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DATA WAREHOUSING AND DATA MINING

PRESENTED BY:-

ANIL SHARMA B-TECH(IT)MBA-A

REG NO : 3470070100

PANKAJ JARIALBTECH(IT)MBA-A

REG NO : 3470070086

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DATA WAREHOUSING

Data warehousing is combining data from multiple sources into one comprehensive and easily manipulated database.

The primary aim for data warehousing is to provide businesses with analytics results from data mining, OLAP, Scorecarding and reporting.

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NEED FOR DATA WAREHOUSING

Information is now considered as a key for all the works.

Those who gather, analyze, understand, and act upon information are winners.

Information have no limits, it is very hard to collect information from various sources, so we need an data warehouse from where we can get all the information.

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TODAYS BUISNESS INFORMATION

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DATA WAREHOUSING INCLUDES:-

Retrieving data

Analyzing data

Extracting data

Loading data

Transforming data

Managing data

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DATA WAREHOUSE ARCHITECTURE

Data warehousing is designed to provide an architecture that will make cooperate data accessible and useful to users.

There is no right or wrong architecture. The worthiness of the architecture can be

judge by its use, and concept behind it . Data Warehouses can be architected in

many different ways, depending on the specific needs of a business. 

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Typical Data Warehousing Environment

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An operational data store (ODS) is basically a database that is used for being an temporary storage area for a datawarehouse.

Its primary purpose is for handling data which are progressively in use.

Operational data store contains data which are constantly updated through the course of the business operations.

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ETL (Extract, Transform, Load) is used to copy data from:-

ODS to data warehouse staging area. Data warehouse staging area to data warehouse

. Data warehouse to data mart . ETL extracts data, transforms values of

inconsistent data, cleanses "bad" data, filters data and loads data into a target database. 

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The Data Warehouse Staging Area is temporary location where data from source systems is copied. 

It increases the speed of data warehouse architecture.

It is very essential since data is increasing day by day.

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The purpose of the Data Warehouse is to integrate corporate data.

The amount of data in the Data Warehouse is massive.  Data is stored at a very deep level of detail.

This allows data to be grouped in unimaginable ways.

Data Warehouses does not contain all the data in the organization ,It's purpose is to provide base that are needed by the organization for strategic and tactical decision making.  

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ETL extract data from the Data Warehouse and send to one or more Data Marts for use of users.

Data marts are represented as shortcut to a data warehouse ,to save time.

It is just an partition of data present in data warehouse.

Each Data Mart can contain different combinations of tables, columns and rows from the Enterprise Data Warehouse. 

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REASONS FOR CREATING AN DATA MART

Easy access to frequently needed data. Creates collective view by a group of users. Improves user response time. Ease of creation. Lower cost than implementing a full Data

warehouse

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DATA MINING

The non-trivial extraction of implicit, previously unknown, and potentially useful information from large databases.

– Extremely large datasets – Useful knowledge that can improve processes – Cannot be done manually

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Where Has it Come From ?

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Motivation

Databases today are huge:

– More than 1,000,000 entities/records/rows

– From 10 to 10,000 fields/attributes/variables

– Giga-bytes and tera-bytes Databases a growing at an unprecendented rate The corporate world is a cut-throat world

– Decisions must be made rapidly

– Decisions must be made with maximum knowledge

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How does data mining work?

Extract, transform, and load transaction data onto the data warehouse system.

Store and manage the data in a multidimensional database system.

Provide data access to business analysts and information technology professionals.

Analyze the data by application software. Present the data in a useful format, such as a graph

or table

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DATA MINING MEASURES

Accuracy Clarity Dirty Data Scalability Speed Validation

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Typical Applications of Data Mining

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ADVANTAGES OF DATA MINING

Engineering and Technology Medical Science Business Combating Terrorism Games Research and Development

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Engineering and Technology

In Electrical Power Engineering

- used for condition monitoring of high

voltage electrical equipment

- vibration monitoring and analysis of

transformer on-load tap-changers Education

- to concentrate their knowledge

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Medical Science

Data mining has been widely used in area of bioinformatics , genetics

DNA sequences and variability in disease susceptibility which is very important to help improve the diagnosis, prevention and treatment of the diseases

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BUSINESS

In Customer Relationship Management applications

It Translate data from customer to merchant Accurately

Distribute Business Processes Powerful Tool For Marketing

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Combating terrorism

Concept used by Interpol against terrorists for searching their records by Multistate Anti-Terrorism Information Exchange

In the Secure Flight program , Computer Assisted Passenger Pre screening System , Semantic Enhancement

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Games

for certain combinatorial games, also called table bases (e.g. for 3x3-chess)

It includes extraction of human-usable strategies

Berlekamp in dots-and-boxes and Joh Nunn in chess endgames are notable examples

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Research And Development

Helps to Develop the search algorithms It offers huge libraries of graphing and

visualisation softwares The users can easily create the models

optimally

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List of the top eight data-mining software vendors in 2008

Angoss Software Infor CRM Epiphany Portrait Software SAS G-Stat SPSS ThinkAnalytics Unica Viscovery

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THANK YOU