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None of these approaches is right or wrong; the best design option depends on the situation. If you are faced with heterogeneous attributes, study the implications of each option before making your design decision.
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The need to support heterogeneous attributes within a dimensional design becomes evident when gathering requirements for the data warehouse. You may notice that managers of different parts of the business have different ways of describing and measuring their processes, while senior management likes to lump them together. Book sales and subscription sales, for example, may be processed by different groups and measured in different ways, while senior management likes to lump these together into a single view of sales. These different views may be reflected in operational systems, or they may not. Regardless of the structure of the operational data, however, it will be necessary to support both kinds of views in the data warehouse.
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Operational systems usually support a type-specific view of the data. In some cases, this may be immediately obvious simply by studying the documentation of the operational systems. More often than not, however, the commonality across types is not clearly recorded. In the world of entity-relationship modeling, type-specific attributes can be represented using a concept called subtyping. This technique can be easily identified in a logical-level ER diagram. One diagramming standard, for example, represents subtypes as boxes within entities. The outer box, or supertype, contains attributes that are common across all types. Each inner box, or subtype, contains the appropriate type-specific attributes. Alternate diagram notations represent relationships between supertypes and subtypes in different ways, such as relationship lines that incorporate special PRODUCT symbols. sku An example of an ER diagram containing subtypes appears name in Figure 13-1. The product entity in this diagram represents publisher the products sold by a seller of books and magazines. A single product_type product entity contains subtypes for books and subscriptions. BOOK_PRODUCT All the products have certain core attributes, such as the SKU author (an internal identifier), the product name, and the publisher. genre Each subtype has some additional attributes. Other entities length binding may be related to the supertype (products) or to one of the more attributes subtypes (books or subscriptions). More often than not, heterogeneous attributes within an important entity will not be clearly laid out. Operational SUBSCRIPTION_ PRODUCT systems are focused on the minutia of process execution and may not provide for a consolidated point of view. Subscription term frequency and book orders, for example, might be handled in entirely annual_cover_price different systems, with no acknowledgment that subscriptions more attributes and books are examples of a single thing called a product. This may reflect the way the business is organized, result from Figure 13-1 Subtypes in an a history of mergers and acquisitions, or be the result of an entity-relationship model organic evolution of internal systems.
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13 Type-Specific Stars 309
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The data warehouse must reflect all the ways in which a process is analyzed and measured. If limited to a single analytic perspective, its overall utility is greatly reduced. Management may want to study orders across all products books and subscriptions using shared facts and dimensions. They may also call for a more detailed representation when focusing on a particular product type, using facts and dimensions unique to the particular type. Failure to deliver one or the other of these capabilities destroys the value of the overall solution.
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The Single-Star Approach
The simplest way to deal with heterogeneous attributes in a dimensional model is to build a single star containing all possible attributes. For a seller of books and magazines, a single product dimension would contain all possible product attributes, including those specific to books or magazines. For any particular row in this table, many of these attributes will be blank (or will contain a NULL alternative such as N/A, as described in 6). Similarly, if different facts are collected for book orders and magazine orders, all potential facts are included in the fact table. For each order, only the relevant facts are recorded. This approach is fully valid. It permits the business to conduct analysis across types or to perform type-specific analysis. The attributes included in each query or report determine its focus. A report that contains attributes common to books and magazines, for example, describes the overall orders process. The details of subscription orders can be studied by limiting analysis to subscription products and using attributes from the product table that apply only to subscriptions.
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