• Title/Summary/Keyword: MDR(MetaData Registry)

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A Data Taxonomy Methodology based on Their Origin (데이터 본질 기반의 데이터 분류 방법론)

  • Choi, Mi-Young;Moon, Chang-Joo;Baik, Doo-Kwon;Kwon, Ju-Hum;Lee, Young-Moo
    • Journal of KIISE:Computing Practices and Letters
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    • v.16 no.2
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    • pp.163-176
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    • 2010
  • The representative method to efficiently manage the organization's data is to avoid data duplication through the promotion of sharing and reusing existing data. The systematic structuring of existing data and efficient searching should be supported in order to promote the sharing and reusing of data. Without regard for these points, the data for the system development would be duplicated, which would deteriorate the quality of the data. Data taxonomy provides some methods that can enable the needed data elements to be searched quickly with a systematic order of managing data. This paper proposes that the Origin data taxonomy method can best maximize data sharing, reusing, and consolidation, and it can be used for Meta Data Registry (MDR) and Semantic Web efficiently. The Origin data taxonomy method constructs the data taxonomy structure built upon the intrinsic nature of data, so it can classify the data with independence from business classification. Also, it shows a deployment method for data elements used in various areas according to the Origin data taxonomy structure with a data taxonomic procedure that supports the proposed taxonomy. Based on this case study, the proposed data taxonomy and taxonomic procedure can be applied to real world data efficiently.