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16 Design and Business Intelligence 395
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ORDER_FACTS day_key product_key salesrep_key company_key . . . order_dollars . . . COMPANY company_key company_id company_name company_type . . .
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Novice Configuration Study only the company that placed the order Avoids double-counting COMPANY COMPANY_HIERARCHY_ BRIDGE superior_company_key subordinate_company_key company_key company_id company_name company_type . . .
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ORDER_FACTS day_key product_key salesrep_key company_key . . . order_dollars . . .
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Expert Configuration 1: Looking Down from a Company Groups transactions that occur at or below each company Double-counting is possible COMPANY COMPANY_HIERARCHY_ BRIDGE superior_company_key subordinate_company_key company_key company_id company_name company_type . . .
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Part VI
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Expert Configuration 2: Looking Up from a Company Groups all transactions that occur at or above each company Double-counting is possible
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PART VI
Tools and Documentation
also discussed in 10, requires use of a subquery to produce a distinct list of companies below (or above) the group in question. This distinct list is then used to filter the main query. It is not likely that this kind of query will be generated successfully by a business intelligence tool; such reports will likely require that SQL be edited by hand.
Working with Cube-Based BI
Many business intelligence solutions are based on multidimensional cubes, rather than relational stars. These are generally referred to as Online Analytical Processing (OLAP) tools. Because the derived schema is already a central concept in cube-centric implementations, many of the solutions and workarounds from the SQL-generating world are a natural fit in the OLAP world. In environments where cubes are created based on relational star schemas, the business intelligence product may be capable of automatically generating cubes. When this is the case, auto-generated cubes will be subject to the same kind of careful scrutiny as autogenerated SQL.
Cube-Centric Business Intelligence
Business intelligence tools that support interaction with multidimensional databases center on cubes, rather than a semantic layer. With cubes as the top level of the analytic taxonomy, these implementations lend themselves nicely to solutions that provide different views of the data for different purposes. Whereas multiple semantic layers may seem confusing in the relational world, multiple cubes are natural in the multidimensional world.
Focus on the Cube
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.
16 Design and Business Intelligence 397
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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