Categories of Data Models

Many data models have been proposed, which we can categorize according to the types of concepts they use to describe the database structure. High-level or conceptual data models provide concepts that are close to the way many users perceive data, whereas low-level or physical data models provide concepts that describe the details of how data is stored on the computer storage media, typically magnetic disks. Concepts provided by physical data models are generally meant for computer specialists, not for end users. Between these two extremes is a class of representational (or implementation) data models, which provide concepts that may be easily understood by end users but that are not too far removed from the way data is organized in computer storage. Representational data models hide many details of data storage on disk but can be implemented on a computer system directly

Conceptual data models use concepts such as entities, attributes, and relationships. An entity represents a real-world object or concept, such as an employee or a project from the miniworld that is described in the database. An attribute represents some property of interest that further describes an entity, such as the employee’s name or salary. A relationship among two or more entities represents an association among the entities, for example, a works-on relationship between an employee and a project

Representational or implementation data models are the models used most frequently in traditional commercial DBMSs. These include the widely used relational data model, as well as the so-called legacy data models the network and hierarchical models

Physical data models describe how data is stored as files in the computer by representing information such as record formats, record orderings, and access paths. An access path is a search structure that makes the search for particular database records efficient, such as indexing or hashing

Another class of data models is known as self-describing data models. The data storage in systems based on these models combines the description of the data with the data values themselves

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