Explain usage of Data warehousing for information processing, analytical processing, and data Mining

  • Data warehouses are used in a wide range of applications for Business executives to perform data analysis and make strategic
  • In many firms, data warehouses are used as an integral part of a plan-execute-assess “closed-loop” feedback system for enterprise management.
  • Data warehouses are used extensively in banking and financial services, consumer goods and retail distribution sectors, and controlled manufacturing, such as demand based
  • Business users need to have the means to know what exists in the data warehouse (through metadata), how to access the contents of the data warehouse, how to examine the contents using analysis tools, and how to present the results of such
  • There are three kinds of data warehouse applications:

1. Information processing

  • It supports querying, basic statistical analysis, and reporting using crosstabs, tables, charts, or
  • A current trend in data warehouse information processing is to construct low-cost Web-based accessing tools that are then integrated with Web browsers
  • Information processing, based on queries, can find useful However, answers to such queries reflect the information directly stored in databases or computable by aggregate functions.
  • They do not reflect sophisticated patterns or regularities buried in the Therefore, information processing is not data mining.

2. Analytical processing

  • It supports basic OLAP operations, including slice-and-dice, drill-down, roll-up, and
  • It generally operates on historical data in both summarized and detailed
  • The major strength of on-line analytical processing over information processing is the multidimensional data analysis of data warehouse
  • It can derive information summarized at multiple granularities from user-specified subsets of a data

3. Data mining

  • It supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, performing classification and prediction, and presenting the mining results using visualization
  • It may analyze data existing at more detailed granularities than the summarized data provided in a data warehouse.
  • It may also analyze transactional, spatial, textual, and multimedia data that are difficult to model with current multidimensional database
Raju Singhaniya
Oct 14, 2021
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