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Part III
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Dimension Design
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Bridge tables are complicated but powerful. If you are still struggling with any of the concepts in this chapter, it may help to see other examples. Here are some places you will find them: Multi-Valued Dimensions In a health care example, Kimball and Ross describe the use of a bridge table to associate a billing fact table with multiple diagnoses. This allows the construction of an impact report illustrating total costs associated with any given diagnosis. This example also contains an allocation factor. See 13 of The Data Warehouse Toolkit, Second Edition (Wiley, 2002). The possibility of time-stamping changes to group membership in a dimension bridge table was suggested but not illustrated. For an example, see the earlier referenced health care scenario from Kimball and Ross. Although the time stamp is not pictured in the example, it is described in the text. It should be obvious that to maintain the time stamp it will be necessary to have a separate group for each patient who has the same set of diagnoses. Building aggregates to summarize within a bridged dimension can be risky. It is important to consider how to summarize the data appropriately, how double-counting will be eliminated, and what the resulting aggregate actually describes. I deal with this topic in 8 of Mastering Data Warehouse Aggregates (Wiley, 2006). Multi-Valued Dimension Attributes In 8 of The Data Warehouse Toolkit, Kimball and Ross use a bridge to associate multiple skills with each employee in an employee dimension table. In the same example, Kimball and Ross explore an alternative not discussed in this book: the creation of a single attribute that concatenates the multiple values. This solution is effective when used with SQL substring functions to search for particular values but cannot be used to group results for an impact report. Kimball and Ross also show how an attribute bridge can be used to associate multiple account holders with an account dimension in 9 of The Data Warehouse Toolkit.
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Recursive Hierarchies and Bridges
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Recursive hierarchies in real-world data pose one of the most perplexing challenges for dimensional designers. This chapter explores two ways to model this kind of hierarchy. One is simple and safe but is limited in its capabilities. The other is powerful and flexible but also easy to misuse and difficult to manage. Choosing between these options requires fully understanding the implications of both. A recursive hierarchy is a set of relationships among instances of the same kind of object or entity. In a corporate organizational structure, one department may report to another department, which in turn reports to still another department. Systems of describing geographic regions can also be recursive, with regions falling within other regions. These recursive hierarchies present a unique reporting challenge. When studying facts, people will want to choose a position in the hierarchy and report on all activity below it or above it. Show me the total spending in departments beneath mine, is one example. If the hierarchy is to be used as a means for aggregating facts, it must be represented in the dimensional model. The simple solution is to flatten the hierarchy, identifying a fixed number of levels that will be associated with each instance. For every department, for example, a flattened hierarchy might record the top-level and second-level departments to which it reports. As you will see, flattened hierarchies have the advantage of fitting nicely into the standard dimensional framework. Unfortunately, they are not always satisfying from an analytic perspective. A far more powerful solution uses a hierarchy bridge table. Like the dimension bridge tables of 9, the hierarchy bridge table is a double-edged sword. The solution is able to address a much wider range of analytic questions than a flattened solution, and it does so with great flexibility. However, this analytic capability is accompanied by an increase in the complexity of the solution, danger of misuse, and significant new demands on the ETL process. These considerations are far more significant than those associated with the dimension bridges of 9, and often outweigh the analytic advantages.
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