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Large Scale Windowed Matching

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cris.lastimport.scopus2024-02-12T20:20:32Z
dc.abstract.enMissing or invalid records in sales data are a common obstacle that can damage the overall effectiveness of market analysis. Completing the data on the basis of the records obtained so far can be formulated in means of a schema matching task. In this paper we present a machine learning based method for performing schema matching for transactional data. The analysis is based on a dataset of over 700.000 transactions from retail stores. We confront the proposed solution with manual and conventional approaches.
dc.affiliationUniwersytet Warszawski
dc.conference.countryJaponia
dc.conference.datefinish2022-12-20
dc.conference.datestart2022-12-17
dc.conference.placeOsaka
dc.conference.seriesIEEE International Conference on Big Data
dc.conference.seriesIEEE International Conference on Big Data
dc.conference.seriesshortcutBigData
dc.conference.shortcutIEEE BigData 2022
dc.conference.weblinkhttp://bigdataieee.org/BigData2022/index.html
dc.contributor.authorCiebiera, Krzysztof
dc.contributor.authorPrzyborowski, Mateusz
dc.contributor.authorStencel, Krzysztof
dc.date.accessioned2024-01-25T04:59:38Z
dc.date.available2024-01-25T04:59:38Z
dc.date.issued2022
dc.description.financePublikacja bezkosztowa
dc.identifier.doi10.1109/BIGDATA55660.2022.10020606
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/110882
dc.identifier.weblinkhttp://xplorestaging.ieee.org/ielx7/10020192/10020156/10020606.pdf?arnumber=10020606
dc.languageeng
dc.pbn.affiliationcomputer and information sciences
dc.relation.pages6253-6255
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.titleLarge Scale Windowed Matching
dc.typeJournalArticle
dspace.entity.typePublication