Data Models in DBMS

Data Model

A Database model defines the logical design of data.Data models are fundamentals entities to define how data is connected to each other and how data is processed and stored inside the system.

Data Models can be categorized as follows:-
1)Hierarchical Model
2)Network Model
3)Relation Model
4)E-R Model
5)Object-Based Model
6)Semi-structured/Hybrid Model

Hierarchical Model - In this model,the data is organized in the form of tree like structure.
In other words ,we can say that it provides parent-child relationship where child is inherited with some of the properties of parent.Parent child relationship is one to many relationship.

Advantage-
1)Simplicity
2)Data security&Data integrity

Disadvantage-
1)Implementation complexity
2)Programming complexity

Network Data Model - The data is organized in the form of record which are linked to each other.
The advantage of this model is that it has less duplication as compare to hierarchical model but it is more complex to hierarchical model.
It permitted the modelling of many to many relationship in data. 


Relational Model - The data is organized in the form of tables.Each table have a set of attributes.

Properties of relational table:-
1)Values are atomic.
2)Each row is unique.
3)Column values are of the same kind.
4)Each column has a unique name.


E-R Model - It comprises of entity and relationship,so it is known as entity -relationship model.

Entity can be defined as a thing or an object in a real word.In otherwords,we can say entity is a set of attributes.It is represented as rectangle.

Relationship can be defined as a relation between two or more entities.It is represented as diamond .


Here ,
Entities are Student and Class.
where as
Relationship between them is Enroll(Enrollment).
Entities are represented with set of certain attributes.

Object Based Data Model - Object database management systems (ODBMS) add new object storage capabilities to the relational systems at the core of modern information systems. Thee new facilities such as integrate management of traditional fielded data, complex objects such as time-series and geospatial data and diverse binary media such as audio, video, images, and applets. By encapsulating methods with data structures, an ODBMS server can execute compile x analytical and data manipulation operations to search and transform multimedia and other complex objects.

Semi-Structured Data Model - In semistructured data model, the information that is normally associated with a schema is contained within the data, which is sometimes called ``self-describing''. In such database there is no clear separation between the data and the schema, and the degree to which it is structured depends on the application. Semistructured data has recently emerged as an important topic of study for a variety of reasons. First, there are data sources such as the Web, which we would like to treat as databases but which cannot be constrained by a schema. Second, it may be desirable to have an extremely flexible format for data exchange between disparate databases. Third, even when dealing with structured data, it may be helpful to view it as semistructured for the purposes of browsing.