barcode vb.net 2010 Factless Fact Tables 305 in Software

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12 Factless Fact Tables 305
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Factless fact tables are essential in many business situations. The techniques outlined in this chapter will help you model a process with no apparent facts, or to capture the conditions that can be used to study activities. Some processes are measured only by counting events or activities. These processes can be modeled as a fact table whose grain is a single activity. No facts are required. Useful analysis is conducted by counting fact table rows. Dimensions are used to constrain the query, group or sort counts, and drive subtotals. A fact with a constant value of 1 can be added to the design to make SQL easier to read and write. Some important relationships between dimensions do not clearly correspond to a process or activity. Examples include conditions, coverage, and eligibility. Conditions and processes can be combined for useful analysis. Conditions are modeled using a factless fact table. Activities and conditions can be combined in various ways using set operations, correlated subqueries, or derived schemas. Type 2 slow changes will generate new rows in related fact tables that describe conditions.
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The techniques in this chapter have applicability in a wide variety of industries and across many subject areas. If you want to refine your knowledge, you can learn more about factless fact tables by studying more examples. The following are some places to start. Factless Fact Tables for Activities A factless fact table can be used to capture information about attendance. An example can be found in 6 of The Data Warehouse Lifecycle Toolkit, Second Edition (Wiley, 2008) by Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, and Bob Becker. Kimball and Ross update the attendance model to incorporate absences in 12 of The Data Warehouse Toolkit, Second Edition (Wiley, 2002). In this case, the fact table is no longer factless; it includes a fact that contains the values 0 or 1 (absent or present). This can also be achieved with a pair of facts one used to count absences and one used to count attendance. In the same chapter, the authors provide a factless design to track student course registrations. In the insurance industry, a factless fact table can be used to track accident events. Kimball and Ross describe this in 15 of The Data Warehouse Toolkit.
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When factless fact tables for events are summarized, the resulting aggregates are not factless. This phenomenon is explained in 8 of Mastering Data Warehouse Aggregates (Wiley, 2006) by Chris Adamson. Factless Fact Tables for Conditions A factless fact table used to track marketing promotions supplements a retail sales star in 12 of The Data Warehouse Toolkit. Note that Kimball and Ross refer to factless fact tables that describe conditions, coverage, or eligibility as coverage tables. Another condition that can be tracked by a factless fact table is the availability of a resource. Kimball and Ross make use of one to study utilization of facilities in 12 of The Data Warehouse Toolkit. It is generally not useful to aggregate a fact table that describes conditions, because doing so destroys the meaning of the original data. This phenomenon is explored in 8 of Mastering Data Warehouse Aggregates.
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Type-Specific Stars
The things represented by a dimension table can often be subdivided into categories or types. Sometimes, the attributes you would like to record in the dimension table vary by type. A department store, for example, describes clothing items with a very different set of attributes from electronic items, yet all of them are products. Similarly, businesses often collect very different information about direct customers and corporate customers, or track different kinds of information for different kinds of facilities or locations. Specialization by type may also extend to the fact table. The facts surrounding an order, for example, may vary depending on whether the order was for merchandise or for a subscription. Tests, inspections, and citations may also have one or more facts that vary by type. In these situations, the dimensional modeler is faced with a dilemma. On one hand, there is a desire to study a business process across all types. On the other hand, there is a desire to study each particular type, using the type-specific attributes. This chapter shows you three ways to accommodate heterogeneous attributes, both within dimensions and within fact tables. A single star solution calls for one star that contains all possible attributes. This is the simplest approach but is not viable if the number of type-specific attributes is very large. The core and custom approach provides multiple stars. A core star contains all common attributes; one custom star for each type contains any type-specific attributes. This is the most flexible approach and may be accomplished using a variety of implementation techniques. The use of generic attributes creates multipurpose columns, the contents of which are determined by type. This technique is only viable when all interaction with the star will take place through a customized application.
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