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dc.contributor.authorIlyas, Ihab
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.accessioned2019-04-11T07:21:21Z
dc.date.available2019-04-11T07:21:21Z
dc.date.issued2019
dc.identifier.isbn978-3-88579-683-1
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/21704
dc.description.abstractMachine learning tools promise to help solve data curation problems. While the principles are well understood, the engineering details in configuring and deploying ML techniques are the biggest hurdle. In this talk I discuss why leveraging data semantics and domain-specific knowledge is key in delivering the optimizations necessary for truly scalable ML curation solutions. The talk focuses on two main problems: (1) entity consolidation, which is arguably the most difficult data curation challenge because it is notoriously complex and hard to scale; and (2) using probabilistic inference to suggest data repair for identified errors and anomalies using our new system called HoloClean. Both problems have been challenging researchers and practitioners for decades due to the fundamentally combinatorial explosion in the space of solutions and the lack of ground truth. There’s a large body of work on this problem by both academia and industry. Techniques have included human curation, rules-based systems, and automatic discovery of clusters using predefined thresholds on record similarity Unfortunately, none of these techniques alone has been able to provide sufficient accuracy and scalability. The talk aims at providing deeper insight into the entity consolidation and data repair problems and discusses how machine learning, human expertise, and problem semantics collectively can deliver a scalable, high-accuracy solution.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.titleBuilding Scalable Machine Learning Solutions for Data Cleaningen
mci.reference.pages27-28
mci.conference.sessiontitleEingeladene Vorträge
mci.conference.locationRostock
mci.conference.date4.-8. März 2019
dc.identifier.doi10.18420/btw2019-02


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