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When cubes are created from star schemas, the various ways of dealing with a bridge can be incorporated into separate cubes With a multi-valued dimension, it is possible to create one cube that isolates a single dimension member for each fact, and another that captures impact In the case of collaborating salespeople, the first cube contains only the primary salesrep This cube is safe but incapable of producing an impact report Another cube is created that repeats an order for each salesrep This cube can produce impact reports, but care must be taken not to double count..

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A Bear Stearns study, as shown in Figure 3-1, found that the average company in the S&P 500 would have seen its earnings shrink by 8 percent in 2000 and by 20 percent in 2001 if options were factored in At technology companies the expensing reduction would have been even greater due to larger grants to executives and broad-based plans that granted options to most employees Executives at technology companies typically receive option grants that are 100 percent to 200 percent greater than grants given to their counterparts in nontech companies..

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A New Concern: Cube Proliferation Because they are less scalable, cubes are usually more targeted. In addition to providing cubes for various processes, it may be necessary to produce multiple cubes for each process, leveraging the principles of derivation and aggregation from s 14 and 15 in order to maintain a performance standard. Thus, the possibility of overproliferation of cubes becomes the primary concern in the OLAP world, replacing the need to deploy multiple semantic layers in the SQL-generation world. Careful selection of the appropriate mix of cubes necessitates evaluating the various analytic requirements for required dimensionality and grain. The one-cube-per-report approach must be avoided at all costs. Each additional cube requires resources to design, build, and maintain. Unchecked, this can lead to an unmanageable environment. Each cube should be designed to support a mix of reporting needs; each new report should attempt to make use of existing cubes.

Many multidimensional architectures provide the ability to generate and maintain cubes automatically based on information that comes from a relational schema. Cube-generating tools work especially well when the relational source is a star schema, since both technologies are used to store information based on a dimensional model. Cube generation is much like SQL generation in that it requires a watchful eye to ensure that information in the star schema is not misused. Additional attention may be required when it comes to defining hierarchies.

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Use of a business intelligence tool that generates multidimensional storage structures requires careful configuration and oversight It is possible for a cube-generator to produce inaccurate results, just as it is with an SQL generator Similarly, cube-generating tools cannot generate every possible cube, just as SQL-generating BI tools cannot generate every possible query Cubes that are most likely to be generated effectively and accurately from star schemas are those that summarize the base data without transforming its dimensional structure This kind of cube represents an aggregate of the data in the star, as described in 15 The similar dimensional structure of the aggregate to the original schema renders such cubes easy to generate Also simple to generate are cubes that represent a sliced version of the original star, as described in 14.

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