Oberhofer, MartinBremer, LarsChkalova, MariyaGrust, TorstenNaumann, FelixBöhm, AlexanderLehner, WolfgangHärder, TheoRahm, ErhardHeuer, AndreasKlettke, MeikeMeyer, Holger2019-04-112019-04-112019978-3-88579-683-1https://dl.gi.de/handle/20.500.12116/21710Clerical 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.enIBM Master Data ManagementMDMMachine LearningRandom ForestXGBoostingSorted Neighborhood MethodData FusionMatchingClerical Task ProcessingDuplicate Detection10.18420/btw2019-251617-5468