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Most people need to spend some time exploring dimension content before formulating a query. This allows them to choose which attributes should appear on a report and which should be used as filters. As you learned in 1, the act of exploring a dimension table is called browsing. Unfortunately, this is an alien concept to most business intelligence tools, which treat the browse as they would any other query. Although most lack a browser interface, business intelligence tools work just fine when building reports that include data from a single dimension table. This is a reasonable stand-in for the browse, particularly if the user interface allows the user to interactively explore the results. However, there are still some design features that may lead to trouble. Mini-dimensions and split dimensions both pose problems when users wish to browse them.
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Mini-Dimensions and Browsing
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When a dimension table is likely to experience a high rate of type 2 changes, designers can mitigate excessive growth by splitting some attributes off into a separate table, called a minidimension. As you learned in 6, the mini-dimension will contain a subset of attributes, each of which has a finite set of possible values. The table is constructed with a Cartesian product of all possible combinations, and never requires further updating. Because these attributes have been removed from the main dimension, the number of new rows generated by type 2 changes is dramatically reduced. The drawback to a mini-dimension is that it is no longer possible to browse the attributes together with the main dimension. They are now in a separate table and only related via facts.
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Part VI
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In Figure 6-5, for example, coverage attributes were separated from a policy dimension. Once separated, the attributes of these two tables are only linked via the fact table. Developers sometimes add a shortcut key in the main dimension that refers to the current corresponding row in the mini-dimension. This is depicted in Figure 16-7. While the intentions are good, this design feature tends to trip up query generators. Policy and policy_coverage can be related in two ways: directly or through the fact table. We would like the join between policy and policy_coverage to be exploited only when browsing. When a fact table is involved, it should be omitted. This is very important, since the coverage data for an old fact may be different from what is current. As you should now be able to recognize, query generators do not respond well when there are multiple possible ways to relate tables. If your business intelligence tool does not have some features that can help work around this situation, the best strategy is to create a separate logical view of the schema, which merges policy with the current coverage characteristics. This can be done by using a view, or from within the semantic layer of the business intelligence tool. A second alias of the coverage table will be connected to the fact table. The result is depicted in Figure 16-8. The user will have different versions of coverage attributes to choose from when creating reports: one set that represents current coverage characteristics of policies, and the other which represents those that were in effect for each fact. For example, family_size in the view represents the current family size. It can be browsed with attributes of policy, even though it is technically a separate table. Family_size also appears in historic_coverage,
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DAY PAYMENT_FACTS INVOICE day_key invoice_key customer_key product_key policy_key pol_coverage_key . . . (facts) . . .
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POLICY policy_key policy_number policy_holder address creation_date status pol_coverage_key_current . . . POLICY_COVERAGE policy_coverage_key family_size covered_parties spouse_coverage covered_children deductible_amount . . .
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