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Article: A Crowdsourcing Framework for Collecting Tabular Data

TitleA Crowdsourcing Framework for Collecting Tabular Data
Authors
KeywordsTask analysis
Crowdsourcing
Computers
Cleaning
Data models
Issue Date2019
PublisherInstitute of Electrical and Electronics Engineers . The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp/?punumber=69
Citation
IEEE Transactions on Knowledge and Data Engineering, 2019, v. 32 n. 11, p. 2060-2074 How to Cite?
AbstractIn crowdsourcing, human workers are employed to tackle problems that are traditionally difficult for computers (e.g., data cleaning, missing value filling, and sentiment analysis). In this paper, we study the effective use of crowdsourcing in filling missing values in a given relation (e.g., a table containing different attributes of celebrity stars, such as nationality and age). A task given to a worker typically consists of questions about the missing attribute values (e.g., What is the age of Jet Li?). Although this problem has been studied before, existing work often treats related attributes independently, leading to suboptimal performance. In this paper, we present T-Crowd, which is a crowdsourcing system that considers attribute relationships. Particularly, T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. The attribute relationship information is used to guide task allocation to workers. Our solution seamlessly supports categorical and continuous attributes. Our extensive experiments on real and synthetic datasets show that T-Crowd outperforms state-of-the-art methods, improving the quality of truth inference and reducing the monetary cost of crowdsourcing.
Persistent Identifierhttp://hdl.handle.net/10722/291248
ISSN
2019 Impact Factor: 4.935
2015 SCImago Journal Rankings: 2.087
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorShan, C-
dc.contributor.authorMamoulis, N-
dc.contributor.authorLi, G-
dc.contributor.authorCheng, R-
dc.contributor.authorHUANG, Z-
dc.contributor.authorZHENG, Y-
dc.date.accessioned2020-11-07T13:54:26Z-
dc.date.available2020-11-07T13:54:26Z-
dc.date.issued2019-
dc.identifier.citationIEEE Transactions on Knowledge and Data Engineering, 2019, v. 32 n. 11, p. 2060-2074-
dc.identifier.issn1041-4347-
dc.identifier.urihttp://hdl.handle.net/10722/291248-
dc.description.abstractIn crowdsourcing, human workers are employed to tackle problems that are traditionally difficult for computers (e.g., data cleaning, missing value filling, and sentiment analysis). In this paper, we study the effective use of crowdsourcing in filling missing values in a given relation (e.g., a table containing different attributes of celebrity stars, such as nationality and age). A task given to a worker typically consists of questions about the missing attribute values (e.g., What is the age of Jet Li?). Although this problem has been studied before, existing work often treats related attributes independently, leading to suboptimal performance. In this paper, we present T-Crowd, which is a crowdsourcing system that considers attribute relationships. Particularly, T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. The attribute relationship information is used to guide task allocation to workers. Our solution seamlessly supports categorical and continuous attributes. Our extensive experiments on real and synthetic datasets show that T-Crowd outperforms state-of-the-art methods, improving the quality of truth inference and reducing the monetary cost of crowdsourcing.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers . The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp/?punumber=69-
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineering-
dc.rightsIEEE Transactions on Knowledge and Data Engineering. Copyright © Institute of Electrical and Electronics Engineers .-
dc.rights©20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectTask analysis-
dc.subjectCrowdsourcing-
dc.subjectComputers-
dc.subjectCleaning-
dc.subjectData models-
dc.titleA Crowdsourcing Framework for Collecting Tabular Data-
dc.typeArticle-
dc.identifier.emailShan, C: sxdtgg@hku.hk-
dc.identifier.emailMamoulis, N: nikos@cs.hku.hk-
dc.identifier.emailCheng, R: ckcheng@cs.hku.hk-
dc.identifier.authorityMamoulis, N=rp00155-
dc.identifier.authorityCheng, R=rp00074-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TKDE.2019.2914903-
dc.identifier.scopuseid_2-s2.0-85092536844-
dc.identifier.hkuros318666-
dc.identifier.volume32-
dc.identifier.issue11-
dc.identifier.spage2060-
dc.identifier.epage2074-
dc.identifier.isiWOS:000576417000001-
dc.publisher.placeUnited States-
dc.identifier.issnl1041-4347-

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