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Prepared by

 Chetan kumar.T                      Email id:chinna_chetan@re

Praveen babu .U                     Email id:praveenbabu2@gm

 

Prepared  For

VAAGADEVI INSTITUTE

OF

TECHNOLOGY & SCIENCEPeddasettipalli(village),PRODDATUR.Kadapa dt.A.P

 A     PAPER    PRESENTATION      ON 

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ABSTRACT:

Organizations are today suffering from a malaise of data

overflow. The developments in the transaction processing technology has

given rise to a situation where the amount and rate of data capture is very high,

but the processing of this data into information that can be utilized for decision

making, is not developing at the same pace. Data warehousing and data

mining (both data & text) provide a technology that enables the decision-

maker in the corporate sector/govt. to process this huge amount of data in a

reasonable amount of time, to extract intelligence/knowledge in a near real

time.

The data warehouse allows the storage of data in a format that

facilitates its access, but if the tools for deriving information and/or knowledge

and presenting them in a format that is useful for decision making are not

provided the whole rationale for the existence of the warehouse disappears.

Various technologies for extracting new insight from the data warehouse have

come up which we classify loosely as "Data Mining Techniques".

Our paper focuses on the need for information  repositories  and

discovery of knowledge and hence the overview of, the so hyped, Data

Warehousing and Data Mining.

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INTRODUCTION:

“Knowledge [no more Information] is not only power, but also

has  significant competitive advantage.”

Organizations have lately realized that just processing transactions

and/or information’s faster and more efficiently, no longer provides them with

a competitive advantage vis-à-vis their competitors for achieving business

excellence.  Information  technology  (IT) tools  that are  oriented  towards

knowledge processing can provide the edge that organizations need to survive

and thrive in the current era of fierce competition. The increasing competitive

pressures and the desire to leverage information technology techniques have

led many organizations to explore the benefits of new emerging technology – 

viz. "Data Warehousing and Data Mining"

Introduction to Data Warehousing:

The age of industrial revolution has finally been completed and the world

has entered the age of information technology. The need for data warehouse

applications is one of the manifestations of this information technology age. It

has  becoming  more  of  necessity  than  an   accessory  for  a  progressiv

competitive, and focused organization.

A data warehouse supports business analysis and decision-making by

creating an enterprise-wide integrated database of summarized, historical

information.  It  integrates  data  from  multiple,  incompatible  sources  .By

transforming data into meaningful information, and a data warehouse allows

the manager to perform more substantive, accurate and consistent analysis.

The data warehouse is not the normal database, as we understand the term

“database”. Data warehouse refers to database that is maintained separately

from an organizations operational databases. A warehouse holds read-only

data.

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What is Data-Warehousing ?

DEFINITION:A data warehouse is subject-oriented, integrated, time varying, non-

volatile collection of data in support of the management’s decision-making

process. The data stored in the warehouse are not just a copy of the data at the

sources. Instead, they can be thought of as a stored view or materialized view

of the data at the sources.

The most basic component in a data warehouse is a relational database.

Relational databases are designed to be able to efficiently insert new data and

locate existing data using a standardized query language. Underneath the

database is a maze of connections and transformations connecting the data

warehouse with other systems. Because data in a company is often created and

stored in functionally specific systems (e. g: payroll system), the data may

need to be replicated and moved between a data warehouse and these other 

systems

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Functions of data warehouse:

The   main   function   behind   a   data

warehouse is to get the enterprise-

wide data in a format that is most

useful to end-users, regardless of 

their locations.

Data warehousing is used for:

• Increasing the speed and flexibility

of analysis.

• Providing   a   foundation   for 

enterprise-wide   integration   and

access.

• Improving or re-inventing business

processes.

• Gaining a clear understanding of 

customer behavior.

Architecture  Of  Data

Warehouse:

Data Warehouses and their architectures

vary depending upon the specifics

of an organization's situation.

Three common architectures are:

•

 Data Warehouse Architecture(Basic).

•  Data Warehouse Architecture

(with a Staging Area) .

• Data Warehouse Architecture

(with   a   Staging   Area   &   Data

Marts).

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Data Warehouse

Architecture(Basic)  :

It shows a simple architecture for a

Data Warehouse. End users directly

access data derived from several source

systems through the data warehouse. The

metadata and raw data of a traditional

online transaction processing (OLTP) 

system is present, as is an additional type

of data, summary data. Summaries are

very valuable in data warehouses because

they pre-compute long operations in

advance. A summary in Oracle are called

a materialized view.

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Data Warehouse Architecture(with  a staging area):

We can do this programmatically, although most data

warehouses use a staging area instead. A staging area simplifies building

summaries and general warehouse management.

Data Warehouse Architecture(with a staging area & Data

marts):We may want to customize your warehouse's architecture for 

different groups within our organization. We can do this by adding data

marts, which are systems designed for a particular line of business.

 

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Datamining with

Datawarehousing

The goal of a data warehouse is to

support decision making with data.

Data mining can be used in

conjunction with a data warehouse

to help

with certain types of decisions.

Data mining can be applied tooperational databases with

individual transactions

To make data mining more

efficient, the data warehouse

should have an aggregated or 

summarized collection of data.

Data mining helps in extracting

meaningful new patterns that

cannot be found   necessarily by

merely querying or processing

data or metadata in data

warehouse.

The knowledge discovery process

comprises four phases:

Data selection, Data about specific items

or categories of items, or from stores in a

specific region or area of the country, may

be selected.

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Data cleansing  process then may correct

invalid zip codes or eliminate records with

incorrect phone prefixes

Enrichment  typically enhances the data

with additional sources of information.

Data transformation and  encoding  may

be done to reduce the amount of data.

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Goals of Data Mining :

The Goals of data mining fall into the following classes :

Prediction: Data mining can show how certain attributes within

the data  will  behave in the future.

Identification: Data patterns can be used to identify the existence of an item, an

event, or an activity.

Classification: Data mining can partition the data so that different classes or 

categories can be identified based on combinations of  parameters.

Optimization: One eventual goal of data mining may be to optimize the use of 

limited resources such as time, space, money, or material

and  to maximize output variables such as sales or profits under a       given  se

of constraints.

 

 

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Applications of Data Mining:-

Data Mining collects, stores and organizes data for use in areas such as

• Data Mining and customer relationship management(CRM)

software for solving business decision problems

• Privacy of data in Insurance companies and Government agencies

• Fraud detection in Telecommunications and stock exchanges

• Medical diagnosis to detect abnormal patterns

• Airline reservation to maximize seat utilization

• Intelligent agency to detect abnormal behavior 

by it employees.

CONCLUSIONS:

Comprehensive data warehouses that integrate operational data with customer,

supplier,  and  market  information  have  resulted  in  an   explosion  of  inform

Competition requires timely and sophisticated analysis on an integrated view of the data.

A new technological leap is needed to structure and prioritize information for specific

end-user problems. The data mining tools can make this leap. Data warehouse and datamining plays an important role in storing data and sorting out the particular data. It has

become very easy for a user to get the information that he wants through this mining.

Quantifiable business benefits have been proven through the integration of data mining

with current information systems, and new products are on the horizon that will bring this

integration to an even wider audience of users.

REFERENCES:

1. Oracle8i warehousing by Michael Corey.

2. Data warehousing and data mining by Kurt Thearling.

3. Database management by Silberschtz, Korth.

4. www.datawarehousingonline.com

5.   Data mining byArun. K. Pujari.

6.    Data warehousing by Sunitha S, IIT Bombay