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dc.date.accessioned2020-05-15T19:02:29Z
dc.date.available2020-05-15T19:02:29Z
dc.date.created2020-01-20T10:47:39Z
dc.date.issued2019
dc.identifier.citationSajid, Saliha Zernichow, Bjørn Marius von Soylu, Ahmet Roman, Dumitru . Predictive Data Transformation Suggestions in Grafterizer Using Machine Learning. Communications in Computer and Information Science. 2019, 1057
dc.identifier.urihttp://hdl.handle.net/10852/75659
dc.description.abstractData preprocessing is a crucial step in data analysis. A substantial amount of time is spent on data transformation tasks such as data formatting, modification, extraction, and enrichment, typically making it more convenient for users to work with systems that can recommend most relevant transformations for a given dataset. In this paper, we propose an approach for generating relevant data transformation suggestions for tabular data preprocessing using machine learning (specifically, the Random Forest algorithm). The approach is implemented for Grafterizer, a Web-based framework for tabular data cleaning and transformation, and evaluated through a usability study.en_US
dc.languageEN
dc.titlePredictive Data Transformation Suggestions in Grafterizer Using Machine Learningen_US
dc.typeJournal articleen_US
dc.creator.authorSajid, Saliha
dc.creator.authorZernichow, Bjørn Marius von
dc.creator.authorSoylu, Ahmet
dc.creator.authorRoman, Dumitru
cristin.unitcode185,0,0,0
cristin.unitnameUniversitetet i Oslo
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.cristin1777347
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Communications in Computer and Information Science&rft.volume=1057&rft.spage=&rft.date=2019
dc.identifier.jtitleCommunications in Computer and Information Science
dc.identifier.volume1057
dc.identifier.startpage137
dc.identifier.endpage149
dc.identifier.doihttps://doi.org/10.1007/978-3-030-36599-8_12
dc.identifier.urnURN:NBN:no-78773
dc.type.documentTidsskriftartikkelen_US
dc.type.peerreviewedPeer reviewed
dc.source.issn1865-0929
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/75659/2/MTSR2019_Camera_Ready.pdf
dc.type.versionAcceptedVersion


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