NDLI: A Taxonomy of Dirty Data

Content Provider	Springer Nature Link
Author	Kim, Won Choi, Byoung Ju Hong, Eui Kyeong Kim, Soo Kyung Lee, Doheon
Copyright Year	2003
Abstract	Today large corporations are constructing enterprise data warehouses from disparate data sources in order to run enterprise-wide data analysis applications, including decision support systems, multidimensional online analytical applications, data mining, and customer relationship management systems. A major problem that is only beginning to be recognized is that the data in data sources are often “dirty”. Broadly, dirty data include missing data, wrong data, and non-standard representations of the same data. The results of analyzing a database/data warehouse of dirty data can be damaging and at best be unreliable. In this paper, a comprehensive classification of dirty data is developed for use as a framework for understanding how dirty data arise, manifest themselves, and may be cleansed to ensure proper construction of data warehouses and accurate data analysis. The impact of dirty data on data mining is also explored.
Starting Page	81
Ending Page	99
Page Count	19
File Format	PDF
ISSN	13845810
Journal	Data Mining and Knowledge Discovery
Volume Number	7
Issue Number	1
e-ISSN	1573756X
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2003-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Data Structures, Cryptology and Information Theory Information Storage and Retrieval Artificial Intelligence (incl. Robotics) Statistics Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Information Systems Computer Science Applications

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