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"Don't repeat yourself" (DRY), also known as "duplication is evil", is a principle of software development aimed at reducing repetition of information which is likely to change, replacing it with abstractions that are less likely to change, or using data normalization which avoids redundancy in the first place.
code which is executed but has no external effect (e.g., does not change the output produced by a program; known as dead code). A NOP instruction might be considered to be redundant code that has been explicitly inserted to pad out the instruction stream or introduce a time delay, for example to create a timing loop by "wasting time".
[1] [2] Data redundancy can also be used as a measure against silent data corruption; for example, file systems such as Btrfs and ZFS use data and metadata checksumming in combination with copies of stored data to detect silent data corruption and repair its effects. [3]
A cyclic redundancy check (CRC) is an error-detecting code commonly used in digital networks and storage devices to detect accidental changes to digital data. [ 1 ] [ 2 ] Blocks of data entering these systems get a short check value attached, based on the remainder of a polynomial division of their contents.
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 .
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.
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]
The code-rate is hence a real number. A low code-rate close to zero implies a strong code that uses many redundant bits to achieve a good performance, while a large code-rate close to 1 implies a weak code. The redundant bits that protect the information have to be transferred using the same communication resources that they are trying to protect.