Database System and Data Models - DBMS - MU

UNIT 1
Database System and Data Models
Database Management System
(09CE1302)
Department of Computer Engineering
Faculty of Diploma Studies
- Surabhi Palkar (Assistant Professor) 1

What is Data?
Prof. Surabhi Palkar
Database Management System (09CE1302)
2

What is Data?
 Data is a collection of one or more values.
 These values are nothing meaningful but raw, unstructured facts
and descriptions such as characters, numbers or any other data
type.
 Generally, the data has no meaningful purpose or significance.
 The data is processed appropriately to make it meaningful
otherwise it has little or no meaning to human beings.
3

What is Information?
4

What is Information?
5
 Information is a set of organized or interpreted data that has
already been processed in a meaningful manner according to
given requirements.
 It is processed, structured or presented in the desired context
to make it meaningful and useful so that human beings can
read, understand and use it.
 Information is processed data that includes data with context,
relevance, and purpose which helps to reduce undetectability
and reduce uncertainty.

What is Database?
6

What is Database?
7
 A database is an organized collection of data, so that it
can be easily accessed and managed.
 You can organize data into tables, rows, columns, and
index it to make it easier to find relevant information.
 The main purpose of the database is to operate a large
amount of information by storing, retrieving, and
managing data.
 Modern databases are managed by the database
management system (DBMS).
 SQL or Structured Query Language is used to operate on
the data stored in a database.

What is Database Management System?
8

What is Database Management System?
9
 A database-management system (DBMS) is a collection of
interrelated data and a set of programs to access those
data.
 The collection of data, usually referred to as the
database, contains information relevant to an enterprise.
 The primary goal of a DBMS is to provide a way to store
and retrieve database information that is both convenient
and efficient.

What is File System?
10
 A File Management system allows access to single files or
tables at a time.
 In a File System, data is directly stored in a set of files.
 It contains flat files that have no relation to other files (when
only one table is stored in a single file, then this file is known
as a flat file).
 This typical file-processing system is supported by a
conventional operating system.
 The system stores permanent records in various files, and it
needs different application programs to extract records from,
and add records to, the appropriate files.

What is Relational Database
Management System?
11
 An RDBMS is a type of database management system
(DBMS) that stores data in a row-based table structure
which connects related data elements.
 An RDBMS includes functions that maintain the security,
accuracy, integrity and consistency of the data.
 This is different than the file storage used in a DBMS.

Advantages of DBMS over File System
12
Data
Redundancy
Data
Inconsistency
Data Sharing
Data
Concurrency
Data
Searching
Data
Integrity
Data
Security
System
Crashing
Maintenance

Summary
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Data Models
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 Data models are visual representations of data elements and
connections between them.
 They allows us to decide how data will be stored, accessed,
shared and updated.
 Data modeling is the process of developing data model for the
data to be stored in a Database.

Types of Data Models
15
Data
Models
1. Relational Data
Model
2. Entity Relationship
Data Model
3. Object based Data
Model
4. Semistructured
Data Model
5. Network Data
Model
6. Hierarchical Data
Model

Evolution of Data Model
16

Hierarchical Data Model
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 In Hierarchical Model, a hierarchical relation is formed by
collection of relations and forms a tree-like structure.
 The relationship can be defined in the form of parent child
type.

Hierarchical Data Model
18
 The hierarchical Model is one of the oldest models in the data
model which was developed by IBM, in the 1950s.
 In a hierarchical model, data are viewed as a collection of
tables, or we can say segments that form a hierarchical
relation.
 In this, the data is organized into a tree-like structure where
each record consists of one parent record and many children.

Network Data Model
19
 The Hierarchical Model creates hierarchical tree with parent/
child relationship, whereas the Network Model has graph and links.
 The relationship can be defined in the form of links and it handles
many-to-many relations. This itself states that a record can have
more than one parent.

Network Data Model
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 The Network Model was formalized by the Database Task group
in the 1960s.
 This model is the generalization of the hierarchical model.
 This model can consist of multiple parent segments and these
segments are grouped as levels but there exists a logical
association between the segments belonging to any level.
Mostly, there exists a many-to-many logical association
between any of the two segments.

Relational Data Model
21
 This type of model designs the data in the form of rows and
columns within a table.
 Thus, a relational model uses tables for representing data and
in-between relationships. Tables are also called relations.
 This model was initially described by Edgar F. Codd, in 1969.
The relational data model is the widely used model which is
primarily used by commercial data processing applications.

