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Like a query against a fact table, a browse query is not limited to studying information at the level of detail stored in the database. Instead, queries may browse for distinct combinations of attribute values. Figure 1-7 shows some queries that browse the product dimension. The first browse in Figure 1-7 simply fetches a list of product categories. The second browse seeks the list of products within a specific category. Browse queries may return many attributes from within a dimension; some tools support browsing in a grid-like interface. The browse query is important in several respects. It may serve as the basis for the selection of query predicates, or filters, for a query that involves a fact table. A browse query may also allow users to explore the relationship between dimension values. This kind of browsing may be considered when making decisions about how to group attributes into dimensions, as discussed in 6.

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*2002 Investor Responsibility Research Center Note: Percentages reflect total options granted plus total shares reserved for future option grants, divided by the total number of shares outstanding.

The remainder of this book covers a wealth of dimensional design techniques which you can use to describe any business process. Sometimes it will be useful to understand the reason some of these techniques have been developed. Other times, it may be necessary for you to

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choose from some design options. Two simple guiding principles drive these decisions: accuracy and performance. It may seem obvious, but it is important to consider the accuracy of any given design. The questions that will be asked of an operational system can be determined in advance, and remain consistent over time, but analytic questions always lead to new questions. They will change over time, sometimes dramatically so. Designers must pay close attention to how a dimensional schema represents facts. Is it possible that they will be aggregated in ways that do not make sense Is there a design alternative that can prevent such a situation Of equal importance is the performance of the schema. An analytic design may offer little value over an operational design if it cannot produce timely results. Dimensional designs are very good at providing a rapid response to a wide range of unanticipated questions. There will be times, however, when a basic design may not be able to serve important business needs efficiently. The performance profile of a solution may drive the decision to provide information in more than one format, as will be seen throughout this book.

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Summary

companies such as Microsoft, Cisco Systems, and Intel have been very liberal in their granting of stock options to virtually all employees. When Intel was founded in 1968, options were granted to the professional staff, which represented about one-third of the workforce, the Wall Street Journal reported. In the mid-1990s Intel opted to make stock options available to all full-time employees. Like many other technology companies, the chip maker has used options heavily as a recruiting and retention tool. Grants can range up to 200,000 options, the Journal reported.8 Needless to say with such broad-based options, Intel opposes expensing them and continues to lobby against such proposals. In 2001 Intel estimated that expensing options would have reduced its earnings from $1.3 billion to $254 million.

Dimensional modeling is a design approach optimized for analytic systems. A dimensional model captures how a process is measured. Data elements that represent measurements are called facts. Data elements that provide context for measurements are called dimensions. These elements are grouped into dimension tables and fact tables. Implemented in a relational database, the design is called a star schema. The dimension tables in a star schema employ surrogate keys, enabling the analytic system to respond to changes in operational data in its own way. The granular facts in a star schema can be queried at various levels of detail, aggregated according to desired dimensional context. Exploring the details within a dimension is referred to as browsing. This chapter has only begun to introduce the fundamentals of dimensional design. After a discussion of architectures in 2, 3 will return to the basics of dimensional design.

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