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Challenges in Data Crowdsourcing

Authors:

Hector Garcia-Molina,

Manas Joglekar,

Adam Marcus,

Aditya Parameswaran,

Vasilis VerroiosAuthors Info & Claims

IEEE Transactions on Knowledge and Data Engineering, Volume 28, Issue 4

Pages 901 - 911

Published: 01 April 2016 Publication History

Abstract

Crowdsourcing refers to solving large problems by involving human workers that solve component sub-problems or tasks. In data crowdsourcing, the problem involves data acquisition, management, and analysis. In this paper, we provide an overview of data crowdsourcing, giving examples of problems that the authors have tackled, and presenting the key design steps involved in implementing a crowdsourced solution. We also discuss some of the open challenges that remain to be solved.

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Wang SDang D(2023)A Generative Answer Aggregation Model for Sentence-Level Crowdsourcing TasksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.314282135:4(3299-3312)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3142821
Zhang YZong RKou ZShang LWang D(2022)CrowdNAS: A Crowd-guided Neural Architecture Searching Approach to Disaster Damage AssessmentProceedings of the ACM on Human-Computer Interaction10.1145/35551796:CSCW2(1-29)Online publication date: 11-Nov-2022
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Zhang YZong RShang LKou ZWang DKlamma RO'Halloran SRokne J(2021)A deep contrastive learning approach to extremely-sparse disaster damage assessment in social sensingProceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3487351.3488318(151-158)Online publication date: 8-Nov-2021
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cover image IEEE Transactions on Knowledge and Data Engineering

IEEE Transactions on Knowledge and Data Engineering Volume 28, Issue 4

April 2016

248 pages

ISSN:1041-4347

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IEEE Educational Activities Department

United States

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Published: 01 April 2016

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Wang SDang D(2023)A Generative Answer Aggregation Model for Sentence-Level Crowdsourcing TasksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.314282135:4(3299-3312)Online publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1109/TKDE.2022.3142821
Zhang YZong RKou ZShang LWang D(2022)CrowdNAS: A Crowd-guided Neural Architecture Searching Approach to Disaster Damage AssessmentProceedings of the ACM on Human-Computer Interaction10.1145/35551796:CSCW2(1-29)Online publication date: 11-Nov-2022
https://dl.acm.org/doi/10.1145/3555179
Zhang YZong RShang LKou ZWang DKlamma RO'Halloran SRokne J(2021)A deep contrastive learning approach to extremely-sparse disaster damage assessment in social sensingProceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3487351.3488318(151-158)Online publication date: 8-Nov-2021
https://dl.acm.org/doi/10.1145/3487351.3488318
Anagnostopoulos AFazzone AVettraino GDemartini GZuccon GCulpepper JHuang ZTong H(2021)Skyline in Crowdsourcing with Imprecise ComparisonsProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482479(37-46)Online publication date: 26-Oct-2021
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Ai BWang WHua MJiang YJiang JZhou Y(2021)Enhancing the Quality in Crowdsourcing E-Markets Through Team Formation GamesIEEE Intelligent Systems10.1109/MIS.2020.298777936:4(13-23)Online publication date: 1-Jul-2021
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Hsiao LWu SChiang NRé CLevis P(2020)Creating Hardware Component Knowledge Bases with Training Data Generation and Multi-task LearningACM Transactions on Embedded Computing Systems10.1145/339190619:6(1-26)Online publication date: 29-Sep-2020
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Lukyanenko RParsons JWiersma YMaddah M(2019)Expecting the unexpectedMIS Quarterly10.25300/MISQ/2019/1443943:2(623-648)Online publication date: 1-Jun-2019
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Amer-Yahia SRoy S(2019)The ever evolving online labor marketProceedings of the VLDB Endowment10.14778/3352063.335211412:12(1978-1981)Online publication date: 1-Aug-2019
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Han SXu ZZeng YChen LBoncz PManegold SAilamaki ADeshpande AKraska T(2019)FluidProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3320238(1921-1924)Online publication date: 25-Jun-2019
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On Moderating Software Crowdsourcing Challenges

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Incentivizing Truthful Data Quality for Quality-Aware Mobile Data Crowdsourcing

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