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ThinkMind // INTELLI 2013, The Second International Conference on Intelligent Systems and Applications // View article intelli_2013_2_40_80123


Bayes Net Analysis to Support Database Design and Normalization

Authors:
Nittaya Kerdprasop
Kittisak Kerdprasop

Keywords: functional dependency; knowledge discovery; data mining; Bayesian network; database design; normalization

Abstract:
Knowledge discovery, also known as data mining, has gained much interest from diverse sectors due to their great potential on revealing potentially useful relationships. Among many possible applications, we focus our research on the database design and analysis application. Functional dependency plays a key role in database normalization, which is a systematic process of verifying database design to ensure the nonexistence of undesirable characteristics. Bad design could incur insertion, update, and deletion anomalies that are the major cause of database inconsistency. In this paper, we propose a novel technique to discover functional dependencies from the database table. The discovered dependencies help the database designers covering up inefficiencies inherent in their design. Our discovery technique is based on the structure analysis of Bayesian network. Most data mining techniques applied to the problem of functional dependency discovery are rule learning and association mining. Our work is a novel attempt of applying the Bayesian network to this area of application. The proposed technique can reveal functional dependencies within a reduced search space. Therefore, computational complexity is acceptable.

Pages: 42 to 47

Copyright: Copyright (c) IARIA, 2013

Publication date: April 21, 2013

Published in: conference

ISSN: 2308-4065

ISBN: 978-1-61208-269-1

Location: Venice, Italy

Dates: from April 21, 2013 to April 26, 2013

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