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In relational database theory, a functional dependency is the following constraint between two attribute sets in a relation: Given a relation R and attribute sets ,, X is said to functionally determine Y (written X → Y) if each X value is associated with precisely one Y value.
Database normalization is the process of structuring a relational database accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. It was first proposed by British computer scientist Edgar F. Codd as part of his relational model .
If a relational schema is in BCNF, then all redundancy based on functional dependency has been removed, [4] although other types of redundancy may still exist. A relational schema R is in Boyce–Codd normal form if and only if for every one of its functional dependencies X → Y, at least one of the following conditions hold: [5]
A database relation (e.g. a database table) is said to meet third normal form standards if all the attributes (e.g. database columns) are functionally dependent on solely a key, except the case of functional dependency whose right hand side is a prime attribute (an attribute which is strictly included into some key).
Fourth normal form (4NF) is a normal form used in database normalization. Introduced by Ronald Fagin in 1977, 4NF is the next level of normalization after Boyce–Codd normal form (BCNF). Whereas the second , third , and Boyce–Codd normal forms are concerned with functional dependencies , 4NF is concerned with a more general type of ...
There is a functional dependency from the candidate key to all the attributes in the relation. The superkeys of a relation are all the possible ways we can identify a row. The candidate keys are the minimal subsets of each superkey and as such, they are an important concept for the design of database schema.
A canonical cover for F (a set of functional dependencies on a relation scheme) is a set of dependencies such that F logically implies all dependencies in , and logically implies all dependencies in F.
Data redundancy leads to data anomalies and corruption and generally should be avoided by design; [5] applying database normalization prevents redundancy and makes the best possible usage of storage. [ 6 ]