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The foreign key is typically a primary key of an entity it is related to. The foreign key is an attribute of the identifying (or owner, parent, or dominant) entity set. 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 ...
an arrow from an entity set to a relationship set indicates a key constraint, i.e. injectivity: each entity of the entity set can participate in at most one relationship in the relationship set; a thick line indicates both, i.e. bijectivity: each entity in the entity set is involved in exactly one relationship.
A stereotype is one of three types of extensibility mechanisms in the Unified Modeling Language (UML), the other two being tags and constraints. [1]: 73 They allow designers to extend the vocabulary of UML in order to create new model elements, derived from existing ones, but that have specific properties that are suitable for a particular domain or otherwise specialized usage.
Thus, the "relation" in "relational database" refers to the various tables in the database; a relation is a set of tuples. The columns enumerate the various attributes of the entity (the employee's name, address or phone number, for example), and a row is an actual instance of the entity (a specific employee) that is represented by the relation.
An associative entity is a term used in relational and entity–relationship theory. A relational database requires the implementation of a base relation (or base table) to resolve many-to-many relationships. A base relation representing this kind of entity is called, informally, an associative table. An associative entity (using Chen notation)
In database design, object-oriented programming and design, has-a (has_a or has a) is a composition relationship where one object (often called the constituted object, or part/constituent/member object) "belongs to" (is part or member of) another object (called the composite type), and behaves according to the rules of ownership.
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A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]