Dbms

Data Base Management
System
(DBMS)
Presented by :Ms.Surkhab Shelly
Astt. Prof. in computer Science Deptt.

Contents
 Data Hierarchy
 Traditional File Processing
 Database approach to Data Management
 DBMS- Features and Capabilities
 Database Schemas
 Components of DBMS
 Data Models
 RDBMS
 Normalization
 What is it and why is it required?
 Background of Normalization: Definitions
 The process of normalization

Data Hierarchy
Data Hierarchy refers to the systematic organization of
data, often in a hierarchical form. A computer system
organizes data in a hierarchy that starts with bits and bytes
and progresses to fields, records, files, and databases. A bit
represents the smallest unit of data a computer can handle.
A group of bits, called a byte, represents a single
character, which can be a letter, a number, or another
symbol
Data organization involves fields, records, files and so on.
 A field holds a single fact - Consider a date field, e.g.
"September 19, 2004". This can be treated as a single date
field (eg birthdate), or 3 fields, namely, month, day of
month and year.
 A record a collection of related fields. An Employee record
may contain a name field(s), address fields, birthdate field

Data Hierarchy
 A file is a collection of related records. If there are 100
employees, then each employee would have a record (e.g.
called Employee Personal Details record) and the collection
of 100 such records would constitute a file (in this
case, called Employee Personal Details file).
 Files are integrated into a Database. This is done using a
Database Management System. If there are other facets of
employee data that we wish to capture, then other files
such as Employee Training History file and Employee Work
History file could be created as well.

Traditional File Processing
 The use of a traditional approach to file processing encourages
each functional area in a corporation to develop specialized
applications. 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.
 Each application, requires its own files and its own computer
program to operate. For example, the human resources functional
area might have a personnel master file, a payroll file, a medical
insurance file, a pension file, a mailing list file, and so forth until
tens, perhaps hundreds, of files and programs existed. In the
company as a whole, this process led to multiple master files
created, maintained, and operated by separate divisions or
departments. As this process goes on for 5 or 10 years, the
organization is saddled with hundreds of programs and
applications that are very difficult to maintain and manage. The
resulting problems are data redundancy and
inconsistency, program-data dependence, inflexibility, poor data
security, and an inability to share data among applications.

Database approach to Data
Management
Database
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 .
DBMS
A Data Base Management System (DBMS) is a set of software
programs that enables users to define, create and maintain a
database. The DBMS also enforces necessary access restrictions
and security measures in order to protect the database.
Database technology cuts through many of the problems a traditional
file organization creates. Database serves many applications
efficiently by centralizing the data and controlling redundant data.
Rather than storing data in separate files for each
application, data are stored so as to appear to users as being
stored in only one location.
For example, instead of a corporation storing employee data in
separate information systems and separate files for
personnel, payroll, and benefits, the corporation creates a single
common human resources database

DBMS Features and Capabilities

 Query abilty: Querying is the process of requesting attribute
information from various perspectives and combinations of
factors.
 Backup and Replication: Copies of attributes are regularly
created to cater to the situation when primary disks or other
equipment fails. Data is consistently replicated among various
database servers.
 Rule Enforcement: Application of rules to attributes so that
attributes are clean and reliable – ability to add and updates to
rules without significant data layout redesign.
 Security: Application of limits for who can see or change which
attributes or groups of attributes.
 Controlling of Redundancy

 Computation: There are common computations requested on
attributes such as
counting, summing, averaging, sorting, grouping, cross-
referencing, etc
 Change and access logging: Often one wants to know who
accessed what attributes, what was changed, and when it was
changed. Logging services allow this by keeping a record of
access occurrences and changes
 Automated Optimization: If there are frequently occurring
usage patterns or requests, some DBMS can adjust themselves to
improve the speed of those interactions. In some cases the DBMS
will merely provide tools to monitor performance, allowing a
human expert to make the necessary adjustments after reviewing
the statistics collected.
 Provides multiple user interfaces

Database Schema
Database Schema: A database schema is its structure described in
a formal language supported by the DBMS. In a relational
database, the schema defines the tables, the fields in each
table, and the relationships between fields and tables.
The three levels of abstractions are:
1. Physical level: the lowest level of abstraction describes how data
is stored: files, indices, etc. on the random access disk system. It
also typically describes the record layout of files and type of files
(hash, b-tree, flat).
2. Logical level: Hides details of the physical level. In the relational
model, this schema presents data as a set of tables. The DBMS
maps data access between the logical to physical schemas
automatically.
 Physical schema can be changed without changing application:
 DBMS must change mapping from conceptual to physical.
 Referred to as physical data independence.

