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Entityâ€“relationship model - Wikipedia, the free encyclopedia. An entityâ€“relationship diagram using Chen's notation. An entityâ€“relationship model (ER model) describes inter- related things of interest in a specific domain of knowledge. An ER model is composed of entity types (which classify the things of interest) and specifies relationships that can exist between instances of those entity types. In software engineering an ER model is commonly formed to represent things that a business needs to remember in order to perform business processes. Consequently, the ER model becomes an abstract data model that defines a data or information structure that can be implemented in a database, typically a relational database. Entityâ€“relationship modeling was developed for database design by Peter Chen and published in a 1.
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However, variants of the idea existed previously, some ER modelers show super and subtype entities connected by generalization- specialization relationships, and an ER model can be used also in the specification of domain- specific ontology. IntroductionAn entityâ€“relationship model is usually the result of systematic analysis to define and describe what is important to processes in an area of a business. It does not define the business processes; it only presents a business data schema in graphical form.
It is usually drawn in a graphical form as boxes (entities) that are connected by lines (relationships) which express the associations and dependencies between entities. An ER model can also be expressed in a verbal form, for example: one building may be divided into zero or more apartments, but one apartment can only be located in one building. Entities may be characterized not only by relationships, but also by additional properties (attributes), which include identifiers called "primary keys". Diagrams created to represent attributes as well as entities and relationships may be called entity- attribute- relationship diagrams, rather than entity- relationship models. An ER model is typically implemented as a database.
In a simple relational database implementation, each row of a table represents one instance of an entity type, and each field in a table represents an attribute type. In a relational database a relationship between entities is implemented by storing the primary key of one entity as a pointer or "foreign key" in the table of another entity. There is a tradition for ER/data models to be built at two or three levels of abstraction. Note that the conceptual- logical- physical hierarchy below is used in other kinds of specification, and is different from the three schema approach to software engineering.
Conceptual data model. This is the highest level ER model in that it contains the least granular detail but establishes the overall scope of what is to be included within the model set.
The conceptual ER model normally defines master reference data entities that are commonly used by the organization. Developing an enterprise- wide conceptual ER model is useful to support documenting the data architecture for an organization. A conceptual ER model may be used as the foundation for one or more logical data models (see below).
The purpose of the conceptual ER model is then to establish structural metadata commonality for the master data entities between the set of logical ER models. The conceptual data model may be used to form commonality relationships between ER models as a basis for data model integration.
Logical data model. A logical ER model does not require a conceptual ER model, especially if the scope of the logical ER model includes only the development of a distinct information system.
The logical ER model contains more detail than the conceptual ER model. In addition to master data entities, operational and transactional data entities are now defined. The details of each data entity are developed and the relationships between these data entities are established. The logical ER model is however developed independent of technology into which it can be implemented. Physical data model. One or more physical ER models may be developed from each logical ER model. The physical ER model is normally developed to be instantiated as a database.
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Therefore, each physical ER model must contain enough detail to produce a database and each physical ER model is technology dependent since each database management system is somewhat different. The physical model is normally instantiated in the structural metadata of a database management system as relational database objects such as database tables, database indexes such as unique key indexes, and database constraints such as a foreign key constraint or a commonality constraint.
The ER model is also normally used to design modifications to the relational database objects and to maintain the structural metadata of the database. The first stage of information system design uses these models during the requirements analysis to describe information needs or the type of information that is to be stored in a database. The data modeling technique can be used to describe any ontology (i. In the case of the design of an information system that is based on a database, the conceptual data model is, at a later stage (usually called logical design), mapped to a logical data model, such as the relational model; this in turn is mapped to a physical model during physical design. Note that sometimes, both of these phases are referred to as "physical design."Entityâ€“relationship modelingTwo related entities.
An entity with an attribute. A relationship with an attribute.
An entity may be defined as a thing capable of an independent existence that can be uniquely identified. An entity is an abstraction from the complexities of a domain. When we speak of an entity, we normally speak of some aspect of the real world that can be distinguished from other aspects of the real world.An entity is a thing that exists either physically or logically.
An entity may be a physical object such as a house or a car (they exist physically), an event such as a house sale or a car service, or a concept such as a customer transaction or order (they exist logicallyâ€”as a concept). Although the term entity is the one most commonly used, following Chen we should really distinguish between an entity and an entity- type.
An entity- type is a category. An entity, strictly speaking, is an instance of a given entity- type. There are usually many instances of an entity- type. Because the term entity- type is somewhat cumbersome, most people tend to use the term entity as a synonym for this term. Entities can be thought of as nouns. Examples: a computer, an employee, a song, a mathematical theorem.
A relationship captures how entities are related to one another. Relationships can be thought of as verbs, linking two or more nouns. Examples: an owns relationship between a company and a computer, a supervises relationship between an employee and a department, a performs relationship between an artist and a song, a proves relationship between a mathematician and a conjecture. The model's linguistic aspect described above is utilized in the declarative database query language ERROL, which mimics natural language constructs.
ERROL's semantics and implementation are based on reshaped relational algebra (RRA), a relational algebra that is adapted to the entityâ€“relationship model and captures its linguistic aspect. Entities and relationships can both have attributes. Examples: an employee entity might have a Social Security Number (SSN) attribute; the proved relationship may have a date attribute. Every entity (unless it is a weak entity) must have a minimal set of uniquely identifying attributes, which is called the entity's primary key.
Entityâ€“relationship diagrams don't show single entities or single instances of relations. Rather, they show entity sets(all entities of the same entity type) and relationship sets(all relationships of the same relationship type). Example: a particular song is an entity. The collection of all songs in a database is an entity set.
The eaten relationship between a child and her lunch is a single relationship. The set of all such child- lunch relationships in a database is a relationship set. In other words, a relationship set corresponds to a relation in mathematics, while a relationship corresponds to a member of the relation.
Certain cardinality constraints on relationship sets may be indicated as well. Mapping natural languageChen proposed the following "rules of thumb" for mapping natural language descriptions into ER diagrams: "English, Chinese and ER diagrams" by Peter Chen. Physical view show how data is actually stored. Relationships, roles and cardinalitiesIn Chen's original paper he gives an example of a relationship and its roles.
He describes a relationship "marriage" and its two roles "husband" and "wife". A person plays the role of husband in a marriage (relationship) and another person plays the role of wife in the (same) marriage.
These words are nouns. That is no surprise; naming things requires a noun. Chen's terminology has also been applied to earlier ideas. The lines, arrows and crow's- feet of some diagrams owes more to the earlier Bachman diagrams than to Chen's relationship diamonds. Another common extension to Chen's model is to "name" relationships and roles as verbs or phrases. Role namingIt has also become prevalent to name roles with phrases such as is the owner of and is owned by. Correct nouns in this case are owner and possession.
Thus person plays the role of owner and car plays the role of possession rather than person plays the role of, is the owner of, etc. The use of nouns has direct benefit when generating physical implementations from semantic models.