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Balan Boobalan

This document discusses data warehousing and mining. It explains that data warehousing is the process of consolidating data from multiple sources into a centralized location. Data mining is the process of analyzing this data to find patterns and relationships. It describes typical analysis tasks, challenges in data warehousing, OLAP for interactive queries, and basic data mining techniques like classification, clustering, and association rule mining.

Formation
1  sur  19
Data warehousing and mining Session VII (Part 1) 15:45 - 16:10 Sunita Sarawagi School of IT, IIT Bombay
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
Typical data analysis tasks ,[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
Dr. Sunita Sarawagi Data Warehousing & Mining Operational data Detailed transactional data Data warehouse Merge Clean Summarize Direct Query Reporting tools Mining tools Decision support tools Oracle SAS Relational DBMS+ e.g. Redbrick IMS Crystal reports Essbase Intelligent Miner Bombay branch Delhi branch Calcutta branch Census data OLAP GIS data
Data warehouse construction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
Warehouse maintenance ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
Dr. Sunita Sarawagi Data Warehousing & Mining Operational data Detailed transactional data Data warehouse Merge Clean Summarize Direct Query Reporting tools Mining tools Decision support tools Oracle SAS Relational DBMS+ e.g. Redbrick IMS Crystal reports Essbase Intelligent Miner Bombay branch Delhi branch Calcutta branch Census data OLAP GIS data
OLAP ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
OLAP ,[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
OLAP products ,[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
Microsoft OLAP strategy ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
Data mining ,[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
Some basic operations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
Classification ,[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining Age Salary Profession Location Customer type Previous customers Classifier Decision rules Salary > 5 L Prof. = Exec New applicant’s data Good/ bad
Classification methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
Clustering ,[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
Association rules ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining Milk, cereal Tea, milk Tea, rice, bread cereal T
Mining market ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining
Conclusions ,[object Object],[object Object],[object Object],Dr. Sunita Sarawagi Data Warehousing & Mining

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Session7part1

  • 1. Data warehousing and mining Session VII (Part 1) 15:45 - 16:10 Sunita Sarawagi School of IT, IIT Bombay
  • 2.
  • 3.
  • 4. Dr. Sunita Sarawagi Data Warehousing & Mining Operational data Detailed transactional data Data warehouse Merge Clean Summarize Direct Query Reporting tools Mining tools Decision support tools Oracle SAS Relational DBMS+ e.g. Redbrick IMS Crystal reports Essbase Intelligent Miner Bombay branch Delhi branch Calcutta branch Census data OLAP GIS data
  • 5.
  • 6.
  • 7. Dr. Sunita Sarawagi Data Warehousing & Mining Operational data Detailed transactional data Data warehouse Merge Clean Summarize Direct Query Reporting tools Mining tools Decision support tools Oracle SAS Relational DBMS+ e.g. Redbrick IMS Crystal reports Essbase Intelligent Miner Bombay branch Delhi branch Calcutta branch Census data OLAP GIS data
  • 8.
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  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
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  • 19.

Notes de l'éditeur

  1. Data Warehousing & Mining Dr. Sunita Sarawagi
  2. Data Warehousing & Mining Dr. Sunita Sarawagi Start with a real-life scenario
  3. Data Warehousing & Mining Dr. Sunita Sarawagi CHECK ON THE PRODUCTS INTERESTING ALGORITHMS
  4. Data Warehousing & Mining Dr. Sunita Sarawagi
  5. Data Warehousing & Mining Dr. Sunita Sarawagi
  6. Data Warehousing & Mining Dr. Sunita Sarawagi
  7. Data Warehousing & Mining Dr. Sunita Sarawagi Cognos and microstrategy next in line 1.4B in 1997, 40% growth from 1994-97, expected to be 3B in 2000 Source: http://www.olapreport.com/Market.htm
  8. Data Warehousing & Mining Dr. Sunita Sarawagi
  9. Data Warehousing & Mining Dr. Sunita Sarawagi Each topic is a talk..
  10. Data Warehousing & Mining Dr. Sunita Sarawagi Absolute: 40 M$ 40M$, expected to grow 10 times by 2000 --Forrester research