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Data warehouse and olap technology
Data warehouse and olap technology
Data warehouse and olap technology
Data warehouse and olap technology
Data warehouse and olap technology
Data warehouse and olap technology
Data warehouse and olap technology
Data warehouse and olap technology
Data warehouse and olap technology
Data warehouse and olap technology
Data warehouse and olap technology
Data warehouse and olap technology
Data warehouse and olap technology
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Data warehouse and olap technology


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Data warehouse and olap technology

Data warehouse and olap technology

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  • 1. Data Warehouse and OLAP Technology
  • 2. What is a Data Ware House?
    Data warehousing provides architectures and tools for business executives to systematically organize, understand, and use their data to make strategic decisions.
  • 3. Difference between data warehouses and other data repository systems
    Subject-oriented: A data warehouse is organized around major subjects, such as customer,supplier, product, and sales.
    Integrated: A data warehouse is usually constructed by integrating multiple heterogeneoussources, such as relational databases, flat files, and on-line transaction records.
    Time-variant: Data are stored to provide information from a historical perspective(e.g., the past 5–10 years).
    Nonvolatile: A data warehouse is always a physically separate store of data transformed from
  • 4. Differences between Database Systems Operational Data Warehouses
    The major task of on-line operational database systems is to perform on-line transaction and query processing. These systems are called on-line transaction processing (OLTP) systems.
    Data warehouse systems serve users or knowledge workers in the role of data analysis and decision making. Such systems can organize and present data in various formats in order to accommodate the diverse needs of the different users. These systems are known as on-line analytical processing (OLAP) systems.
  • 5. Comparison between OLTP and OLAP
  • 6. A Multidimensional Data Model
    Tables and Spreadsheets to Data Cubes, Stars, Snowflakes, and Fact Constellations are example for Multidimensional Databases
  • 7. OLAP Operations in the Multidimensional Data Model
    OLAP provides a user-friendly environment for interactive data analysis.
    Roll-up: The roll-up operation (also called the drill-up operation by some vendors)performs aggregation on a data cube, either by climbing up a concept hierarchy fora dimension or by dimension reduction.
    Drill-down: Drill-down is the reverse of roll-up. It navigates from less detailed data to more detailed data
    Slice and dice: The slice operation performs a selection on one dimension of thegiven cube, resulting in a sub cube.
    Pivot (rotate): Pivot (also called rotate) is a visualization operation that rotates the data axes in view in order to provide an alternative presentation of the data.
  • 8. Steps for the Design and Construction of Data Warehouses
    The Design of a Data Warehouse: A Business Analysis Framework
    The Process of Data Warehouse Design
    A Three-Tier Data Warehouse Architecture design
    Building of Data Warehouse Back-End Tools and Utilities
    Building a Metadata Repository
  • 9. Different applications of data warehouse
    Information processing supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts, or graphs
    Analytical processing supports basic OLAP operations, including slice-and-dice, drill-down, roll-up, and pivoting.
    Data mining supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization tools.
  • 10. Efficient Processing of OLAP Queries
    Determine which operations should be performed on the available cuboids
    Determine to which materialized cuboid(s) the relevant operations should be applied
  • 11. On-Line Analytical Mining
    On-line analytical mining (OLAM) (also called OLAP mining) integrates on-line analytical processing (OLAP) with data mining and mining knowledge in multidimensional databases. Among the many different paradigms and architectures of data mining systems
  • 12. Importance of OLAM
    High quality of data in data warehouses
    Available information processing infrastructure surrounding data warehouses
    OLAP-based exploratory data analysis
    On-line selection of data mining functions
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