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Negative Side Effects of Denormalization-Oriented Data Modeling in Enterprise-Wide Database Design  

Rhee, Hae-Kyung (Yong-In Songdam College Dept. of Computer Game Information)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.43, no.6, 2006 , pp. 17-25 More about this Journal
Abstract
As information systems to be computerized get significantly scaled up, data modeling issues apparently considered to be crucial once again as the early 1980's under the terms of data governance, data architecture or data quality. Unfortuately, merely resorting to heuristics-based field approaches with more or less no firm theoretical foundation of knowledge with regard to criteria of data design lead quite often to major failures in efficacy of data modeling. In this paper, we have compared normalization-critical data modeling approach, well-known as the Non-Stop Data Modeling methodology in the literature, to the Information Engineering in which in many occasions the notion of do-normalization is supported and even recommended as a mandatory part in its modeling nature. Quantitative analyses have revealed that NS methodology ostensibly outperforms IE methodology in terms of efficiency indices like adequacy of entity judgement, degree of existence of data circulation path that confirms the balancedness of data design and ratio of unnecessary data attribute replication.
Keywords
Data Modeling; Non-Stop(NS) Data Modeling Methodology; Information Engineering Methodology; De-normalization; Data Circulation Path;
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