The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of MATHEMATICAL MODELS AND METHODS IN APPLIED SCIENCES
Transactions ID Number: 17-226
Full Name: Nittaya Kerdprasop
Position: Associate Professor
Age: ON
Sex: Female
Address: Suranaree University of Technology, Computer Engineering
Country: THAILAND
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E-mail address: nittaya@sut.ac.th
Other E-mails: nittaya.k@gmail.com
Title of the Paper: Discovering Functional Dependencies Through the Structure Analysis of Bayesian Network
Authors as they appear in the Paper: Nittaya Kerdprasop and Kittisak Kerdprasop
Email addresses of all the authors: nittaya@sut.ac.th, kittisakThailand@gmail.com
Number of paper pages: 8
Abstract: Data mining is the process of searching through databases for interesting relationships that can turn into valuable information. The data mining methods have gained much interest from diverse sectors due to their great potential on revealing useful and actionable relationships. Among many potential applications, we focus our research study on the database analysis and design 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 ne!
twork. 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.
Keywords: Functional dependency discovery, Bayesian network, Data mining, Database design, Database normalization.
EXTENSION of the file: .pdf
Special (Invited) Session: Functional Dependency Discovery via Bayes Net Analysis
Organizer of the Session: 658-389
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IP ADDRESS: 203.158.4.227
