Data Modeling and Data Models and its Importance

Module I – Session 4 -Data
Models
Presented by
Dr.S.Zulaikha Beevi,
Professor/CSE.

Data modeling: Iterative and progressive
process of creating a specific data model for a
determined problem domain
Data models: Simple representations of
complex real-world data structures;Useful for
supporting a specific problem domain
Model - Abstraction of a real-world object or
event
Data Modeling and Data
Models

✔Are a communication tool
✔Give an overall view of the database
✔Organize data for various users
✔Are an abstraction for the creation of
good database
Importance of Data
Models

Data Model Basic Building Blocks
✔Entity: Unique and distinct object used to
collect and store data
✔ Attribute: Characteristic of an entity
✔Relationship: Describes an association
among entities
✔ One-to-many (1:M)
✔ Many-to-many (M:N or M:M)
✔ One-to-one (1:1)
✔Constraint: Set of rules to ensure data
integrity

Data
Models
A Database model defines the logical design and
structure of a database and defines how data will be
stored, accessed and updated in a database
management system.
✔ Hierarchical Model
✔ Network Model
✔ Relational Model
✔ Entity-relationship Model
✔ Object Data Model

Hierarchical
Model
✔ This database model organises data into a tree-like-
structure, with a single root, to which all the other data is
linked. The hierarchy starts from the Root data, and
expands like a tree, adding child nodes to the parent
nodes.
✔ In this model, a child node will only have a single parent
node.
✔ This model efficiently describes many real-world
relationships like index of a book, recipes etc.
✔ In hierarchical model, data is organised into tree-like
structure with one one-to-many relationship between two
different types of data, for example, one department can
have many courses, many professors and of-course many
students.

Network
Model
✔ This is an extension of the Hierarchical model. In this
model data is organised more like a graph, and are
allowed to have more than one parent node.
✔ In this database model data is more related as more
relationships are established in this database model.
Also, as the data is more related, hence accessing the
data is also easier and fast. This database model was
used to map many-to-many data relationships.
✔ This was the most widely used database model,
before Relational Model was introduced.

Hierarchical and Network
Models
Hierarchical Models Network Models
Manage large amounts of
data for complex
manufacturing projects
Represent complex
data relationships
Represented by an
upside down tree which
contains segments
Segments: Equivalent of a
file system’s record type
Improve database
performance and impose a
database standard
Depicts a set of one-to-
many (1:M) relationships
Depicts both one-to-many
(1:M) and many-to-many
(M:N) relationships

Hierarchical
Model
Advantages Disadvantages
Promotes data sharing Requires knowledge of
physical data storage
characteristics
Parent/child relationship
promotes conceptual
simplicity and data integrity
Navigational system
requires knowledge of
hierarchical path
Database security is
provided and enforced by
DBMS
Changes in structure
require changes in all
application programs
Efficient with 1:M relationships Implementation limitations
No data definition
Lack of standards

Network
Mode
Conceptual simplicity System complexity limits efficiency
Handles more relationship types Navigational system yields
complex implementation,
application development, and
management
Data access is flexible Structural changes require changes
in all application programs
Data owner/member
relationship promotes data
integrity
Conformance to standards
Includes data definition language
(DDL) and data manipulation
language (DML)

Relational
Model
In this model, data is organised in two-dimensional
tables and the relationship is maintained by storing
a common field.
This model was introduced by E.F Codd in 1970, and
since then it has been the most widely used
database model, infact, we can say the only
database model used around the world.
The basic structure of data in the relational model is
tables. All the information related to a particular type
is stored in rows of that table.
Hence, tables are also known as relations in relational
model.

The Relational
Model
Structural independence is
promoted using
independent tables
Requires substantial
hardware and system
software overhead
Tabular view
improves conceptual
simplicity
Conceptual simplicity
gives untrained people
the tools to use a good
system poorly
Ad hoc query capability
is based on SQL
May promote
information problems
Isolates the end user
from physical-level
details
Improves implementation

Entity-Relationship
Model
Entity-Relationship (ER) Model is based on the notion of
real-world entities and relationships among them.
While formulating real-world scenario into the
database model, the ER Model creates entity set,
relationship set, general attributes and constraints.
ER Model is best used for the conceptual design
of a database.
ER Model is based on −
Entities and their attributes.
Relationships among
entities.

Entity − An entity in an ER Model is a real-world entity
having properties called attributes. Every attribute is
defined by its set of values called domain. For example,
in a University database, a student is considered as an
entity.
Student has various attributes like name, age, class, etc.
✔
✔
✔
✔
Relationship − The logical association among entities is
called relationship. Relationships are mapped with
entities in various ways. Mapping cardinalities define
the number of association between two entities.
Mapping cardinalities −
one to one
one to many
many to one
many to
many

Entity Relationship
Model
Advantage Disadvantages
Visual modeling
yields conceptual
simplicity
Limited
constraint
representation
Visual representation makes
it an effective
communication tool
Limited
relationship
representation
Is integrated with the
dominant relational model
No data manipulation
language
Loss of information content
occurs when attributes are
removed from entities to
avoid crowded displays

The Object-Oriented Data Model
(OODM) or Semantic Data Model
✔ Object-oriented database management
system(OODBMS)
✔ Based on OODM
✔ Object: Contains data and their
relationships with operations that are
performed on it
✔ Basic building block for autonomous
structures
✔ Abstraction of real-world entity
✔ Attributes - Describe the properties of an
object

The Object-Oriented Data Model
(OODM)
✔ Class: Collection of similar objects with shared
structure and behavior organized in a class
hierarchy
✔ Class hierarchy: Resembles an upside-down tree in
which each class has only one parent
✔ Inheritance: Object inherits methods and
attributes of parent class
✔ Unified Modeling Language (UML)
✔ Describes sets of diagrams and symbols to
graphically model a system

Object-Oriented
Model
Semantic content is added Slow development of
standards caused vendors to
supply their own
enhancements
Visual representation
includes semantic content
Compromised widely
accepted standard
Inheritance promotes
data integrity
Complex navigational system
Learning curve is steep
High system overhead
slows transactions

A Comparison of OO, UML, and
ER Models

- Data Model Basic
Terminology Comparison

Data Modeling and Data Models and its Importance

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