Database Schema, contd.
3. View level (External Schema):
It is tailored to the needs of a particular
category of users. Portions of stored data
should not be seen by some users and
simplifies the view for these users. E.g.
students should not see faculty salaries.
Applications are written in terms of an
external schema. The external view is
computed when accessed. It is not stored.
Translation from external level to logical
level is done automatically by DBMS at run
time. The conceptual schema can be
changed without changing application.
Mapping from external to conceptual must
be changed. This is referred
as conceptual data independence.

Components of DBMS
A database management system has three components:
1. A data definition language (DDL) is the formal
language programmers use to specify the structure of the
content of the database. DDL defines each data element
as it appears in the database before that data element is
translated into the forms required by application
programs. With this help a data scheme can be defined
and also changed later.
Typical DDL operations (with their respective keywords in
SQL):
 Creation of tables and definition of attributes
(CREATE TABLE ...)
 Change of tables by adding or deleting attributes
(ALTER TABLE …)
 Deletion of whole table including content (!) (DROP
TABLE …)

Components of DBMS
2. A data manipulation language (DML) is a language for
the descriptions of the operations with data like
store, search, read, change, etc. the so-called data
manipulation, is needed. Typical DML operations (with
their respective keywords in the structured query
language SQL):
 Add data (INSERT)
 Change data (UPDATE)
 Delete data (DELETE)
 Query data (SELECT)
Often DDL and DML for the definition and manipulation of
databases are combined in one comprehensive language.
A good example is the structured query language SQL.

Components of DBMS
3. Data Dictionary: This is an automated or manual file
that stores definitions of data elements and data
characteristics, such as usage, physical
representation, ownership (who in the organization is
responsible for maintaining the data), authorization, and
security.
Many data dictionaries can produce lists and reports of
data use, groupings, program locations, and so on.

Data Models
A data model is a theory or specification describing how a database
is structured and used.
A data model is not just a way of structuring data: it also defines a
set of operations that can be performed on the data. The
relational model, for example, defines operations such as
select, and join. Although these operations may not be explicit in
a particular query language, they provide the foundation on which
a query language is built.
Common Data Models:
 Hierarchical Model
 Network Model
 Relational Model
 Object Model (Object Oriented Database Management System)
The relational model is the most widely used model today.

Hierarchical Model
In a hierarchical model, the data is
organized into a tree-like structure.
The structure allows repeating
information using parent/child
relationships: each parent can have
many children but each child only has
one parent. This structure is simple but
nonflexible because the relationship is
confined to a one-to-many relationship.
These models were popular in late
1960s, and in 1970. The most widely
used hierarchical databases
is IMS developed by IBM.

Network Model
The network model is a variation on the
hierarchical model – allowing each record to
have multiple parent and child records.
Network models generally implement the set
relationships by means of pointers that
directly address the location of a record on
disk. This gives excellent retrieval
performance, at the expense of operations
such as database loading and
reorganization.
Some well known DBMS using Network
Model:
 Honeywell IDS (Integrated Data Store)
 IDMS (Integrated Database Management
System)

Relational Model
The data is stored in two-dimensional tables (rows and columns).
The data is manipulated based on the relational theory of
mathematics.
Properties of Relational Tables:
 Values Are Atomic
 Each Row is Unique
 Column Values Are of the Same Kind
 The Sequence of Columns is Insignificant
 The Sequence of Rows is Insignificant
 Each Column Has a Unique Name
A relational database management system (RDBMS) is a DBMS that
is based on the relational model.
Some well known RDBMS:
IBM DB2, Informix, Microsoft SQL Server, Microsoft Visul
Foxpro, MySQL, Oracle, Sybase, Teradata, Microsoft Access

Object Model
Object model (ODBMS, object-oriented database management
system): The data is stored in the form of objects, which are
structures called classes that display the data within. The fields are
instances of these classes .
The object oriented structure has the ability to handle
graphics, pictures, voice and text, types of data, without difficultly
unlike the other database structures. This structure is popular for
multimedia Web-based applications. It was designed to work with
object-oriented programming languages such as Java.

RDBMS
A RDBMS stores information in a set of "tables", each of which has a
The tables are then related to
A foreign key is simply the
unique identifier or "primary key” (PK).
one another using "foreign keys” (FK).
primary key in a different table.
In the example above, "Customer ID" is the PK in one table and the FK
in another. The arrow represents a one-to-many relationship between
the two tables. The relationship indicates that one customer can have
one or more orders. A given order, however, can be initiated by one
and only one customer.

Normalization
Normalization is a systematic way of ensuring that a database structure is
suitable for general-purpose querying and free of certain undesirable
characteristics that could lead to a loss of data integrity.
The objectives of normalization:
 Free the database of modification anomalies
 Minimize redesign when extending the database structure
 Make the data model more informative to users
 Avoid bias towards any particular pattern of querying
In general, relational databases should be normalized to the "third normal
form".

