Machine Learning Applied to the Clerical Task Management Problem in Master Data Management Systems
| dc.contributor.author | Oberhofer, Martin | |
| dc.contributor.author | Bremer, Lars | |
| dc.contributor.author | Chkalova, Mariya | |
| dc.contributor.editor | Grust, Torsten | |
| dc.contributor.editor | Naumann, Felix | |
| dc.contributor.editor | Böhm, Alexander | |
| dc.contributor.editor | Lehner, Wolfgang | |
| dc.contributor.editor | Härder, Theo | |
| dc.contributor.editor | Rahm, Erhard | |
| dc.contributor.editor | Heuer, Andreas | |
| dc.contributor.editor | Klettke, Meike | |
| dc.contributor.editor | Meyer, Holger | |
| dc.date.accessioned | 2019-04-11T07:21:25Z | |
| dc.date.available | 2019-04-11T07:21:25Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | 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. | en |
| dc.identifier.doi | 10.18420/btw2019-25 | |
| dc.identifier.isbn | 978-3-88579-683-1 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/21710 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | BTW 2019 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) – Proceedings, Volume P-289 | |
| dc.subject | IBM Master Data Management | |
| dc.subject | MDM | |
| dc.subject | Machine Learning | |
| dc.subject | Random Forest | |
| dc.subject | XGBoosting | |
| dc.subject | Sorted Neighborhood Method | |
| dc.subject | Data Fusion | |
| dc.subject | Matching | |
| dc.subject | Clerical Task Processing | |
| dc.subject | Duplicate Detection | |
| dc.title | Machine Learning Applied to the Clerical Task Management Problem in Master Data Management Systems | en |
| gi.citation.endPage | 431 | |
| gi.citation.startPage | 419 | |
| gi.conference.date | 4.-8. März 2019 | |
| gi.conference.location | Rostock | |
| gi.conference.sessiontitle | Industriebeiträge |
Dateien
Originalbündel
1 - 1 von 1