Lect 30 dbms_fundamentals

Ashis Talukder, MIS, DU
Lecture on
DATABASE
Ashis Talukder
Lecturer, MIS
University Of Dhaka

Introduction
Traditional File Management
DBM
Purpose of Database Systems
View of Data
Data Models
Data Definition Language
Data Manipulation Language
Database Administrator
Database Users
Overall System Structure

Database
 Data may be logically organized
into characters, fields, records,
files & databases.

Database OrganizationDatabase Organization
Bit:
− Smallest unit of data
− binary digit (0,1/ On, Off & Y, N)
− Most basic unit of physical storage
Byte:
− Group of bits that represents a single
alphabetic, numeric or other symbols
− Also called charcter
ORGANIZING DATA IN A TRADITIONAL FILE
ENVIRONMENT

Field:
− Next higher level of data
− Consists of group of bytes or a complete
number which represent DETUM or FACT
− Example: Name, account number, roll,
balance
Record:
− Group of interrelated fields
− Example: (11, Mina, 82) (roll, name, marks)
ENVIRONMENT

File/Table:
− Group of records of same type
− Example: customer table, account table
ENVIRONMENT

Database:
− Integrated ca
− Example: customer table, account table
ENVIRONMENT

The Data Hierarchy

Data Hierarchy

Traditional File Processing

Database Management System (DBMS)
 Collection of interrelated data
 Set of programs to access the data
 DBMS contains information about a particular enterprise
 Goal of the DBMS is to provides a way to store and retrieve
database information that is both convenient and efficient.
 Database Applications:
 Banking: Customer Information, loans, and all transactions
 Airlines: reservations, schedules
 Universities: registration, grades
 Sales: customers, products, purchases
 Manufacturing: production, inventory, orders, supply chain
 Human resources: employee records, salaries, tax deductions
 Databases touch all aspects of our lives

Purpose of Database System
 In the early days, database applications were built on top of file
systems
 Drawbacks of using file systems to store data:
 Data redundancy and inconsistency
 Multiple file formats, duplication of information in different files
 Difficulty in accessing data
 Need to write a new program to carry out each new task
 Data isolation — multiple files and formats
 Integrity problems
 Integrity constraints (e.g. account balance > 0) become part of
program code
 Hard to add new constraints or change existing ones

Purpose of Database Systems (Cont.)
 Drawbacks of using file systems (cont.)
 Atomicity of updates
 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
 Concurrent access by multiple users
 Concurrent accessed needed for performance
 Uncontrolled concurrent accesses can lead to
inconsistencies
 E.g. two people reading a balance and updating it at the same time
 Security problems
 Database systems offer solutions to all the above
problems

Some Commercial Database Management Systems
 Microsoft Access
 FoxPro
 dBase
 Oracle – Oracle 8i, Oracle9i, Oracle 10g
Microsoft SQL Server
IBM DB2/DB2UDB
Informix
Sybase
MySQL
Ingress
Postgre SQL

Levels of Abstraction
 Physical level describes how a record (e.g.,
customer) is stored.
 Logical level: describes data stored in database, and
the relationships among the data.
type customer = record
name : string;
street : string;
city : integer;
end;
 View level: application programs hide details of data
types. Views can also hide information (e.g., salary)
for security purposes.

Data Abstraction

Instances and Schemas
 Similar to types and variables in programming languages
 Schema – the logical structure of the database
 e.g., the database consists of information about a set of customers and accounts
and the relationship between them)
 Analogous to type information of a variable in a program
 Physical schema: database design at the physical level
 Logical schema: database design at the logical level
 Instance – the actual content of the database at a particular point in time
 Analogous to the value of a variable
 Physical Data Independence – the ability to modify the physical schema without
changing the logical schema
 Applications depend on the logical 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.

Data Models
 A collection of tools for describing
 data
 data relationships
 data semantics
 data constraints
 Entity-Relationship model
 Relational model
 Other models:
 object-oriented model
 semi-structured data models
 Older models: network model and hierarchical
model

Entity-Relationship Model

Entity Relationship Model (Cont.)
 E-R model of real world
 Entities (objects)
 E.g. customers, accounts, bank branch
 Relationships between entities
 E.g. Account A-101 is held by customer Johnson
 Relationship set depositor associates customers
with accounts
 Widely used for database design
 Database design in E-R model usually converted
to design in the relational model (coming up
next) which is used for storage and processing

customer-
name
Customer-id
customer-
street
customer-
city
account-
number
Johnson
Smith
Johnson
Jones
Smith
192-83-7465
019-28-3746
192-83-7465
321-12-3123
019-28-3746
Alma
North
Alma
Main
North
Palo Alto
Rye
Palo Alto
Harrison
Rye
A-101
A-215
A-201
A-217
A-201
Attributes
Relational Database Model

A Sample Relational Database

Database Language
 A database system provides a
data-definition language to
specify the database schema
and a data-manipulation
language to express database
queries and update.

