Data Dependencies Mining In Database by Removing Equivalent Attributes

Pradeep Sharma¹ , Vijay Kumar Verma²

Section:Research Paper, Product Type: Isroset-Journal
Vol.1 , Issue.4 , pp.7-11, Jul-2013

Online published on Aug 31, 2013

Copyright © Pradeep Sharma , Vijay Kumar Verma . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Data 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 [1, 2]. The discovery of Data Dependency from databases has recently become a significant research problem this paper, we propose a new algorithm, called DM_EC (dependency mining using Equivalent Candidates) for the discovery of all Dependency from a database. DM_EC takes advantage of the rich theory of Functional dependencies [1, 3, 4]. The use of Functional dependencies theory can reduce both the size of the dataset and the number of FDs to be checked by pruning redundant data and skipping the search that follow logically from the Functional dependencies already discovered. We show that our method is sound, that is, the pruning does not lead to loss of information. Experiments on datasets show that DM_EC can prune more candidates than previous methods [5].

Key-Words / Index Term :
DBMS Normalization, Data Dependencies Mining, Data Mining

References :
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