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representing concept hierarchies for the dimensions location, customer, item, and time, respectively. Each line consists of footprints representing abstraction levels of the dimension. For example, the time line has four footprints: “day,” “month,” “quarter,” and “year.” A concept hierarchy may involve a single attribute (e.g.,datefor the time hierarchy) or several attributes (e.g., the concept hierarchy for location involves the attributes street, city, province_or_state, and country). In order to examine the item sales at AllElectronics, users can roll up along the time dimension from month to quarter, or, say, drill down along the location dimension from country to city.

Figure 4.13 A starnet model of business queries.

Concept hierarchies can be used to generalize data by replacing low-level values (such as “day” for the time dimension) by higher-level abstractions (such as “year”), or to specialize data by replacing higher-level abstractions with lower-level values.

4.3. Data Warehouse Design and Usage

“What goes into a data warehouse design? How are data warehouses used? How do data warehousing and OLAP relate to data mining?” This section tackles these questions. We study the design and usage of data warehousing for information processing, analytical processing, and data mining. We begin by presenting a business analysis framework for data warehouse design (Section 4.3.1). Section 4.3.2 looks at the design process, while Section 4.3.3 studies data warehouse usage. Finally, Section 4.3.4 describes multidimensional data mining, a powerful paradigm that integrates OLAP with data mining technology.

4.3.1. A Business Analysis Framework for Data Warehouse Design

“What can business analysts gain from having a data warehouse?” First, having a data warehouse may provide a competitive advantage by presenting relevant information from which to measure performance and make critical adjustments to help win over competitors. Second, a data warehouse can enhance business productivity because it is able to quickly and efficiently gather information that accurately describes the organization. Third, a data warehouse facilitates customer relationship management because it provides a consistent view of customers and items across all lines of business, all departments, and all markets. Finally, a data warehouse may bring about cost reduction by tracking trends, patterns, and exceptions over long periods in a consistent and reliable manner.

To design an effective data warehouse we need to understand and analyze business needs and construct a business analysis framework. The construction of a large and complex information system can be viewed as the construction of a large and complex building, for which the owner, architect, and builder have different views. These views are combined to form a complex framework that represents the top-down, business-driven, or owner's perspective, as well as the bottom-up, builder-driven, or implementor's view of the information system.

Four different views regarding a data warehouse design must be considered: the top-down view, the data source view, the data warehouse view, and the business query view.

■ The top-down view allows the selection of the relevant information necessary for the data warehouse. This information matches current and future business needs.

■ The data source view exposes the information being captured, stored, and managed by operational systems. This information may be documented at various levels of detail and accuracy, from individual data source tables to integrated data source tables. Data sources are often modeled by traditional data modeling techniques, such as the entity-relationship model or CASE (computer-aided software engineering) tools.

■ The data warehouse view includes fact tables and dimension tables. It represents the information that is stored inside the data warehouse, including precalculated totals and counts, as well as information regarding the source, date, and time of origin, added to provide historical context.

■ Finally, the business