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A methodology for developing data taxonomy for data architecture

by Mi Young Choi, Jong Youn Bae, Chang Joo Moon, Doo Kwon Baik
NCM 2009 - 5th International Joint Conference on INC, IMS, and IDC ()
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A large scale organization's data architecture should be able to offer a method to share and reuse existing data. It should also suppress data duplication for efficient data management and high data quality. For this purpose efficient data search and systematic building of existing data should be supported. Were it not for these points, the data isolated from system development would call for data duplication and deteriorate the quality of data. Therefore data taxonomy methodology and data taxonomic procedures are necessary to build a data structuralization and efficient data search. Data taxonomy provides some methods to enable needed data elements to be searched fast and also it offers some advantages for adaptable techniques to the same data elements in one classification system such as analysis, statistical forecasting, and maintenance. This article suggests a data taxonomy for fast data search for data sharing and reuse. Since data are engaged in different systems they can be candidates for data consolidation or integration through data taxonomy, too. In order to meet this purpose, data taxonomy should be independent from other classifications but for data itself. This article's data taxonomy is built upon the intrinsic nature of data based on data creation. Also this article shows a deployment method for data elements used in various areas according to a suggested taxonomy with a data taxonomic procedure. With a case study, this article shows that a suggested data taxonomy and taxonomic procedure can be applied to real world data.

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