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Figure 71-3 shows a user hierarchy that rst presents the browser with a list of countries, which can be expanded into a list of states, then cities, and so on. Ultimately, the user will experience the dimension as some combination of attribute and user hierarchies. One of the most important practices to optimize cube performance is the careful construction of user hierarchies in conjunction with attribute relationships. This follows from how Analysis Services pre-calculates data summaries, called aggregations, to speed query performance. For example, totals by year or month might be pre-calculated along the time dimension. To understand attribute relationships, consider a simple time dimension with attributes for year, quarter, month, and day, with day relating to the fact table (key attribute). By default, every attribute in a dimension is related directly to the key attribute, resulting in the default relationships shown in Figure 71-5(a) and (b). The value for each non-key level summarizes all the day values. Contrast this to the properly assigned relationships in Figure 71-5(c), in which values for the month level must reference all the day values, but the quarters level need only reference the months, and the years need only reference the quarters.

>$days=@{ January =31; February =28; March =31; April =30; June =30} >$days[ January ] 31

The research foundation TEE with Thorleif Hjeltnes (ManagingDirector) as our primary contact. The organisation has contributed in the publication of this book by giving financial support. The Faculty o Information Technologg and e-Leaning has f played a substantial role by means of its professional skills and good working environment. The publishing companyTAPIR who is responsible for the original,Norwegian editors.

Figure 25-4: Example implementation of the data-access-object pattern (XML Source)

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