Background to Normalization:
Definitions
Functional Dependency: If A and B are attributes of relation R, B is
functionally dependent on A (denoted A  B), if each A value is
associated with precisely one B value.
Or in other words, In every possible legal value of R (relation),
whenever two tuple agree on their A values, they also agree on their
B value.
Determinant of a functional dependency refers to attribute or group of
attributes on left-hand side of the arrow.
e.g. in an "Employee" table that includes the attributes "Employee ID" and
"Employee Date of Birth", the functional dependency {Employee ID} →
{Employee Date of Birth} would hold.

Definitions
Full Functional Dependency
 A and B are attributes of a relation,
 B is fully dependent on A if B is functionally dependent on A but
not on any proper subset of A.
A functional dependency X  Y is full functional dependency if removal of
any attribute A from X means that the dependency does not hold any
more.

Definitions
Transitive Dependency: A transitive dependency is an indirect functional
dependency. Let A, B, and C designate three distinct attributes in the
relation. Suppose all three of the following conditions hold:
 A → B
 It is not the case that B → A
 B → C
Then the functional dependency A → C is a transitive dependency.
The functional dependency {Book} → {Author Nationality} applies; that is, if
we know the book, we know the author's nationality. Furthermore:
 {Book} → {Author}
 {Author} → {Author Nationality}
 {Author} does not → {Book}
Therefore {Book} → {Author Nationality} is a transitive dependency.

Definitions
An Index or Key is an attribute or collection of attributes that may be used
to identify or retrieve one or more records.
SuperKey: A superkey is a set of columns within a table whose values can be
used to uniquely identify a row.
e.g. Imagine a table with the fields <Name>, <Age>, <SSN> and <Phone
Extension>. This table has many possible superkeys. Three of these are
<SSN>, <Phone Extension, Name> and <SSN, Name>. Of those
listed, only <SSN> is a candidate key, as the others contain information
not necessary to uniquely identify records
A candidate key is a key that can be used to uniquely identify record. I.e., it
may be used to retrieve one specific record.
The primary key of a relation is a candidate key that has been designated
as the main key.
A foreign key is an attribute (or collection of attributes) in a relation that can
be used as a key to another relation. Foreign keys link tables together to
form an integrated database.

The Process of Normalization
There are two main steps of the normalization process:
eliminate redundant data (for example, storing the same
data in more than one table) and ensure data dependencies
make sense (only storing related data in a table). Both of
these are worthy goals as they reduce the amount of space
a database consumes and ensure that data is logically
stored.
 Formal technique for analyzing a relation based on its
primary key and functional dependencies between its
attributes.
 Often executed as a series of steps. Each step corresponds
to a specific normal form, which has known properties.
 As normalization proceeds, relations become progressively
more restricted (stronger) in format and also less
vulnerable to update anomalies.

First Normal Form (1NF)
No Repeating Elements or Groups of Elements
A relation in which intersection of each row and column contains one
and only one value.
 All key attributes get defined
 No repeating groups in table
 All attributes dependent on primary key
UNF to 1NF:
 Eliminate duplicative columns from the same table (In other
words.. Remove subsets of data that apply to multiple rows of a
table and place them in separate tables.).
 Create separate tables for each group of related data and identify
each row with a unique column or set of columns (the primary
key).
 Create relationships between these new tables and their
predecessors through the use of foreign keys.

Second Normal Form (2NF)
No Partial Dependencies on a Concatenated Key
A relation that is in 1NF and every non-primary-key attribute is fully
functionally dependent on the primary key (no partial dependency).
1NF to 2NF:
 Identify primary key for the 1NF relation.
 Identify functional dependencies in the relation.
 If partial dependencies exist on the primary key remove them by placing
them in a new relation along with copy of their determinant (in other
words, remove columns that are not fully dependent upon the primary
key).
 Create relationships between these new tables and their predecessors
through the use of foreign keys.

Third Normal Form (3NF)
No Dependencies on Non-Key Attributes
A relation that is in 1NF and 2NF and in which no non-primary-key attribute is
transitively dependent on the primary key.
2NF to 3NF
 Identify the primary key in the 2NF relation.
 Identify functional dependencies in the relation.
 If transitive dependencies exist on the primary key remove them by
placing them in a new relation along with copy of their determinant.

Boyce-Codd normal form (BCNF)
A relation is in Boyce-Codd normal form (BCNF) if every determinant is a
candidate key.
 Difference between 3NF and BCNF is that for a functional dependency A
B, 3NF allows this dependency in a relation if B is a primary-key
attribute and A is not a candidate key.
 Whereas, BCNF insists that for this dependency to remain in a
relation, A must be a candidate key.

Dbms

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