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Data Quality Mining: Employing Classifiers for Assuring consistent Datasets
 

Summary: Data Quality Mining: Employing Classifiers for
Assuring consistent Datasets
Fabian Grüning
Carl von Ossietzky Universität Oldenburg, Germany,
fabian.gruening@informatik.uni-oldenburg.de
Abstract: Independent from the concrete definition of the term "data qual-
ity" consistency always plays a major role. There are two main points
when dealing with the data quality of a database: Firstly, the data quality
has to be measured, and secondly, if is necessary, it must be improved. A
classifier can be used for both purposes regarding consistency demands by
calculating the distance of the classified value to the stored value for
measuring and using the classified value for correction.
Keywords: data mining, data quality, classifiers, ontology, utilities
1 Introduction
A good introduction of the main topics of the field of "data quality" can be
found in (Scannapieco et al. 2005) where a motivation is given and relevant
data quality dimensions are highlighted. Having discussed an ontology that
describes such a definition and the semantical integration of data quality
aspects into given data schemas using an ontological approach in Grüning
(2006) we now come to the appliance of data quality mining algorithms to

  

Source: Appelrath, Hans-Jürgen - Department für Informatik, Carl von Ossietzky Universität Oldenburg

 

Collections: Computer Technologies and Information Sciences