@inbook{92465adacce14bf487965637208f9a4a,
title = "Comparative analysis of cost and elapsed time of normalization and de-normalization in the very large database",
abstract = "Today, data to be processed by information systems is rapidly increasing and complicated, resulting in data integration, standardization, and quality problems. The explosive increase in data is causing performance problems for users seeking the desired information and for operators targeting these users. The industry defines a normalization or de-normalization model and builds a database to solve the performance problems of this very large database. However, it is not well known how they affect actual performance. Therefore, it is necessary to confirm whether the database constructed from the normalized data models and the databases constructed from the data models considering the de-normalization actually contributes to the performance improvement, the development and the simplification of the operation. In this paper, we analyze the effectiveness of de-normalization cost and processing time in the Very Large Database based on the case of establishing database for business to business service of large retailers. As a result, the de-normalized database had 15\% faster processing time at a cost of 0.2\% of the normalized.",
keywords = "Database de-normalization, Database modeling, Database normalization, VLDB",
author = "Chun, \{Seok Tai\} and Jihyun Lee and Yoo, \{Cheol Jung\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2019.",
year = "2019",
doi = "10.1007/978-3-319-96803-2\_13",
language = "English",
series = "Studies in Computational Intelligence",
publisher = "Springer Verlag",
pages = "173--187",
booktitle = "Studies in Computational Intelligence",
}