Data Definition Language (DDL)
 Specification notation for defining the database schema
 E.g.
create table account (
account-number char(10),
balance integer)
 DDL is also used to specify additional properties of the data.
 Example : Alter/DROP/TRUNCATE/ comment/ Grant/ Revoke
 The storage structure and access methods used by the database system by a
set of statements in a special type of DDL called a data storage and
definition language
 DDL compiler generates a set of tables stored in a data dictionary
 Data dictionary contains metadata (i.e., data about data)
 Database schema
 Data storage and definition language
 language in which the storage structure and access methods used
by the database system are specified
 Usually an extension of the data definition language

DDL ……………. Cont.
 The data values stored in the database must satisfy certain consistency
constraints.
 Domain Constraints : A domain of possible values must be associated
with every attribute ( i.e. Char type, integer type, date/time etc)
 Referential Integrity : There are cases where we wish to ensure that a
value that appear in one relation for a given set of attribute also appears
for a certain set of attributes in another relation.
 Assertions: An assertion is any condition that the database must always
satisfy. i.e Account Balance >=$100
 Authorization : differentiate among the user as far as the type of
access they are permitted on various data values in the database.
 Read authorization
 Insert authorization
 Update authorization
 Delete authorization.

Database Languages
The output of the DDL is placed in the data dictionary, which
contains metadata- that is, data about data.
1. relation-metadata (relation-name, no-of-attributes, storage-
organization, location)
2. attribute-metadata (attribute-name, relation-name, domain-
type, position, length)
3. user-metadata (user-name, encrypted-password, group)
4. index-metadata (index-name, relation-name, index-type, index-
attributes)
5. view-metadata (view-name, definition)

Data Manipulation Language (DML)
 Language for accessing and manipulating
the data organized by the appropriate data
model
 DML also known as query language
 Two classes of languages
 Procedural – user specifies what data is required
and how to get those data
 Nonprocedural – user specifies what data is
required without specifying how to get those
data
 SQL is the most widely used query language

SQL
 SQL: widely used non-procedural language
 E.g. find the name of the customer with customer-id 192-83-7465
select customer.customer-name
from customer
where customer.customer-id = ‘192-83-7465’
 E.g. find the balances of all accounts held by the customer with
customer-id 192-83-7465
select account.balance
from depositor, account
where depositor.customer-id = ‘192-83-7465’ and
depositor.account-number = account.account-
number
 Application programs generally access databases through one of
 Language extensions to allow embedded SQL
 Application program interface (e.g. ODBC/JDBC) which allow SQL
queries to be sent to a database

An Un-normalized Relation for ORDER

Normalized Tables Created from ORDER

Database Users
Users are differentiated by the way they expect to
interact with the system. Four different types:
1. Naive users – are unsophisticated users who interact
with the system by invoking one of the permanent
application programs that have been written previously.
E.g. people accessing database over the web, bank
tellers, clerical staff
2. Application programmers – are computer
professionals who write application programs. Application
programmers can choose from many tools to develop
user interface.

Database Users
 3. Sophisticated users – interact with the system without
writing programs. Instead, they form their requests in a
database query language. Analysts who submits queries to
explore data in the database.
e.g., analyst looking at sales data (OLAP – Online analytical
processing), data mining – finds certain kinds of patterns in
data.
 4. Specialized users – are sophisticated users who write
specialized database applications that do not fit into the
traditional data processing framework.
e.g., computer-aided design systems, knowledge-base and
expert systems and environment-modeling systems – uses
complex data types.

