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A rule based taxonomy of dirty data.

Li, Lin; Peng, Taoxin; Kennedy, Jessie

Authors

Lin Li



Abstract

There is a growing awareness that high quality of data is a key to today’s business success and that dirty data existing within data sources is one of the causes of poor data quality. To ensure high quality data, enterprises need to have a process, methodologies and resources to monitor and analyze the quality of data, methodologies for preventing and/or detecting and repairing dirty data. Nevertheless, research shows that many enterprises do not pay adequate attention to the existence of dirty data and have not applied useful methodologies to ensure high quality data for their applications. One of the reasons is a lack of appreciation of the types and extent of dirty data. In practice, detecting and cleaning all the dirty data that exists in all data sources is quite expensive and unrealistic. The cost of cleaning dirty data needs to be considered for most of enterprises. This problem has not attracted enough attention from researchers. In this paper, a rule-based taxonomy of dirty data is developed. The proposed taxonomy not only provides a mechanism to deal with this problem but also includes more dirty data types than any of existing such taxonomies.

Presentation Conference Type Conference Paper (published)
Conference Name Annual International Academic Conference on Data Analysis, Data Quality and Metadata Management
Publication Date 2011
Deposit Date Feb 4, 2011
Publicly Available Date May 16, 2017
Journal GSTF JOurnal on Computing
Print ISSN 2010-2283
Peer Reviewed Peer Reviewed
Volume 1
Issue 2
Pages 140-148
Book Title Proceedings of Annual International Academic Conference on Data Analysis, Data Quality and Metadata Management
ISBN 978-981-08-6308-1
Keywords Data warehousing; dirty data; data cleansing; rule-based taxonomy;
Public URL http://researchrepository.napier.ac.uk/id/eprint/3887
Contract Date May 16, 2017

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