10 - Business Intelligence

Abstract

Data warehousing, online analytical processing (OLAP), and data mining are three areas of computer science that are tightly interlinked and marketed under the heading of business intelligence. The functionalities of these three areas complement each other. This chapter covers each of these technologies in turn. The requirements for a data warehouse, its basic components and principles of operation, the critical issues in the design of a data warehouse, and the important logical database design elements in a data warehouse environment are discussed. The basic elements of OLAP and data mining as special query techniques applied to data warehousing are investigated. Data warehousing provides an infrastructure for storing and accessing large amounts of data in an efficient and user-friendly manner. Dimensional data modeling is the approach best suited for designing data warehouses. OLAP is a service that overlays the data warehouse. The purpose of OLAP is to provide quick response to ad hoc queries, typically involving grouping rows and aggregating values. Roll-up and drill-down operations are typical. OLAP systems automatically perform some design tasks, such as selecting which views to materialize in order to provide quick response times. OLAP is a good tool for exploring the data in a human-driven fashion, when a person has a clear question in mind. Data mining is usually computer driven, involving analysis of the data to create likely hypotheses that may be of interest to users. Data mining can bring to the forefront valuable and interesting structure in the data that would otherwise have gone unnoticed.