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Encoding Quick Response Code in Software Design and Business Intelligence 377

16 Design and Business Intelligence 377
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will only make these items available to users of the tool itself; anyone writing SQL or using a different tool will not benefit. Dimensions are not the only area where this capability may prove tempting. Facts, too, often contain some degree of redundancy. For an orders model, you might be tempted to design a fact table that contains order_quantity, and use the semantic layer to multiply the value by standard price and cost data to produce fully additive facts like order dollars. Once again, though, this generates a runtime burden for the RDBMS, which must compute extended amounts on the fly. Further, it provides no such benefits to people using a different tool.
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Relying on Subqueries
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Many business intelligence tools are capable of producing subqueries, which can be very powerful in qualifying analysis. For example, the business may categorize customers based on spending behavior high-volume, average volume, and low-volume customers. This categorization is based on analysis of previous spending, as recorded in a fact table somewhere. A user might want to use this categorization to filter query results. A business intelligence tool may provide the capability to define this computation so that it can easily be added to a report as a filter. When invoked by the user, it generates the necessary subquery to segment customers. However, this capability is often difficult to make use of. More importantly, it is likely to hamper response time. If it is to be commonly used, this kind of classification is better incorporated directly into the dimensional design as a behavioral dimension. As you learned in 6, behavioral dimensions precompute categorizations based on existing facts as part of the ETL process, dramatically improving performance at query time. Implemented in this manner, they will also be accessible to all users of the schema, ensuring a consistent definition is used for concepts like high-volume customer. TIP If users will use past behavior to qualify a query, don t rely on a business intelligence tool to generate subqueries. Instead, incorporate a behavioral dimension into the design. Once again, this does not mean you should completely avoid use of such features. Adding this ability within a business intelligence environment may allow for speedier deployment, since it does not impact the design of an existing schema or the alteration of an existing ETL process. In addition, computing this kind of information at query time, rather than when the schema is loaded, may make sense if it is not frequently used.
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While some business intelligence capabilities should not be used when it is possible to achieve the same thing in the schema design, there are other situations where the opportunities offered by a semantic layer are highly useful.
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Compute Nonadditive Facts
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Many important facts are not additive. For example, margin rate is usually expressed as the percentage of an order that represents margin. Ratios like this are not additive; selling two items at 10 percent margin does not result in 20 percent margin. As you learned in 3, nonadditive facts should be broken down into fully additive components. These can be stored
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Part VI
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PART VI
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Tools and Documentation
at a detailed level and rolled up as needed. The ratio is computed in queries or reports as a final step, after the data has been aggregated. The nonadditive fact is not stored explicitly, since it would not be possible to aggregate it. If the business intelligence tool allows it, the semantic layer is an ideal place to define a nonadditive fact. For margin rate, it might be defined as follows:
sum( margin_dollars ) / sum( order_dollars )
By defining this computation centrally, you ensure that reports will compute it correctly and consistently as the ratio of two sums. For this to work, the SQL generator must dynamically construct the appropriate GROUP BY clause when this item is added to a query. TIP Use business intelligence tools to define nonadditive facts that can be computed from fully additive components. This is one of the most valuable contributions that a semantic layer can make to the dimensional design. Other contributions, such as concatenated columns or decoded values, can be duplicated by creating a view. A nonadditive fact cannot be expressed in a view, because the scope of aggregation varies with each query. The GROUP BY clause must contain whatever dimension attributes have also been requested. A query generator can handle this dynamic aspect with grace.
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