extended entity relationship model (EERM)
The Extended Entity Relationship Model (EERM) is an extension of the traditional Entity Relationship Model (ERM) used in database design
The Extended Entity Relationship Model (EERM) is an extension of the traditional Entity Relationship Model (ERM) used in database design. It enhances the modeling capabilities of the ERM by introducing additional concepts and relationships, allowing for a more comprehensive representation of complex data structures and business requirements.
EERM introduces three main extensions to the basic ERM: generalization and specialization, aggregation, and attributes.
1. Generalization and Specialization: With generalization, you can model a hierarchy of entities in which lower-level entities inherit properties and relationships from higher-level entities. This is useful when you have entities with common attributes and relationships, but also with unique properties. For example, in a university database, you could have a general “Person” entity that can be specialized into “Student,” “Faculty,” and “Staff” entities.
2. Aggregation: Aggregation allows you to represent relationships between entities without fully merging them. It is used when an entity represents a whole and can be made up of smaller entities. For example, in a car rental system, you can aggregate the “Car” entity with the “Rental” entity to represent the fact that a rental consists of a specific car.
3. Attributes: EERM allows for various types of attributes, including composite, derived, and multi-valued attributes. Composite attributes are made up of multiple simpler attributes, such as storing an address as separate attributes for street, city, and postal code. Derived attributes are calculated based on other attributes, like calculating the age from a birth date attribute. Multi-valued attributes can store multiple values for a single attribute, such as storing multiple phone numbers for a contact.
By incorporating these extensions, EERM provides a richer and more flexible modeling approach. It helps in capturing a greater level of complexity and detail in the data requirements, allowing for more accurate and comprehensive database designs.
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