Organizing Data in a Traditional File Environment File organization Term and Concepts Computer system organizes data in a hierarchy Bit: Smallest unit of data; binary digit (0,1) Byte: Group of bits that represents a single character Field: Group of characters as word(s) or number Record: Group of related fields File: Group of records of same type

1. Database Management
Systems
Dr. Md. Rakibul Hoque
University of Dhaka

2. Organizing Data in a Traditional
File Environment
• File organization Term and Concepts
• Computer system organizes data in a hierarchy
• Bit: Smallest unit of data; binary digit (0,1)
• Byte: Group of bits that represents a single
character
• Field: Group of characters as word(s) or
number
• Record: Group of related fields
• File: Group of records of same type

3. Organizing Data in a Traditional
File Environment
• File organization Term and Concepts
• Computer system organizes data in a hierarchy
• Database: Group of related files
• Entity: Person, place, thing on which we
store information.
• Attribute: Each characteristic, or quality,
describing entity
• E.g., Attributes Date or Grade belong to entity
COURSE

4. The Data Hierarchy
A computer system
organizes data in a
hierarchy that starts
with the bit, which
represents either a 0 or
a 1. Bits can be
grouped to form a byte
to represent one
character, number, or
symbol. Bytes can be
grouped to form a field,
and related fields can
be grouped to form a
record. Related records
can be collected to form
a file, and related files
can be organized into a
database.
Organizing Data in a Traditional
File Environment

5. Traditional File Processing
The use of a traditional approach to file processing encourages each
functional area in a corporation to develop specialized applications and files.
Each application requires a unique data file that is likely to be a subset of the
master file. These subsets of the master file lead to data redundancy and
inconsistency, processing inflexibility, and wasted storage resources.
Organizing Data in a Traditional
File Environment

6. Database
• A database is a collection of information that is
organized so that it can easily be accessed,
managed, and updated. In one view, databases
can be classified according to types of content:
bibliographic, full-text, numeric, and images.
• A database is a logically coherent collection of data
with some inherent meaning, representing some
aspect of real world and which is designed, built and
populated with data for a specific purpose. A
database is not necessarily computerized. It can
be generated and maintained manually, or it may be
computerized.

7. Database
Databases are used in every part of day-to-day life.
Examples of common database use include: depositing
or withdrawing money from a bank, making a travel
reservation, accessing a library catalog, buying
something from the internet etc. These are examples of
traditional database applications, where data is stored
either in textual or numeric format. Less traditional
database applications that are starting to become more
popular include multimedia databases, which store
pictures, video clips, and sounds, Geographic
Information Systems (GIS) that store maps, satellite
images and weather data.

8. Database Management
System
 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 a database, contains
information relevant to an enterprise.
 DBMS is a system that allows inserting,
updating, deleting and processing of
data.

9. Database Management
System
 A database management system is a
collection of programs that enable users to
create and maintain a database.
-----Elmarsi & Navathe
 A database management system, or
DBMS, is a software designed to assist in
maintaining and utilizing large collections of
data. --------- Ramakrishnan & Gehrke

10. Goals/Purposes of DBMS
 The primary goal of a DBMS is to provide
an environment that is both convenient
and efficient for people to use in
retrieving and storing information.
 Database systems are designed to
manage large bodies of information.
Management of data involves both
defining structures for storage of
information and providing mechanisms for
manipulation of information.

11. Goals/Purposes of DBMS
 The database system must ensure the
safety and security of the information
stored, despite system crashes or attempts
at unauthorized access.
 The system must avoid possible
anomalous results if data are to be shared
among several users.

12. Database System
Applications
Databases are widely used. Some
representative applications are:
1. Banking: For customer information,
accounts, loans and banking transactions.
2. Airlines/Railways/Road Transport: For
ticket reservation, schedules and routes.

13. Database System
Applications
3. Universities: For student information,
courses and grades (education
management).
4.Credit card transaction: For
purchases on credit card, monthly
statement generation

14. Database System
Applications
5.Telecommunication: For keeping
records of call made, generating
monthly bills, maintaining balances on
prepaid calling cards, storing
information about the communication
networks.

