A taxonomy of dirty data

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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.
Publisher
SPRINGER
Issue Date
2003-01
Language
English
Article Type
Article
Keywords

MULTIDATABASE SYSTEMS; RELATIONAL DATABASES; DATA QUALITY; FUZZY; HETEROGENEITY

Citation

DATA MINING AND KNOWLEDGE DISCOVERY, v.7, pp.81 - 99

ISSN
1384-5810
URI
http://hdl.handle.net/10203/18464
Appears in Collection
BiS-Journal Papers(저널논문)
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