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Machine Learning Applied to the Clerical Task Management Problem in Master Data Management Systems

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2019

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Gesellschaft für Informatik, Bonn

Zusammenfassung

Clerical 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.

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Oberhofer, Martin; Bremer, Lars; Chkalova, Mariya (2019): Machine Learning Applied to the Clerical Task Management Problem in Master Data Management Systems. BTW 2019. DOI: 10.18420/btw2019-25. Gesellschaft für Informatik, Bonn. PISSN: 1617-5468. ISBN: 978-3-88579-683-1. pp. 419-431. Industriebeiträge. Rostock. 4.-8. März 2019

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