Quality Management of Workers in an In-House Crowdsourcing-Based Framework for Deduplication of Organizations' Databases

Saberi, M; Hussain, OK; Chang, E

Quality Management of Workers in an In-House Crowdsourcing-Based Framework for Deduplication of Organizations' Databases

Saberi, M

Hussain, OK Chang, E

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Access, 2019, 7, pp. 90715-90730
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Hussain, OK
dc.contributor.author	Chang, E
dc.date.accessioned	2020-05-11T06:27:12Z
dc.date.available	2020-05-11T06:27:12Z
dc.date.issued	2019-01-01
dc.identifier.citation	IEEE Access, 2019, 7, pp. 90715-90730
dc.identifier.issn	2169-3536
dc.identifier.issn	2169-3536
dc.identifier.uri	http://hdl.handle.net/10453/140633
dc.description.abstract	© 2013 IEEE. While organizations in the current era of big data are generating massive volumes of data, they also need to ensure that its quality is maintained for it to be useful in decision-making purposes. The problem of dirty data plagues every organization. One aspect of dirty data is the presence of duplicate data records that negatively impact the organization's operations in many ways. Many existing approaches attempt to address this problem by using traditional data cleansing methods. In this paper, we address this problem by using an in-house crowdsourcing-based framework, namely, DedupCrowd. One of the main obstacles of crowdsourcing-based approaches is to monitor the performance of the crowd, by which the integrity of the whole process is maintained. In this paper, a statistical quality control-based technique is proposed to regulate the performance of the crowd. We apply our proposed framework in the context of a contact center, where the Customer Service Representatives are used as the crowd to assist in the process of deduplicating detection. By using comprehensive working examples, we show how the different modules of the DedupCrowd work not only to monitor the performance of the crowd but also to assist in duplicate detection.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Access
dc.relation.isbasedon	10.1109/ACCESS.2019.2924979
dc.rights	© 2019 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.	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.title	Quality Management of Workers in an In-House Crowdsourcing-Based Framework for Deduplication of Organizations' Databases
dc.type	Journal Article
utslib.citation.volume	7
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2020-05-11T06:27:08Z
pubs.publication-status	Published
pubs.volume	7
utslib.start-page	90715

Abstract:

© 2013 IEEE. While organizations in the current era of big data are generating massive volumes of data, they also need to ensure that its quality is maintained for it to be useful in decision-making purposes. The problem of dirty data plagues every organization. One aspect of dirty data is the presence of duplicate data records that negatively impact the organization's operations in many ways. Many existing approaches attempt to address this problem by using traditional data cleansing methods. In this paper, we address this problem by using an in-house crowdsourcing-based framework, namely, DedupCrowd. One of the main obstacles of crowdsourcing-based approaches is to monitor the performance of the crowd, by which the integrity of the whole process is maintained. In this paper, a statistical quality control-based technique is proposed to regulate the performance of the crowd. We apply our proposed framework in the context of a contact center, where the Customer Service Representatives are used as the crowd to assist in the process of deduplicating detection. By using comprehensive working examples, we show how the different modules of the DedupCrowd work not only to monitor the performance of the crowd but also to assist in duplicate detection.

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http://hdl.handle.net/10453/140633