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dc.contributor.authorGu, Binbin
dc.contributor.authorLi, Zhixu
dc.contributor.authorYang, Qiang
dc.contributor.authorXie, Qing
dc.contributor.authorLiu, An
dc.contributor.authorLiu, Guanfeng
dc.contributor.authorZheng, Kai
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2017-03-15T07:15:28Z
dc.date.available2017-03-15T07:15:28Z
dc.date.issued2017-03-08
dc.identifier.citationGu B, Li Z, Yang Q, Xie Q, Liu A, et al. (2017) Web-ADARE: A Web-Aided Data Repairing System. Neurocomputing. Available: http://dx.doi.org/10.1016/j.neucom.2016.09.132.
dc.identifier.issn0925-2312
dc.identifier.doi10.1016/j.neucom.2016.09.132
dc.identifier.urihttp://hdl.handle.net/10754/623010
dc.description.abstractData repairing aims at discovering and correcting erroneous data in databases. In this paper, we develop Web-ADARE, an end-to-end web-aided data repairing system, to provide a feasible way to involve the vast data sources on the Web in data repairing. Our main attention in developing Web-ADARE is paid on the interaction problem between web-aided repairing and rule-based repairing, in order to minimize the Web consultation cost while reaching predefined quality requirements. The same interaction problem also exists in crowd-based methods but this is not yet formally defined and addressed. We first prove in theory that the optimal interaction scheme is not feasible to be achieved, and then propose an algorithm to identify a scheme for efficient interaction by investigating the inconsistencies and the dependencies between values in the repairing process. Extensive experiments on three data collections demonstrate the high repairing precision and recall of Web-ADARE, and the efficiency of the generated interaction scheme over several baseline ones.
dc.description.sponsorshipThis research is partially supported by Natural Science Foundation of China (Grant No. 61303019, 61402313, 61472263, 61572336), Postdoctoral scientific research funding of Jiangsu Province (No. 1501090B) National 58 batch of postdoctoral funding (No. 2015M581859), Collaborative Innovation Center of Novel Software Technology and Industrialization, Jiangsu, China, and the King Abdullah University of Science and Technology.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0925231217304642
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, [, , (2017-03-08)] DOI: 10.1016/j.neucom.2016.09.132 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Repairing
dc.subjectRule-based Method
dc.subjectWeb-aided Method
dc.subjectMultiple Sources
dc.titleWeb-ADARE: A Web-Aided Data Repairing System
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journalNeurocomputing
dc.eprint.versionPost-print
dc.contributor.institutionSchool of Computer Science and Technology, Soochow University, China
dc.contributor.institutionSchool of Computer Science and Technology, Wuhan University of Technology, China
kaust.personZhang, Xiangliang
refterms.dateFOA2019-03-08T00:00:00Z
dc.date.published-online2017-03-08
dc.date.published-print2017-08


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