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A rule based taxonomy of dirty data.

Li, Lin, Peng, Taoxin and Kennedy, Jessie (2010) A rule based taxonomy of dirty data. In: Proceedings of Annual International Academic Conference on Data Analysis, Data Quality and Metadata Management. GSTF, Singapore. ISBN 978-981-08-6308-1

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Abstract/Description

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.

Item Type: Book Section
ISBN: 978-981-08-6308-1
Uncontrolled Keywords: Data warehousing; dirty data; data cleansing; rule-based taxonomy;
University Divisions/Research Centres: Faculty of Engineering, Computing and Creative Industries > School of Computing
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 005 Computer programming, programs & data
Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Item ID: 3887
Depositing User: Computing Research
Date Deposited: 04 Feb 2011 16:31
Last Modified: 19 Nov 2013 14:46
URI: http://researchrepository.napier.ac.uk/id/eprint/3887

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