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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
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  • 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
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  • 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
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Hinweis der Redaktion

  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