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dc.contributor.authorVisengeriyeva, L
dc.contributor.authorAkbik, A
dc.contributor.authorKaul, Manohar
dc.contributor.authorRabl, T
dc.contributor.authorMarkl, V
dc.date.accessioned2019-10-28T20:21:00Z
dc.date.available2019-10-28T20:21:00Z
dc.date.created2018-09-05 00:44
dc.date.issued2016
dc.identifieroai:raiith.iith.ac.in:2469
dc.identifierhttp://raiith.iith.ac.in/2469/1/larysa.pdf
dc.identifierVisengeriyeva, L and Akbik, A and Kaul, Manohar and Rabl, T and Markl, V (2016) Improving Data Quality by Leveraging Statistical Relational Learning. In: International Conference on Information Quality, 22-23 June, 2016, Ciudad Real, Spain.
dc.identifier.urihttp://hdl.handle.net/20.500.12424/2486248
dc.description.abstractDigitally collected data su
 ↵
 ers from many data quality issues, such as duplicate, incorrect, or incomplete data. A common
 approach for counteracting these issues is to formulate a set of data cleaning rules to identify and repair incorrect, duplicate and
 missing data. Data cleaning systems must be able to treat data quality rules holistically, to incorporate heterogeneous constraints
 within a single routine, and to automate data curation. We propose an approach to data cleaning based on statistical relational
 learning (SRL). We argue that a formalism - Markov logic - is a natural fit for modeling data quality rules. Our approach
 allows for the usage of probabilistic joint inference over interleaved data cleaning rules to improve data quality. Furthermore, it
 obliterates the need to specify the order of rule execution. We describe how data quality rules expressed as formulas in first-order
 logic directly translate into the predictive model in our SRL framework.
dc.format.mediumtext
dc.languageen
dc.language.isoeng
dc.relation.ispartofhttp://raiith.iith.ac.in/2469/
dc.subjectBig Data Analytics
dc.titleImproving Data Quality by Leveraging Statistical Relational
 Learning
dc.typeConference or Workshop Item
ge.collectioncodeOAIDATA
ge.dataimportlabelOAI metadata object
ge.identifier.legacyglobethics:15134962
ge.identifier.permalinkhttps://www.globethics.net/gel/15134962
ge.lastmodificationdate2018-09-05 00:44
ge.lastmodificationuseradmin@pointsoftware.ch (import)
ge.submissions0
ge.oai.exportid149801
ge.oai.repositoryid100146
ge.oai.setnameStatus = Published
ge.oai.setnameSubject = Computer science: Big Data Analytics
ge.oai.setnameType = Conference or Workshop Item
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ge.linkhttp://raiith.iith.ac.in/2469/1/larysa.pdf


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