Data Models

Semantic Object Model Databases Management System

 Semantic Object Model Databases Management System Semantic Object Model demonstrate is an intelligent portrayal data in an association. It sees the whole system as a gathering of objects identified with each other. Semantic Object Model demonstrate takes a semantic object as the fundamental component initially exhibited in 1988. The semantic Object Model can speak to the view of the client more intently than E-R show.


Semantic Object Model


Semantic Objects:

A semantic Object Model is a thing that can be recognized in the client’s workplace. Semantic signifies “meaning”. Semantic objects are utilized to speak to or show the importance of client’s data. It depends on the ideas presented. For example, distinctive objects in a college are understudies, educators, and offices and so forth.

Semantic Object Model are assembled into classes. Each object class has a special name. For example, unique classes in a college are STUDENT, TEACHER, and DEPARTMENT and so on. A specific semantic object is called a case of the class. For example, Usman is an occasion of STUDENT class, Abdullah is a case of TEACHER class and “Software engineering” is an occurrence of DEPARTMENT class.

The Object Model, however, ORDER isn’t a physical object. It is essentially a composed archive. At the point when objects in a 4.2.1 Attributes Some objects exist physically however some don’t. For example, a STUDENT is a physical workplace are characterized, all physical and non-physical objects must be distinguished.


An Object Model has numerous traits. Each quality speaks to a normal for the object. place an object of STUDENT class can have traits like Name, Address, Email, and e and so forth. These credits are utilized to portray the object. When we characterize objects for a database system, we utilize just those characteristics, which are imperative for the system. The typically not utilized as a part of a school Database since it isn’t imperative.

Distinctive kinds of characteristics are as per the following:

Basic Attributes:

  • Basic characteristics are the properties that contain a solitary esteem. Roll No, Marks and Salary are examples of basic.

Gathering Attributes:

  • Gathering qualities are composites of different traits. For example, Name is an accumulation of First Name and Last Name, Address is a gathering of Street, Town, City, and Country.
  • Gathering Attributes are overlooked. For example, weight is a property of an understudy however it is

Semantic Object Attributes:

  • Semantic object qualities are the traits the setup connections between one semantic object to another semantic object.


Following is a basic example that clarifies the utilization of semantic object outline. The objects in this graph are demonstrated by rectangles. The name of the object shows up on the highest point of the rectangle and the characteristics are composed after the name of the object in the rectangle.

The COLLEGE object graph contains diverse qualities. Teacher, Student, and Library are semantic object characteristics. The semantic object properties demonstrate that COLLEGE object is sensibly associated with different objects. It implies that Professor, Student, and Library are additionally some other semantic objects.

Property Cardinality:

Each semantic object contains two kinds of cardinalities:

Least Cardinality: The most extreme cardinality demonstrates the greatest number of occurrences of the trait that must exist.

Least Cardinality: The most extreme cardinality demonstrates the greatest number of occurrences of the trait that may exist.

Each trait has both least and most extreme cardinality. The base cardinality is generally 0 or 1. The esteem 0 demonstrates that the trait isn’t necessary. In the event that it is 1, the esteem. At times, least cardinality is more than 1. For example, PLAYERS quality of CRICKET-TEAM object ought to have a base cardinality 11 as the trait must have a cricket group can’t have under 11 players.

The greatest cardinality is typically 1 or N. On the off chance that it is 1, the property can’t have in excess of 1 case. On the off chance that it is N, the property can have any number of occasions. In some cases, RS trait of CRICKET-TEAM object ought to have a most extreme cardinality 11 as the cricket group can’t greatest cardinality is given as a correct esteem. For example, PLATE has in excess of 11 players.

Cardinalities are determined in n.m to organize where n shows the base cardinality and m demonstrates the most extreme cardinality. In the above chart, Name property of cardinality. It implies that COLLEGE object must have precisely one name. Telephone qualities have least cardinality1 and greatest cardinality N. It implies that COLLEGE must have no less than one telephone, however, the auto object has a base cardinality 1 and most extreme c have numerous telephones.

Object Class and Object Instance:

An object class is a general configuration for all object occasions of that specific sort. The past figure demonstrates the structure of a school and can be utilized for any school. When we speak to an object example, the figure will contain the estimations of all qualities. Following is an example of a COLLEGE object occasion.

Object Identifiers:

An object identifier is a property or accumulation of ascribes that are utilized to distinguish an object case. For example, conceivable identifiers of STUDENT are Roll No or Name. Trouble properties are not identifiers since a few credits can’t be utilized to recognize a standard object instance. For example, Marks of an understudy isn’t an identifier since you can’t distinguish an understudy by utilizing marks.

A gathering identifier is a kind of identifier that has in excess of one quality. For example, you can distinguish an understudy by utilizing an identifier that comprises First Name and Second Name.

Object identifier can be novel or non-exceptional. For example, Roll No of an understudy is an interesting identifier however Name isn’t special. There may perhaps be two understudies with a name “Usman”. In this circumstance, “Usman” recognizes a gathering of understudies and one understudy will be distinguished by another characteristic like Roll No and so on.

In the semantic chart, object identifiers are demonstrated by the letters “ID” before the characteristic. On the off chance that the identifier is extraordinary, “ID” is underlined.

Characteristic Domains:

A gathering of conceivable estimations of a quality is known as characteristic area. The area of a basic quality comprises of two sorts of depictions.

  • Physical Description
  • Semantic Description

The physical portrayal demonstrates the kind of data. For example, Name of an understudy ought to be a string quality and Roll No ought not to surpass 99 and so forth.

The semantic portrayal shows the capacity or motivation behind the characteristic. For example, the Name of an understudy ought to be a substantial name. This is a semantic depiction of Name quality of the understudy. This limitation is identified with the importance of the trait. For example, “Math” isn’t a legitimate name regardless of whether it comprises of string esteem since it doesn’t fulfill the semantic portrayal of the Name property.

Some of the time, the physical portrayal is indicated in a predefined rundown of qualities. For example, Class quality of an understudy might be indicated as “MCS”, “BCS”, “MBA”, “MS”.

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