Data warehouse and data mining

Abstract

The architectural foundations of decision support systems are data warehouse and data mining. The nature of a data warehouse includes integrated data, detailed and summarized data, historical data and metadata. Integrated data enable the data miner to easily and quickly look across vistas of data. Detailed data is important when the miner wishes to examine data in its most detailed form while historical data is essential because important information nuggets are hidden in this type of data. Metadata serves as a roadmap to the miner who utilizes metadata in describing the context of information. Examples of architectural extension of the data warehouse are Online Analytical Processing (OLAP) data marts.