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Business intelligence tools geared for multidimensional databases provide a user experience that is very different from those provided by tools geared for relational databases. As discussed in 3, these applications take advantage of the cube s ability to precompute the value of facts across different members of each dimension. This results in instantaneous response, enabling a level of interactivity that contrasts sharply with the query/response cycle for a relational schema. This characteristic is responsible for the first two letters in the acronym OLAP (Online Analytic Processing). Online connotes real-time interactivity. As the focal point of the OLAP experience, the cube replaces the semantic layer of an SQL generator. Specific tools may provide a mechanism to provide an abstracted view of a cube, but the presentation of available data elements is intrinsically dimensional and usually does not require the creation of virtual dimension elements. To compute these on the fly would eliminate the performance advantage of OLAP. OLAP tools often do, however, support calculated facts. When working with an OLAP tool, the user starts by choosing a cube with which to work. Because of this, OLAP tools are ideally positioned to provide alternative perspectives on data. Want to study orders Access the orders cube. Want to study shipments Access the shipments cube. Want to compare the process Access the orders and shipments cube. Sophisticated OLAP tools may provide the ability to link two or more cubes, but the affinity remains. It is expected that different cubes will be provided for different purposes, or for users of different skill sets.

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Online performance comes at a cost; as the number or cardinality of attributes grows, cubes can become extremely large. This has led to the cube being tagged as less scalable than relational storage in a star schema. Vendors have countered by providing the ability to optimize cubes for large data sets, but this requires sacrificing some of the performance benefit. Many data warehouse architectures manage the trade-off between relational scalability and OLAP performance by using the star schema to store granular dimensional data from which cubes are created for OLAP applications. This paradigm was discussed in 3. Cubes built in this architecture summarize or transform the structure of the underlying dimensional data held in the star. This provides a wealth of opportunities to design cubes for specific purposes. Cubes can take the form of any of the numerous derived schema designs from 14 or aggregate designs from 15.

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Because multiple cubes are expected and accepted in an OLAP environment, none of the hand-wringing caused by the need to deploy multiple semantic layers in SQL-generating environments will be encountered. Drilling across, dealing with multiple fact tables, and providing multiple configurations for bridged solutions are handled with grace, simply by producing multiple cubes and controlling who can access them.

The FASB applauds those companies because recognizing compensation expense relating to the fair value of employee stock options granted is the preferable approach under current US accounting standards It is also the treatment advocated by an increasing number of investors and other users of financial statements, the FASB said in a statement issued July 31, 2002 Until now, only a handful of companies elected to follow the preferable method The decision to expense options is more than just lip service on the part of companies The action will impact earnings In the case of Coca-Cola, the Financial Times reported that if the company had expensed options in 2001, it would have reduced net income from $397 billion to $377 billion and its earnings per share from $160 to $151.

Cubes for Each Process, Cubes for Comparing Processes Although many OLAP tools are capable of drilling into multiple cubes simultaneously, the ability to do so is less of a concern for the OLAP BI tool The addition of a merged cube that compares two processes fits naturally into this environment, although it may seem a kludge for SQL generators Because each cube can be selected individually, there is also far less concern about how to deal with factless queries The path for linking dimensions together is determined when a cube is selected as the focus of analysis Scenarios that are factless also present no problem; the simple process of selecting the cube determines the scope of analysis.

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