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Data Mining and Data Warehousing

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Paper Presentation for Data Mining and Data Warehosuing.

Paper Presentation for Data Mining and Data Warehosuing.

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Transcript

  • 1. Yogesh Benawat Sameer Deshmukh
  • 2. Outline
    • Data Mining
    • Data Warehousing
    • Q ‘n’ A
    • Conclusion
  • 3. Historical Perspective
    • 1960s:
      • Data collection, database creation, IMS and network DBMS
    • 1970s:
      • Relational data model, relational DBMS implementation
    • 1980s:
      • RDBMS, advanced data models (extended-relational, OO, deductive, etc.) and application-oriented DBMS (spatial, scientific, engineering, etc.)
    • 1990s —2000s :
      • Data mining and data warehousing, multimedia databases, and Web databases
  • 4. Data Mining
  • 5. Definition
    • Data mining automates the process of locating and extracting the hidden patterns and knowledge
    • In simple words
      • Searching for new knowledge
  • 6. Why we need data mining
    • Data explosion problem
      • Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories
    • We are drowning in data, but starving for knowledge!
    • Solution: Data mining
      • Data warehousing and on-line analytical processing
      • Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data in large databases
  • 7. Data Mining Models
    • Predictive Model
    • Descriptive Model
  • 8. Predictive Model
    • Prediction
      • determining how certain attributes will behave in the future
    • Regression
      • mapping of data item to real valued prediction variable
    • Classification
      • categorization of data based on combinations of attributes
    • Time Series analysis
      • examining values of attributes with respect to time
  • 9. Descriptive Model
    • Clustering
      • most closely data clubbed together into clusters
    • Data Summarization
      • extracting representative information about database
    • Association Rules
      • associativity defined between data items to form relationship
    • Sequence Discovery
      • it is used to determine sequential patterns in data based on time sequence of action
  • 10. Data mining process Fig. General Phases of Data Mining Process Problem Definition Creating Database Exploring database Preparation for creating a data mining model Building Data Mining Model Evaluation Phase Deploying the Data Mining model
  • 11. Who needs data mining?
    • Whoever has information fastest and uses it wins
      • Don McKeough former president of Coke Cola
    • Businesses are looking for new ways to let end users find the data they need to:
      • make decisions
      • Serve customers
      • Gain the competitive edge
  • 12. Applications
    • Business analysis and management
    • Computer security
    • Customer relationships analysis and management
    • Telecommunication analysis and management
    • News and entertainment
    • Bioinformatics and Healthcare analysis
  • 13. Summary
    • Need of data mining
    • Data mining models
    • Process of data mining
    • Some applications
  • 14. Data Warehousing
  • 15. Data Warehousing
    • Data Warehouse
      • What is Data Warehouse?
        • Database & Data Warehouse.
        • How to distinguish?
          • Purpose
            • Database : Transactional
            • Data Warehouse :Intended for Decision Supporting Applications.
          • Functionality
            • Optimized for data retrieval, not routine transaction processing.
          • Structure
          • Performance
  • 16. Data Warehousing
    • Modern Organization’s needs ?
      • Companies spread world wide.
        • Have
          • So many Data Sources
          • Different Operational Systems
          • Different Schemas
        • Need Data for
          • Complex Analysis
          • Knowledge Discovery
          • Decision Making .
        • Solution ???
  • 17. Data Warehousing
    • Solution … Data Warehouse.
    • Data Warehouse . Definition ??
      • No single definition….
    • Data Warehouse
      • Collection of Information gathered from multiple sources , stored under unified schema , at a single site & mainly intended for decision support applications.
      • A subject oriented, integrated, nonvolatile, time-variant, collection of data in support of management’s decision.
      • ~ W.H. Inmon
  • 18. Warehouses are Very Large Databases 35% 30% 25% 20% 15% 10% 5% 0% 5GB 5-9GB 10-19GB 50-99GB 250-499GB 20-49GB 100-249GB 500GB-1TB Initial Projected 2Q96 Source: META Group, Inc. Respondents
  • 19. Data Warehousing
    • Data Warehouse - Architecture
  • 20. Data Warehousing
    • Data Warehouse building
      • When & how to gather data
        • Source-driven architecture
        • Destination-driven architecture
      • What schema to use
      • Data Cleansing
        • Task of correcting and processing data
      • How to propagate updates
      • What data to summarize
      • And many more……
  • 21. Summary
    • What is Data Warehousing?
    • Data Warehouse.
    • Data Warehouse – Architecture
    • Data Warehouse vs. Data Mining
  • 22. Conclusion
    • Your data is full of undiscovered gems; start digging!
  • 23. References
    • Data Mining Introductory and advanced Topics
    • Margaret H. Dunham
    • Modern Data Warehousing, Mining, and visualization George M. Marakas
    • Data Mining
    • BPB Publications
    • Database System Concepts
    • Silbershatz, Korth, Sudarshan
    • www.statoo.info/
    • www.crm2day.com/
    • www.trilliumsoftware.com/
  • 24. Q ‘n’ A
  • 25. Thank You!