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Essential tuple normal form (ETNF) is a normal form used in database normalization. It lies strictly between fourth normal form (4NF) and fifth normal form (5NF). As per the original paper, ETNF, although strictly weaker than 5NF, is exactly as effective as 5NF in eliminating redundancy of tuples.
Hugh Darwen, C. J. Date and Ronald Fagin introduced ETNF in their paper in March 2012. [1]
Essential Tuple Normal Form (ETNF) is a concept in the field of database normalization, which is the process of organizing the attributes of a relational database to reduce redundancy and improve data integrity. ETNF is a specific normal form that aims to ensure that the database schema is free from undesirable redundancy and dependency anomalies by focusing on the essential tuples, which are the minimal set of tuples necessary to represent the data accurately.
Key characteristics of ETNF include:
The goal of achieving ETNF in a database design is to create a robust, efficient, and reliable database schema that supports accurate data representation and manipulation.
Denormalization is a strategy used on a previously-normalized database to increase performance. In computing, denormalization is the process of trying to improve the read performance of a database, at the expense of losing some write performance, by adding redundant copies of data or by grouping data. It is often motivated by performance or scalability in relational database software needing to carry out very large numbers of read operations. Denormalization differs from the unnormalized form in that denormalization benefits can only be fully realized on a data model that is otherwise normalized.
Database normalization is the process of structuring a relational database in 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.
A relational database (RDB) is a database based on the relational model of data, as proposed by E. F. Codd in 1970. A database management system used to maintain relational databases is a relational database management system (RDBMS). Many relational database systems are equipped with the option of using SQL for querying and updating the database.
The relational model (RM) is an approach to managing data using a structure and language consistent with first-order predicate logic, first described in 1969 by English computer scientist Edgar F. Codd, where all data is represented in terms of tuples, grouped into relations. A database organized in terms of the relational model is a relational database.
First normal form (1NF) is a property of a relation in a relational database. A relation is in first normal form if and only if no attribute domain has relations as elements. Or more informally, that no table column can have tables as values. Database normalization is the process of representing a database in terms of relations in standard normal forms, where first normal is a minimal requirement. SQL-92 does not support creating or using table-valued columns, which means that using only the "traditional relational database features" most relational databases will be in first normal form by necessity. Database systems which do not require first normal form are often called NoSQL systems. Newer SQL standards like SQL:1999 have started to allow so called non-atomic types, which include composite types. Even newer versions like SQL:2016 allow JSON.
Third normal form (3NF) is a database schema design approach for relational databases which uses normalizing principles to reduce the duplication of data, avoid data anomalies, ensure referential integrity, and simplify data management. It was defined in 1971 by Edgar F. Codd, an English computer scientist who invented the relational model for database management.
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 dependency known as a multivalued dependency. A table is in 4NF if and only if, for every one of its non-trivial multivalued dependencies XY, {X, Y} is a superkey—that is, the combination of all attributes in X and Y is either a candidate key or a superset thereof.
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 determineY iff each X value is associated with precisely one Y value. R is then said to satisfy the functional dependency X → Y. Equivalently, the projection is a function, that is, Y is a function of X. In simple words, if the values for the X attributes are known, then the values for the Y attributes corresponding to x can be determined by looking them up in any tuple of R containing x. Customarily X is called the determinant set and Y the dependent set. A functional dependency FD: X → Y is called trivial if Y is a subset of X.
Referential integrity is a property of data stating that all its references are valid. In the context of relational databases, it requires that if a value of one attribute (column) of a relation (table) references a value of another attribute, then the referenced value must exist.
Database design is the organization of data according to a database model. The designer determines what data must be stored and how the data elements interrelate. With this information, they can begin to fit the data to the database model. A database management system manages the data accordingly.
In the relational data model a superkey is any set of attributes that uniquely identifies each tuple of a relation. Because superkey values are unique, tuples with the same superkey value must also have the same non-key attribute values. That is, non-key attributes are functionally dependent on the superkey.
Object–relational impedance mismatch is a set of difficulties going between data in relational data stores and data in domain-driven object models. Relational Database Management Systems (RDBMS) is the standard method for storing data in a dedicated database, while object-oriented (OO) programming is the default method for business-centric design in programming languages. The problem lies in neither relational databases nor OO programming, but in the conceptual difficulty mapping between the two logic models. Both logical models are differently implementable using database servers, programming languages, design patterns, or other technologies. Issues range from application to enterprise scale, whenever stored relational data is used in domain-driven object models, and vice versa. Object-oriented data stores can trade this problem for other implementation difficulties.
Boyce–Codd normal form is a normal form used in database normalization. It is a slightly stricter version of the third normal form (3NF). By using BCNF, a database will remove all redundancies based on functional dependencies.
Fifth normal form (5NF), also known as projection–join normal form (PJ/NF), is a level of database normalization designed to remove redundancy in relational databases recording multi-valued facts by isolating semantically related multiple relationships. A table is said to be in the 5NF if and only if every non-trivial join dependency in that table is implied by the candidate keys. It is the final normal form as far as removing redundancy is concerned.
Sixth normal form (6NF) is a normal form used in relational database normalization which extends the relational algebra and generalizes relational operators to support interval data, which can be useful in temporal databases.
In database theory, a multivalued dependency is a full constraint between two sets of attributes in a relation.
Domain-key normal form is a normal form used in database normalization which requires that the database contains no constraints other than domain constraints and key constraints.
A database model is a type of data model that determines the logical structure of a database. It fundamentally determines in which manner data can be stored, organized and manipulated. The most popular example of a database model is the relational model, which uses a table-based format.
Anchor modeling is an agile database modeling technique suited for information that changes over time both in structure and content. It provides a graphical notation used for conceptual modeling similar to that of entity-relationship modeling, with extensions for working with temporal data. The modeling technique involves four modeling constructs: the anchor, attribute, tie and knot, each capturing different aspects of the domain being modeled. The resulting models can be translated to physical database designs using formalized rules. When such a translation is done the tables in the relational database will mostly be in the sixth normal form.
The following is provided as an overview of and topical guide to databases:
