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Figure 16-7 A shortcut join between main dimension and mini-dimension can confound query generators
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16 Design and Business Intelligence 389
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View: Policy with Current Coverage POLICY PAYMENT_FACTS day_key invoice_key customer_key product_key policy_key pol_coverage_key . . . (facts) . . . policy_key policy_number policy_holder adderss creation_date status pol_coverage_key_current . . . CURRENT_COVERAGE pol_coverage_key family_size covered_parties spouse_coverage covered_children deductible_amount . . . Alias for: Coverage Role: Current coverage
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HISTORIC_COVERAGE pol_coverage_key family_size covered_parties spouse_coverage covered_children deductible_amount . . . Alias for: Coverage Role: Coverage at transaction time
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Aliasing two roles for the mini-dimension eliminates confusion
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where it represents the status at the time of each transaction recorded in the fact table. When added to the query, it will be joined to the fact table, not back to the policy table. TIP When there is a shortcut relationship between a mini-dimension and the main dimension, it is important that it not be brought into queries involving the fact table. To avoid this problem, create separate aliases for the mini-dimension. One will be joined to the main dimension; the other will be joined to the fact table. To make the two versions of each attribute in the mini-dimension easily distinguishable, the views (or semantic layer) should name them appropriately. For example, the version of family_size that appears in the view that joins to the policy table can be named current_ family_size; the other can be named historic_family_size. Notice that it is only possible to cross-browse the policy table with current coverage attributes. This is a consequence of the mini-dimension, not the business intelligence tool. As discussed in 6, mini-dimensions disrupt the ability to study historic correlations between the main dimension and the mini-dimension, unless a factless fact table is added to the schema to track the history.
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PART VI
Tools and Documentation
Split Dimensions
A similar pattern occurs when a very wide dimension is split into two tables arbitrarily. Also discussed in 6, this technique differs from the mini-dimension in that both halves of the split dimension share the same surrogate key. There is a true one-to-one correspondence between rows of the tables. In this case, we would like to join the two tables together when browsing, but join each to the fact table when querying facts. The join between the two halves of the split dimension will not compromise the results, but it may disrupt performance. The best alternative here is to configure the lesser-used half of the split dimension as an outrigger. This is also not ideal, since it has the potential to disrupt a star-join optimizer. The database administrator may be the best person to make the call in this situation.
Bridge Tables
The last dimensional feature upon which business intelligence tools may have an impact is the bridge table. As you learned in s 9 and 10, bridge tables allow a single fact to refer to more than one instance of a dimension. A bridge table exploits the Cartesian product that is created when the dimension is placed in a many-to-many relationship with the fact table. Although this provides powerful analytic possibilities, it also introduces the potential for misuse. The potential for double-counting when using a bridge table can be compensated for by creating an alternative view of the schema for novice users. Like many of the other techniques in this chapter, that may be accomplished using views, or by taking advantage of features that are built into your business intelligence product. This safe configuration for the novice must be accompanied by a more flexible, albeit dangerous, solution for the experts.
Dimension Bridge Tables
A dimension bridge table allows a fact table to be associated with more than one row in a dimension table. This allows modeling of a multi-valued dimension one that can take on more than one value for a given fact. The technique is very flexible, allowing an indefinite number of dimension instances to be associated with each fact. This permits the development of impact reports, which associate the same fact with each member of the dimension. The bridge table sales_goup, at the top of Figure 16-9, permits any number of salesreps to be associated with each order. A group_key is assigned for each collaboration, and rows are added to the sales_group table for each salesrep who contributed. This allows the fact table to carry a single group_key, regardless of the number of salespeople involved. When the sum of order_dollars is grouped by salesperson_name, the value will be repeated for each salesrep who participated in the sale. This kind of report illustrates the involvement of each salesrep in the orders process. It is known as an impact report, since it would not be accurate to create a grand total from the results. Proper use of the bridge table avoids aggregating the fact across members of the group. There are three ways to avoid doing so: 1. Group results by dimension rows (generally with the natural key) 2. Constrain the query for a single member of the dimension (again using a natural key) 3. Constrain the query to isolate a single member of each group
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