reward, available to be performed and (3) that the worker is willing to possibly deviate from his/her preferred path to perform tasks as long as (4) he/she travels at most a total given distance/time. We name this the In-Route Task Selection (IRTS) problem and investigate it using the skyline paradigm in order to obtain a set of diverse solutions yielding good combinations of detour and reward. Given the NP-hardness of the IRTS problem we present a heuristic approach that produces solutions with good values of precision and recall for problems of realistic sizes within practical query processing time.

References

[1]

Elham Ahmadi and Mario A. Nascimento. 2017. Datasets of Roads, Public Transportation and Points-of-Interest in Amsterdam, Oslo and Berlin. In: https://sites.google.com/ualberta.ca/nascimentodatasets/.

Google Scholar

[2]

Stephan Borzsony, Donald Kossmann, and Konrad Stocker. 2001. The skyline operator. In IEEE ICDE. 421--430.

Digital Library

Google Scholar

[3]

Peng Cheng et al. 2015. Reliable diversity-based spatial crowdsourcing by moving workers. Proceedings of the VLDB Endowment 8, 10 (2015), 1022--1033.

Digital Library

Google Scholar

[4]

C. F. Costa and M. A. Nascimento. 2018. In-Route Task Selection in Crowdsourcing. ArXiv e-prints (Sept. 2018). arXiv:cs.DB/1809.05234

Digital Library

Google Scholar

[5]

Khanh-Hung Dang and Kim-Tuyen Cao. 2013. Towards reward-based spatial crowdsourcing. In ICCAIS. 363--368.

Google Scholar

[6]

Dingxiong Deng, Cyrus Shahabi, and Ugur Demiryurek. 2013. Maximizing the number of worker's self-selected tasks in spatial crowdsourcing. In ACM SIGSPATIAL. 324--333.

Digital Library

Google Scholar

[7]

Dingxiong Deng, Cyrus Shahabi, and Linhong Zhu. 2015. Task matching and scheduling for multiple workers in spatial crowdsourcing. In ACM SIGSPATIAL. 21.

Digital Library

Google Scholar

[8]

Bruce L Golden, Larry Levy, and Rakesh Vohra. 1987. The orienteering problem. Naval research logistics (1987), 307--318.

Google Scholar

[9]

Shashi Shekhar and Jin Soung Yoo. 2003. Processing in-route nearest neighbor queries: a comparison of alternative approaches. In ACM GIS. 9--16.

Digital Library

Google Scholar

[10]

Hien To et al. 2016. Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints. In IEEE PerCom. 1--8.

Google Scholar

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Xie YWu FZhou XLuo WYin YZimmermann RLi KLi K(2024)Trajectory-Aware Task Coalition Assignment in Spatial CrowdsourcingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333664236:11(7201-7216)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2023.3336642
Wang STang SRuan SLong CLiang YLi QYuan ZBao JZheng Y(2024)Urban Sensing for Multi-Destination Workers via Deep Reinforcement Learning2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00318(4167-4179)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00318
Zhang SLuo ZYang LTeng FLi T(2024)A survey of route recommendations: Methods, applications, and opportunitiesInformation Fusion10.1016/j.inffus.2024.102413(102413)Online publication date: Apr-2024
https://doi.org/10.1016/j.inffus.2024.102413
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Index Terms

In-route task selection in crowdsourcing
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems
      2. Location based services

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Published In

SIGSPATIAL '18: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2018

655 pages

ISBN:9781450358897

DOI:10.1145/3274895

General Chairs:
Farnoush Banaei-Kashani
University of Colorado, Denver
,
Erik Hoel
Esri
,
Program Chairs:
Ralf Hartmut Güting
FernUniversität in Hagen, Germany
,
Roberto Tamassia
Brown University
,
Li Xiong
Emory University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2018

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SIGSPATIAL

SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6 - 9, 2018

Washington, Seattle

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SIGSPATIAL '18 Paper Acceptance Rate 30 of 150 submissions, 20%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

View all

Xie YWu FZhou XLuo WYin YZimmermann RLi KLi K(2024)Trajectory-Aware Task Coalition Assignment in Spatial CrowdsourcingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.333664236:11(7201-7216)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2023.3336642
Wang STang SRuan SLong CLiang YLi QYuan ZBao JZheng Y(2024)Urban Sensing for Multi-Destination Workers via Deep Reinforcement Learning2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00318(4167-4179)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00318
Zhang SLuo ZYang LTeng FLi T(2024)A survey of route recommendations: Methods, applications, and opportunitiesInformation Fusion10.1016/j.inffus.2024.102413(102413)Online publication date: Apr-2024
https://doi.org/10.1016/j.inffus.2024.102413
Embarak O(2023)Decoding the Black Box: A Comprehensive Review of Explainable Artificial Intelligence2023 9th International Conference on Information Technology Trends (ITT)10.1109/ITT59889.2023.10184238(108-113)Online publication date: 24-May-2023
https://doi.org/10.1109/ITT59889.2023.10184238
Bu HKuwabara K(2021)Task Selection Based on Worker Performance Prediction in Gamified CrowdsourcingAgents and Multi-Agent Systems: Technologies and Applications 202110.1007/978-981-16-2994-5_6(65-75)Online publication date: 8-Jun-2021
https://doi.org/10.1007/978-981-16-2994-5_6
Zhen YKhan ANazir SHuiqi ZAlharbi AKhan S(2021)Crowdsourcing usage, task assignment methods, and crowdsourcing platformsJournal of Software: Evolution and Process10.1002/smr.236833:8Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1002/smr.2368
Gummidi SPedersen TXie X(2020)Transit-based Task Assignment in Spatial CrowdsourcingProceedings of the 32nd International Conference on Scientific and Statistical Database Management10.1145/3400903.3400929(1-12)Online publication date: 7-Jul-2020
https://dl.acm.org/doi/10.1145/3400903.3400929
Tong YZhou ZZeng YChen LShahabi C(2019)Spatial crowdsourcing: a surveyThe VLDB Journal10.1007/s00778-019-00568-7Online publication date: 29-Aug-2019
https://doi.org/10.1007/s00778-019-00568-7

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