Dependency mining in relational databases
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Semantic query optimisation promises to free users from the need of understanding the intricacies of databases when making an efficient query. The aim of semantic query optimisation is to use knowledge for reformulating a query into one that may require less answering time than the original query. Most approaches have the disadvantage of presuming this knowledge to be given by an expert or stated in the data dictionary as integrity constraints. This drawback can be overcome by using discovered knowledge.
Discovering data about data in databases, i.e. metadata, entails a new point of view, because only states of databases are considered. A consequence of this new view is that data dependencies as metadata and their relationships have to be extended by an expanded axiomatisation in order to minimise the database access in the discovery process. In this paper, the expanded implication problem is discussed in order to decide entailment of functional dependencies. Results are an axiomatisation of functional dependencies, and the corresponding inference relation. The approach also discuss general properties of data mining approaches in relational databases.
KeywordsRelational Database Functional Dependency Inference Rule Integrity Constraint Inductive Logic Programming
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