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These cubes limit scope by focusing on a subset of the original members, such as a single department or region Other forms of cubes may be more difficult to generate in an automated manner A merged cube, for example, must be constructed according to the standard drill-across process, lest it result in double-counting Many cube-generating tools can handle this requirement; for those that do not, it may be necessary to build the cube manually This may involve use of tools provided by the vendor to define cube-processing routines It may also be achieved by building a merged fact table, then using it as the basis to auto-generate the merged cube Other kinds of derived cubes also require one of these techniques Examples include building snapshot cubes from a transaction star, accumulating snapshots from transactions, cubes that juxtapose coverage with a process, and so forth.

Bear Stearns Employee Stock Option Expense Report July 2002*

Developers may choose to forgo automatic generation of cubes so that they can retain control of when and how cube processing takes place Some products require the developer.

7,500 Dow Transportation Average 7,000

to relinquish control of cube maintenance, save for some configuration options, in order to generate cubes. This can make it difficult to integrate the maintenance of cubes into a complex, dependency-laden architecture. In order to retain control of the refresh cycle for all analytic data, it is often useful to assume manual control even where automatic control is possible.

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Multidimensional products often rely on the declaration of attribute hierarchies in the source star schema. These hierarchies may be used to define the level of detail to be included in a generated cube. They may also be used to support drilling within a generated cube. Attribute hierarchies, however, represent a narrow view of the potential ways in which data may be summarized, as you learned in 7. More importantly, there may be more than one possible attribute hierarchy within a dimension.

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Option Expense as a Percentage of Earnings Expense as a percentage of earnings 1999 260% 221% 49% 42% 35% 30% 28% 25% 24% 20% 17% 17% 15% 14% 12% N/M N/M N/M N/M N/M N/M N/M Expense as a Expense as a percentage of percentage of earnings 2000 earnings 2001 12% 71% 17% 11% 18% 16% 20% 12% 16% 11% 219% 10% 12% 16% 11% 27% N/M 1887% 58% 53% 14% 150% 11% 55% 10% 21% 26% 11% 12% 10% 29% 10% N/M 11% 10% 29% 33% 15% 44% 664% 25% 27% 14% 31%

Hierarchies Influence on Cube Generation or Drilling An attribute hierarchy represents a dimension as a series of levels in a successive master-detail relationships, placing each dimension attribute within one of the levels. The attributes of a product dimension table, for example, might be grouped into three levels of successive detail: categories, brands, and products. One additional level, representing all products, is typically added to this representation. The hierarchy might be represented as follows:

All Products Categories Brands Products Similar attribute hierarchies may be defined for other dimensions: All Salesreps Regions Territories Salesreps All Customers Industries Customers All Dates Years Quarters Months Days Defining attribute hierarchies in this manner makes it possible to define a cube s level of summarization simply by choosing the appropriate level from each dimension. Figure 15-5 depicted this act diagrammatically: a summary of order facts was defined that included month, product, saleserep, and all customers. Some tools that generate cubes work on this paradigm. The relational source is described in terms of attribute hierarchies, which are then used to define the level of aggregation for each cube. If brand is included in a cube, for example, the generator will include all category and brand-level attributes in the generated data structure. Similarly, subdivision of dimension attributes into levels facilitates the drilling feature of many OLAP tools. Once a cube is constructed, the attribute hierarchy is used to determine what happens when a user requests to drill into a data element. If he or she drills into a category, for example, the attribute hierarchy determines that the next level is brand. For products that make use of attribute hierarchies, either to define cubes or to facilitate drilling, it will be necessary to document the attribute relationships as part of the dimensional design. Figure 7-3 provided one way to do this. This diagram illustrated the included attributes at each level. Some tools may require additional information, such as the declaration of a unique identifier for each level.

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