Data Dependencies Mining In Database by Removing Equivalent Attributes

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]. KeywordsDBMS Normalization, Data Dependencies Mining, Data Mining