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dc.contributor.authorOberhofer, Martin
dc.contributor.authorBremer, Lars
dc.contributor.authorChkalova, Mariya
dc.contributor.editorGrust, Torsten
dc.contributor.editorNaumann, Felix
dc.contributor.editorBöhm, Alexander
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorHärder, Theo
dc.contributor.editorRahm, Erhard
dc.contributor.editorHeuer, Andreas
dc.contributor.editorKlettke, Meike
dc.contributor.editorMeyer, Holger
dc.date.accessioned2019-04-11T07:21:25Z
dc.date.available2019-04-11T07:21:25Z
dc.date.issued2019
dc.identifier.isbn978-3-88579-683-1
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/21710
dc.description.abstractClerical tasks are created if a duplicate detection algorithm detects some similarity of records but not enough to allow an auto-merge operation. Data stewards review clerical tasks and make a final non-match or match decision. In this paper we evaluate different machine learning algorithms regarding their accuracy to predict the correct action for a clerical task and execute that action automatically if the prediction has sufficient confidence. This approach reduces the amount of work for data stewards by factors of magnitude.en
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofBTW 2019
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) – Proceedings, Volume P-289
dc.subjectIBM Master Data Management
dc.subjectMDM
dc.subjectMachine Learning
dc.subjectRandom Forest
dc.subjectXGBoosting
dc.subjectSorted Neighborhood Method
dc.subjectData Fusion
dc.subjectMatching
dc.subjectClerical Task Processing
dc.subjectDuplicate Detection
dc.titleMachine Learning Applied to the Clerical Task Management Problem in Master Data Management Systemsen
mci.reference.pages419-431
mci.conference.sessiontitleIndustriebeiträge
mci.conference.locationRostock
mci.conference.date4.-8. März 2019
dc.identifier.doi10.18420/btw2019-25


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