Entity-Relationship Data Model
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 An ER model is the logical representation of data as objects
and relationships among them.
 These objects are known as entities, and relationship is an
association among these entities.
 This model was designed by Peter Chen and published in 1976
papers. It was widely used in database designing.
 A set of attributes describe the entities.
 For example, student_name, student_id describes the
'student' entity.

Object based Data Model
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 An extension of the ER model with notions of functions,
encapsulation, and object identity, as well. This model supports a
rich type system that includes structured and collection types.
 Thus, in 1980s, various database systems following the object-
oriented approach were developed.
 Here, the objects are nothing but the data carrying its properties.
 In the Object-Oriented Data Model, data and their relationships are
contained in a single structure which is referred to as an object in
this data model.
 In this, real-world problems are represented as objects with
different attributes. All objects have multiple relationships
between them.
 Basically, it is a combination of Object Oriented programming and a
Relational Database Model.

Semi-structured Data Model
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 This type of data model is different from the other three data
models.
 The semistructured data model allows the data specifications at
places where the individual data items of the same type may have
different attributes sets.
 The Extensible Markup Language, also known as XML, is widely used
for representing the semistructured data.
 Although XML was initially designed for including the markup
information to the text document, it gains importance because of
its application in the exchange of data.
 Semi-Structured data models deal with the data in a flexible way.
Some entities may have extra attributes and some entities may
have some missing attributes. Basically, you can represent data
here in a flexible way.

Advantages of Data Models
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 Data Models help us in representing data accurately.
 It helps us in finding the missing data and also in minimizing
Data Redundancy.
 Data Model provides data security in a better way.
 The data model should be detailed enough to be used for
building the physical database.
 The information in the data model can be used for defining
the relationship between tables, primary and foreign keys,
and stored procedures.

Basic Building blocks of Data Models
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 A data model constitutes of following building blocks:
Entities
Attributes
Relationships
Constraints

Basic Building blocks of Data Models
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 Entities: Entities are real time objects that exist. It can be a person,
place, object, event, concept. Entities are represented by a rectangle box
containing the entity name in it. Example: Student, employee.
 Attributes: It is the set of characteristics representing an entity. It is
represented by a ellipse symbol with attribute name on it. Example: A
student has attributes like name, roll number, age and much more.
 Relationship: It describes the association between two entities. It is
represented using diamond symbol containing relationship name with it.
The data model generally uses three kinds of relationships: one to many,
many to many, one to one. Example: The relationship between two
entities Student and Class has many to many relationship.
 Constraints: Constraints are conditions applied on the data. It provides
the data integrity. Example: A student can take a maximum of 2 books
from the library is applied as a constraint on the student database.

Business Rules
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 Business rules are set of approved guidelines or framework
within an organization. They simplify daily operations of any
organization.
 From an engineering perspective, a business rule is set of
reusable logic.
 Examples:
 Hierarchy of decision making and approving company invoices
 Company policies in taking medical leave
 At database level, business rules will become constraints.
 Example: Each customers invoice should have an unique
identifying number.

Business Rules
29
 So what are Constraints?
The word gives you the meaning – Limitations!
 What do we limit?
The behavior/process/mechanism/pattern of organization.
 Constraints allows you to maintain a unique structure of the
database.

What is Data Abstraction?
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What is Data Abstraction?
31
 Data abstraction is the procedure of concealing irrelevant or
unwanted data from the end user.

Degrees of Data Abstraction
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1. Physical or Internal Level
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 The physical or internal level is the lowest level of data
abstraction in the database management system.
 It is the layer that defines how data is actually stored in the
database. It defines methods to access the data in the database.
 It defines complex data structures in detail, so it is very complex
to understand, which is why it is kept hidden from the end user.
 Data Administrators (DBA) decide how to arrange data and where
to store data.
 The Data Administrator (DBA) is the person whose role is to
manage the data in the database at the physical or internal level.
 There is a data center that securely stores the raw data in detail
on hard drives at this level.

2. Logical or Conceptual level
34
 The logical or conceptual level is the intermediate or next
level of data abstraction.
 It explains what data is going to be stored in the database and
what the relationship is between them.
 It describes the structure of the entire data in the form of
tables.
 The logical level or conceptual level is less complex than the
physical level.
 With the help of the logical level, Data Administrators (DBA)
abstract data from raw data present at the physical level.

3. View or External Level
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 View or External Level is the highest level of data abstraction.
 There are different views at this level that define the parts of
the overall data of the database.
 This level is for the end-user interaction; at this level, end
users can access the data based on their queries.

Advantages of Data Abstraction
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 Users can easily access the data based on their queries.
 It provides security to the data stored in the database.
 Database systems work efficiently because of data abstraction.

Database System and Data Models - DBMS - MU

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