This document discusses data warehousing and online analytical processing (OLAP) technology. It defines a data warehouse, compares it to operational databases, and explains how OLAP systems organize and present data for analysis. The document also describes multidimensional data models, common OLAP operations, and the steps to design and construct a data warehouse. Finally, it discusses applications of data warehouses and efficient processing of OLAP queries.
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.
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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