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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.
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. One owner could have many cars, one-to-many. In a relational database, a one-to-many relationship exists when one record is related to many records of another table. A one-to ...
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.
High-cardinality refers to columns with values that are very uncommon or unique. High-cardinality column values are typically identification numbers, email addresses, or user names. An example of a data table column with high-cardinality would be a USERS table with a column named USER_ID. This column would contain unique values of 1-n. Each ...
Associative tables are colloquially known under many names, including association table, bridge table, cross-reference table, crosswalk, intermediary table, intersection table, join table, junction table, link table, linking table, many-to-many resolver, map table, mapping table, pairing table, pivot table (as used in Laravel—not to be ...
Each element in the weak entity set must have a relationship with exactly one element in the owner entity set, [1] and therefore, the relationship cannot be a many-to-many relationship. Two entities can be associated without either being classified as weak, even if one depends on the other, as long as each has its own unique attribute. [1]
In computer science, the count-distinct problem [1] (also known in applied mathematics as the cardinality estimation problem) is the problem of finding the number of distinct elements in a data stream with repeated elements. This is a well-known problem with numerous applications.
The picture shows an example f and the corresponding T; red: n∈f(n)\T, blue:n∈T\f(n). While the cardinality of a finite set is simply comparable to its number of elements, extending the notion to infinite sets usually starts with defining the notion of comparison of arbitrary sets (some of which are possibly infinite).