• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Data warehouse and olap technology
 

Data warehouse and olap technology

on

  • 8,429 views

Data warehouse and olap technology

Data warehouse and olap technology

Statistics

Views

Total Views
8,429
Views on SlideShare
8,370
Embed Views
59

Actions

Likes
5
Downloads
0
Comments
0

4 Embeds 59

http://www.dataminingtools.net 26
http://dataminingtools.net 23
http://reminderkeepsake.blogspot.de 9
http://reminderkeepsake.blogspot.com 1

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Data warehouse and olap technology Data warehouse and olap technology Presentation Transcript

    • Data Warehouse and OLAP Technology
    • 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.
    • 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
    • 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.
    • Comparison between OLTP and OLAP
    • A Multidimensional Data Model
      Tables and Spreadsheets to Data Cubes, Stars, Snowflakes, and Fact Constellations are example for Multidimensional Databases
    • 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.
    • 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
    • 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.
    • 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
    • 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
    • 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
    • Visit more self help tutorials
      Pick a tutorial of your choice and browse through it at your own pace.
      The tutorials section is free, self-guiding and will not involve any additional support.
      Visit us at www.dataminingtools.net