15. Database System
Applications
6.Finance: For storing information
about holdings, sales, and purchases
of financial instruments such as stocks
and bonds.
7. Sales: For customer, product and
purchase information.

16. Database System
Applications
8. Manufacturing: For management of
supply chains and for tracking production
of items in factories, inventories of items
in warehouses/stores and order for items.
9. Human resources: For information
about employees, salaries, payroll taxes
and benefits and for generation of pay
checks.

17. Some Commercial Database
Management Software
 For Personal Computers
1. Microsoft Access
2. FoxPro
3. dBase

18. Some Commercial Database
Management Software
1.Oracle – Oracle 8i, Oracle9i, Oracle 10g, 11g
2. Microsoft SQL Server
3. IBM DB2/DB2UDB
4. Informix
5. Sybase
6. Ingress

19. Some Open Source Database
Management Software
1. CUBRID
2. Firebird
3. MariaDB
4. MongoDB
5. Postgre SQL
6. MySQL
7. SQLite

20. Database Systems Vs File
Systems ( Why DBMS?)
 Ordinary file system has a number of major
drawbacks:
1. Data redundancy and inconsistency-
Multiple file formats, duplication of
information in different files.
2.Difficulty in accessing data
- Need to write a new program to carry out
each new task

21. Database Systems Vs File
Systems ( Why DBMS?)
3.Data isolation
-Multiple files and formats
4.Integrity problems
- Integrity constraints (e.g. account
balance > 0) become part of program
code
- Hard to add new constraints or
change existing ones.

22. Database Systems Vs File
Systems ( Why DBMS?)
5. Atomicity problems
- Failures may leave database in an
inconsistent state with partial updates
carried out. E.g., transfer of funds from
one account to another should either
complete or not happen at all.

23. Database Systems Vs File
Systems ( Why DBMS?)
 6. Concurrent-access anomalies
- Needed for system performance and
usability
- Uncontrolled concurrent accesses
can lead to inconsistencies. E.g. two
people reading a balance and updating
it at the same time.

24. Database Systems Vs File
Systems ( Why DBMS?)
7. Security problems: Not every user of
the database system should be able to
access all the data.
Database systems offer solutions to
all these problems

25. View of Data
 A database system is a collection of
interrelated files and set of programs
that allow users to access and modify
these files. A major purpose of a
database system is to provide users
an abstract view of the data. That is,
the system hides certain details of how
the data is stored and maintained.

26. Data Abstraction
Several levels of data abstraction are
maintained to simplify users’ interaction
with the system:
1. Physical level: The lowest level of
abstraction describes how data are
actually stored. It describes complex
low-level data structures in detail.

27. Data Abstraction
 2. Logical level: The next higher level
of abstraction describes what data
stored in database, and what
relationships exist among those data.
The logical level thus describes the
entire database in terms of a number of
relatively simple structures.

28. Data Abstraction
 For Example:
 create table customer (
customer_id varchar(10),
customer_name varchar (35),
customer_street varchar (20),
salary number (10,2));

29. Data Abstraction
 3. View level: The highest level of
abstraction describes only part of the
entire database. Computer users see a
set of application programs that hide
details of data types. Several views of
database are defined and users see
these views. In addition to hiding details,
the views also provide a security
mechanism to prevent users from
accessing certain part of the database.

30. Data Abstraction

31. Instances and Schemas
 Schema: The overall design of the
database is called the database
schema. Schemas are changed
infrequently, if at all. This corresponds
to the variable declaration (with type
definition) of a programming language.

32. Instances and Schemas
Schemas are:
1.Physical schema: Describes the
database design at physical level
2.Logical schema: Describes the
database design at logical level
3.View schema: Describes the database
design at view level

33. Instances and Schemas
Instances: Databases change over time
as information is inserted and deleted. The
collection of information stored in the
database at a particular moment is
called an instance of the database. While
executing, each variable of a programming
language has a particular value at a given
instant. The values of the variable in a
program at a point in time correspond to
an instance of the database schema.

34. Instances and Schemas
 Physical Data Independence – The
ability to modify the physical schema
without changing the logical schema is
called physical data independence.
- Applications depend on the logical
schema (or the view schema)
- In general, the interfaces between the
various levels and components should be
well defined so that changes in some
parts do not seriously influence others.

