The document provides an overview of various data models used in database management systems, including relational, hierarchical, network, entity-relationship, object-oriented, and object-relational models. It discusses the definitions, advantages, and disadvantages of each model, along with their structural characteristics and usage scenarios. The importance of understanding these models for effective data organization and management in databases is emphasized.

1. Data Model
Database System
08 November 2024
Shams Ullah
Lecturer in Computer
Science
GPGC Miran Shah
Week III

2. Contents
Definition
Importance
Types
Conclusion
2
3
4
22
23

3. Definition
3
A data model in a database management system
(DBMS) is a way to organize, document, and define data
in a database. It shows how data is connected, stored,
accessed, and changed
The term “data model” refers to the way of
representing the logical structure of a database

4. Relational Model
Hierarchical
Model
Network Model Entity-
Relationship
Model
Object-Oriented
Data Model
Object-
Relational Data
Model
Types
4

5. Hierarchical Data Model
5
A hierarchical data model is a type of database model
that organizes data into a tree-like structure, where
data elements are linked through parent-child
relationships
One of the most influential hierarchical database
systems is IBM's IMS (Information Management
System), introduced in the late 1960s

6. Advantages
6
Disadvantage
s
• Simple Structure
• Fast Data Retrieval
• Efficient Querying
• Data Integrity
• Controlled Access and
Security
• Rigid Structure
• Insertion and Deletion
Constraints
• Complex Queries for Non-
Hierarchical Data
• Difficult Data Modification
• Complex Management and
Maintenance

7. Network Model
7
This looks like a Hierarchical database model due to
which many time it is called as modified version of
Hierarchical database. Network database model
organize data more like a graph and can have more
than one parent node.
This model was formalized by the Database Task group
in the 1960s. This model is the generalization of the
hierarchical model

8. Advantages
8
Disadvantages
• Simple and easy to design
• Can handle multiple types of
relationships
• Easy access
• Data Integrity
• Controlled Access and Security
• Complex in nature
• Existence of operational
anomalies
• Not user-friendly
• No query optimization
• Can’t achieve structural
independence even though it
achieves data independence.

9. Feature Hierarchical Model Network Model
Structure Tree-like structure Graph structure
Relationships One-to-many (single parent, multiple children) Many-to-many (multiple parents and children)
Flexibility Less flexible More flexible
Data Access Single access path Multiple access paths
Redundancy Higher redundancy due to rigid hierarchy Lower redundancy due to shared relationships
Complexity Simpler to design and implement More complex to design and manage
Usage Scenario Suitable for simple, hierarchical data structures Suitable for complex, interconnected data structures
Efficiency Efficient for hierarchical traversal Efficient for complex queries and data retrieval
Example Organizational chart Telecommunications network
9

10. ER Data Model
10
The Entity Relational Model is a model for
identifying entities to be represented in the database
and representation of how those entities are related
Peter Chen developed the ER diagram in 1976. The ER
model was created to provide a simple and
understandable model for representing the structure
and logic of databases. It has since evolved into
variations such as the Enhanced ER Model

11. Advantages
11
Disadvantages
• Simple
• Effective
• Easy to understand
• Integrated
• Useful in decision making
• Easy conversion
• Database troubleshooting
• Flexible
• Minimize data redundancy
• Scalability
• Visual representation
• Documentation
• Loss of information
• Limited relationship
• No preservation for data manipulation
• No industry standard
• Data inconsistency
• Missing cardinalities
• Difficult to modify
• Limited attribute representation
• Lack of support for business rules
• Difficult to scale
• Dependence on user understanding
• Limited support for abstraction

12. 12

13. Relational Data Model
13
The relational model represents data in the form of
relation/table.
E.F. Codd proposed the relational Model to model
data in the form of relations or tables. After
designing the conceptual model of the Database
using ER diagram, we need to convert the
conceptual model into a relational model which can
be implemented using any RDBMS language like
Oracle SQL, MySQL, etc.

14. Advantages
14
Disadvantages
• Simple model
• Flexible
• Secure
• Data Accuracy
• Data Integrity
• Operations can be Applied Easily
• Relational Database Model is not
very good for large databases.
• Sometimes, it becomes difficult to
find the relation between tables.
• Because of the complex structure,
the response time for queries is
high.

15. 15

16. Object Oriented Data
Model
16
In Object Oriented Data Model, data and their
relationships are contained in a single
structure which is referred as object in this data
model. In this, real world problems are
represented as objects with different
attributes. All objects have multiple
relationships between them.
To represent the complex real world problems
there was a need for a data model that is
closely related to real world. Object Oriented
Data Model represents the real world problems
easily.

17. Advantages
17
Disadvantages
• Codes can be reused due to
inheritance.
• Easily understandable.
• Cost of maintenance can reduced
due to reusability of attributes
and functions because of
inheritance.
• It is not properly developed so not
accepted by users easily.

18. 18
Object Oriented Data
Model Terminologies

19. Object Relational Data
Model
19
As the name suggests, the Object-Relational Data Model
merges the relational and Object-Oriented models. This
model was designed to bridge the gap that existed between
the two paradigms. It offers numerous capabilities like
creating complex data types based on our needs using
existing data types. However, the complexity of this model
can make it challenging to handle, underlining the
importance of a thorough understanding of the model.
One of the primary objectives of the Object-Relational data
model is to smoothen the integration of Object-Oriented
practices with the Relational databases predominantly used
in programming languages such as C++, C#, and Java.

20. Advantages
20
Disadvantage
s
• The Power of Inheritance
• Handling Complex Data Types
• The Scope for Extensibility
• The complexity of this data model,
resulting from its integration of
the features of both the Object-
Oriented and the Relational data
models, can sometimes make it
quite challenging to manage

21. OODBMS ORDBMS
It stands for Object Oriented Database
Management System.
It stands for Object Relational Database
Management System.
Object-oriented databases, like Object Oriented
Programming, represent data in the form of
objects and classes.
An object-relational database is one that is based
on both the relational and object-oriented
database models.
OODBMSs support ODL/OQL.
ORDBMS adds object-oriented functionalities to
SQL.
Every object-oriented system has a different set of
constraints that it can accommodate.
Keys, entity integrity, and referential integrity are
constraints of an object-oriented database.
The efficiency of query processing is low. Processing of queries is quite effective.
21
Difference Between OODBMS and
ORDBMS

22. • Accurate data representation
• Relationship between database
objects
• Control of data redundancy
• Security
• Security
• Faster software builds
• Reduced cost
• Improved performance
• Better documentation
• High-quality applications
• Data analytics and visualization
Purpose/Importance
22

23. The End
Database Systems
2024 November 08
Shams Wazir
Week III