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In SQL (Structured Query Language), the term cardinality refers to the uniqueness of data values contained in a particular column (attribute) of a database table. The lower the cardinality, the more duplicated elements in a column. Thus, a column with the lowest possible cardinality would have the same value for every row.
Within data modelling, cardinality is the numerical relationship between rows of one table and rows in another. Common cardinalities include one-to-one , one-to-many , and many-to-many . Cardinality can be used to define data models as well as analyze entities within datasets.
In systems analysis, a one-to-many relationship is a type of cardinality that refers to the relationship between two entities (see also entity–relationship model). For example, take a car and an owner of the car. The car can only be owned by one owner at a time or not owned at all, and an owner could own zero, one, or multiple cars.
For example, think of A as Authors, and B as Books. An Author can write several Books, and a Book can be written by several Authors. In a relational database management system, such relationships are usually implemented by means of an associative table (also known as join table, junction table or cross-reference table), say, AB with two one-to-many relationships A → AB and B → AB.
A country has only one capital city, and a capital city is the capital of only one country. (Not valid for some countries).. In systems analysis, a one-to-one relationship is a type of cardinality that refers to the relationship between two entities (see also entity–relationship model) A and B in which one element of A may only be linked to one element of B, and vice versa.
Illustration of set type using a Bachman diagram. A Bachman diagram is a certain type of data structure diagram, [2] and is used to design the data with a network or relational "logical" model, separating the data model from the way the data is stored in the system.
Cardinality bounds can be used in the body of a rule as well, for instance: :- 2 { p , q , r }. Adding this constraint to an Lparse program eliminates the stable models that contain at least 2 of the atoms p , q , r {\displaystyle p,q,r} .
The HyperLogLog has three main operations: add to add a new element to the set, count to obtain the cardinality of the set and merge to obtain the union of two sets. Some derived operations can be computed using the inclusion–exclusion principle like the cardinality of the intersection or the cardinality of the difference between two HyperLogLogs combining the merge and count operations.