Database Administrator
(DBA)
 Coordinates all the activities of the database system; the
database administrator has a good understanding of the
enterprise’s information resources and needs. DBA has
central control of both data and the programs that
access that data.
 The functions of Database administrator (DBA) include:
 Schema definition
 Storage structure and access method definition
 Schema and physical-organization modification
 Granting of authorization for data access

Transaction Management
 A transaction is a collection of operations
that performs a single logical function in a
database application
e.g., deposit, withdrawal, transfer
between accounts
A – Atomicity, C – Consistency,
I – Isolation, D - Durability

Transaction Management
 Transaction-management
component ensures that the
database remains in a consistent
(correct) state despite system failures
(e.g., power failures and operating
system crashes) and transaction
failures
e.g., system crash cannot wipe out
“committed” transactions

Storage Manager
 A storage manager is a program module that provides the interface
between the low-level data stored in the database and the application
programs and queries submitted to the system. The storage manager
translates the various DML statements into low-level file system thus
 The storage manager is responsible for
Efficient storage, retrieval and updating of data in the database.
The storage manager components include :
 Authorization & Integrity manager: test for satisfaction of integrity
constraints and checks the authority of user to access data.
 Transaction manager: which ensure that the database remains in a
consistent despite of power failure.
 File Manager: Which manages the allocation of space on disk storage & the
data structures used to represent information stored on disk.
 Buffer Manager : Which is responsible for fetching data from disk storage into
main memory, and deciding what data to cache in main memory.

Storage Manager
The storage manager stores:
 Data files (relations): which stores the database
itself.
 Data dictionary : Which stores metadata about
structure of the database, in particular the
schema of the database. (sometimes called
catalog)
 Indices : which can provide fast access to data
Items. Like Index of a text book.

The Query Processor
 Components include:
1. DDL interpreter : Which Interprets DDL
statements and records the definitions in the
dictionary.
2. DML compiler : Which translates DML statements
in a query language into an evaluation plan
consisting of low level instruction that the query
evaluation engine understand.
3. Query evaluation engine : Which execute low-
level instructions generated by the DML compiler.

Database Administrator
 Coordinates all the activities of the database system; the database
administrator has a good understanding of the enterprise’s
information resources and needs.
 Database administrator's duties include:
 Schema definition
 Storage structure and access method definition
 Schema and physical organization modification
 Granting user authority to access the database
 Specifying integrity constraints
 Acting as liaison with users
 Monitoring performance and responding to changes in
requirements

Overall System Structure

Application Architectures
Two-tier architecture: E.g. client programs using ODBC/JDBC to
communicate with a database
Three-tier architecture: E.g. web-based applications, and
applications built using “middleware”

Centralized database:
• Used by single central processor or multiple processors in
client/server network
• There are advantages and disadvantages to having all
corporate data in one location.
• Security is higher in central environments, risks lower.
• If data demands are highly decentralized, then a
decentralized design is less costly, and more flexible.
Distributing DatabasesDistributing Databases

• Databases can be decentralized either by
partitioning or by replicating
• Partitioned database: Database is divided into
segments or regions. For example, a customer
database can be divided into Eastern customers and
Western customers, and two separate databases
maintained in the two regions.
CREATING A DATABASE ENVIRONMENT
Distributed database:

• Duplicated database: The database is completely
duplicated at two or more locations. The separate
databases are synchronized in off hours on a batch
basis.
• Regardless of which method is chosen, data
administrators and business managers need to
understand how the data in different databases will
be coordinated and how business processes might
be effected by the decentralization.

• The quality of decision making in a firm is directly
related to the quality of data in its databases.
• Data Quality Audit: Structured survey of the accuracy
and level of completeness of the data in an
information system
• Data Cleansing: Consists of activities for detecting
and correcting data in a database or file that are
incorrect, incomplete, improperly formatted, or
redundant
Ensuring Data Quality:

Online Analytical Processing (OLAP):
• Multidimensional data analysis
• Supports manipulation and analysis of large
volumes of data from multiple
dimensions/perspectives
DATABASE TRENDS
Multidimensional Data AnalysisMultidimensional Data Analysis

Multidimensional Data Model
DATABASE TRENDS

Data warehouse: A Data warehouse is a database
that stores current & historical data of potential interest
to manager throughout the company
• Supports reporting and query tools
• Stores current and historical data
• Consolidates data for management analysis and decision
making
DATABASE TRENDS
Data Warehousing and Data MiningData Warehousing and Data Mining

Components of a Data Warehouse
DATABASE TRENDS

Data mart:
• Subset of data warehouse
• Contains summarized or highly focused portion of
data for a specified function or group of users
DATABASE TRENDS
Data mining:
• Tools for analyzing large pools of data
• Find hidden patterns and infer rules to predict
trends

Benefits of Data Warehouses:
• Improved and easy accessibility to information
• Ability to model and remodel the data
DATABASE TRENDS

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