35. Data Models
 A data model is a collection of
conceptual tools for describing data,
data relationship, data semantics and
consistency constraints.
A. Base Models: Describes the
design of the database at the logical
level.

36. Data Models
 A. 1. Entity-Relationship Model: This
is a higher-level data model. It is
based on a perception of a real world
that consists of a collection of basic
objects, called entities and the
relationship among these objects.

37. Data Models
 Entity: An entity is a “thing” or “object”
in the real world that is distinguishable
from all other objects. An entity has a set
of properties, called attributes and the
values for some set of
properties/attributes may uniquely
identify an entity. An entity may be
concrete, such as a person or a book,
or it may be abstract, such as loan, or a
holiday, or a concept.

38. Data Models
 customer- customer-id, customer-
name, customer-street, customer-city
 loan – loan-number, amount

39. Data Models
 Relationship: A relationship is an
association among several entities. A
depositor relationship associates a
customer with each account that he or
she has.
The set of all entities of the same type
and the set of all relationships of the
same type are termed an entity set and
relationship set, respectively

40. Data Models

41. Data Models
A. 2. Relational Model: This is a
lower level model. It uses a collection
of tables to represent both data and
relationships among those data.
Each table has multiple columns, and
each column has a unique name.

42. Data Models
 The relational model is an example of a
record-based model. This is because
the database is structured in fixed-format
records of several types. Each table
contains records of a particular type.
Each record type defines a fixed no. of
fields, or attributes. The columns of the
table correspond to the attributes of the
record type.

43. Data Models
 The relational model is the most widely
used data model and a vast majority of
current database systems are based
on the relational model.
The relational model is at a lower level
of abstraction than the E-R model.
Database designs are often carried out
in the E-R model and then translated
to the relational model.

44. Data Models

45. Data Models
 B. Other Models:
B. 1. Object-oriented data model: Drawing
increasing attention. It can be seen as extending of
E-R model with notions of encapsulation, methods
(functions) and object identity.
An object database (also object-oriented
database management system, OODBMS) is a
database management system in which information
is represented in the form of objects as used in
object-oriented programming. Object databases
are different from relational databases which are

46. Data Models

47. Data Models
 B. 2. Object-relational data model: Combines the
features of object-oriented data model and
relational data model.
 An object-relational database (ORD), or object-
relational database management system
(ORDBMS), is a database management system
(DBMS) similar to a relational database, but with an
object-oriented database model: objects, classes
and inheritance are directly supported in database
schemas and in the query language.

48. Data Models
 B. 3. Semi-structured data model: Permits the
specification of data where individual data items of
the same type may have different sets of attributes.
The extensible markup language (XML) is widely
used to represent semi-structured data.
 The semi-structured model is a database model
where there is no separation between the data and
the schema, and the amount of structure used
depends on the purpose. It can represent the
information of some data sources that cannot be
constrained by schema.

49. Data Models

50. Data Models
 C. Historical Models: These are in
little use now.
C. 1. Network data model
C. 2. Hierarchical model

51. Data Models
 The network model is a database model
conceived as a flexible way of
representing objects and their
relationships. Its distinguishing feature is
that the schema, viewed as a graph in
which object types are nodes and
relationship types are arcs, is not
restricted to being a hierarchy or lattice.

52. Network DBMS:
• Depicts data logically as many-to-many
relationships
The Network Data Model
Data Models

53. TYPES OF RELATIONS
ONE-TO-ONE: STUDENT ID
ONE-TO-MANY:
CLASS
STUDENT
A
STUDENT
B
STUDENT
C
MANY-TO-MANY:
STUDENT
A
STUDENT
B
STUDENT
C
CLASS
1
CLASS
2

54. Data Models
 A hierarchical database model is a
data model in which the data is
organized into a tree-like structure.
The data is stored as records which
are connected to one another through
links. A record is a collection of fields,
with each field containing only one
value.

55. Hierarchical DBMS:
• Organizes data in a tree-like structure
• Supports one-to-many parent-child
relationships
• Prevalent in large legacy systems
Data Models

56. A Hierarchical Database for a Human Resources
System

57. Thank
You