Introduction to Data Models in DBMS
The Database models in the Database Management System explains the logic behind the structure of a Database system that should usually include all the tables, which are represented as entities in ER model, the relationships between the tables and objects, and the requirement provided by the project team in order to settle on how data can be stored & accessed, granted the aimed Database System needs to be designed with respect to the rules and notions of the given data model the Database Architect prefers to be implement.
Different types of Data Models in DBMS
The different types of data models in DBMS that are used are as given below:
- Flat Data Model
- Entity-Relationship Model
- Relation Model
- Record base Model
- Network Model
- Hierarchical Model
- Object-oriented Data Model
- Object Relation Model
- Semi-structured Model
- Associative Model
- Context Data Model
Below are the detailed description of the above database models
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Flat Data Model:
Flat data model is the first introduced traditional data model where data is kept in the same plane. This is a very old model which is not much scientific.
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Entity Relationship Data Model:
The Entity relationship data model structure based on the impression of the real world entities and the existing relationship between them. In the process of designing the real world scenario into the database model the Entity sets are created in the beginning and then the model is dependent on the two below vital things which are entities consisting of the attributes and the relationship that exists among the entities. An entity contains a real-world property called attribute. Attributes are defined by a set of values known as domains. For example, in an office the employee is an entity, the office is the database, employee ID, name are the attributes. The logical association between the different entities are known as the relationship among them.
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Relational Data Model:
The most popular and extensively used data model is the relational data model. The data model allows the data to be stored in tables called relation. The relations are normalized and the normalized relation values are known as atomic values. Each of the rows in a relation is called tuples which contains the unique value. The attributes are the values in each of the columns which are of the same domain.
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Network Data Model:
In the network data model, all the entities are organized in graphical representations. There may be several parts in the graph in which the entities can be accessed.
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Hierarchical Data Model:
The hierarchical model is based on the parent-child hierarchical relationship. In this model, there is one parent entity with several children entity. At the top, there should be only one entity which is called root. For example, an organization is the parent entity called root and it has several children entities like clerk, officer and many more.
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Object-oriented Data Model:
An object-oriented data model is one of the most developed data models which contains video, graphical files, and audio. This consists of the data piece and the methods in the form of database management system instructions.
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Record base Data Model:
The record-based data model is used to determine the overall design of the database. This data model contains different kinds of record types. Each of the record types has a fixed length and a fixed number of fields.
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Object-relational Data Model:
The object-relational data model is a powerful data model but for the design of the object-relational data, the model is very complex. This model gives efficient results and widespread with huge application thus some part of the complexity problem can be ignored because of this. It also offers features like working with other data models. Using the object-relational data model we can work with the relational model also.
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Semi-structured Data Model:
The semi-structured data model is a self-describing data model. The data stored in this model is generally associated with a scheme which is contained within the data property known as self-describing property.
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Associative Data Model:
Associative data model follows the principle of division which data in two ways between entities and association. Hence, the model is dividing the data for all the real world scenarios into entities and associations.
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Context Data Model:
Context data models are very flexible as it contains a collection of several data models. It is a collection of data models like the relational model, network model, semi-structured model, object-oriented model. Thus, because of the versatile design of this database model different types of tasks can be accomplished. As a result, support for different types of users is added which may differ by the interaction of the users in the database. The context data model brought a revolutionary change in the industries by properly handling relevant data. The main function of the data models in a database management system is helping the users to use and create databases. There are several types of data models depending on the kind of structure the users need and based on that we can select the data models in the database management system.
Conclusion – Data Models in DBMS
Data modeling is the method of developing the data model for the data to be stored in the database. This ensures consistent naming convention and different other security features to maintain the quality of the data. Because of data modeling, proper structure is defined for tables and different primary and foreign keys as well as stored procedures in the database. There are three main models of data modelling like conceptual, logical, and physical. A conceptual model is used to establish the entities, attributes, and relationships. A logical data model is to define the structure of the data elements and set the relationship between them. Finally, the physical model is used to specify the database-centric implementation of the model. The main motive of designing the data model is to ensure that the objects given by the functional team are represented properly and accurately. The main disadvantage of database modeling is that a minimum modification in the structure may result in the change in the entire application.
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