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Part VI
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PART VI
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Competing Hierarchies For OLAP tools based on attribute hierarchy paradigms, problems arise when it is necessary to support more than one hierarchy within a dimension. A day dimension, for example, may possess two hierarchies: one for calendar years and one for fiscal years. A product dimension, too, may be subject to alternative hierarchies: one that segments products based on how they are produced and one that segments them based on how they are marketed. Some OLAP tools that work with attribute hierarchies do not accommodate competing hierarchies. Using these tools to generate cubes will require aliasing a source star twice, so that each can be defined with a different hierarchy. This makes it possible to define each desired cube. If calendar and fiscal views of order facts are required, for example, it will be necessary to present two logical versions of the orders star to the OLAP tool. For one version, a fiscal hierarchy is defined in the time dimension; for the other, a calendar hierarchy is defined. These two versions of the star are required at the logical level only. Their physical implementation may be achieved by using views or aliases. They can then be used to define and generate the necessary cubes. When used to guide drilling processes, attribute hierarchies may also necessitate the creation of multiple cubes. For example, one cube may be used to study orders using marketing s product hierarchy; another may be used to study orders using manufacturing s product hierarchy. Although these cubes contain the same attributes, they differ in what the OLAP front-end application will do if someone drills into the product dimension. Other Kinds of Hierarchy An attribute hierarchy is one of many ways that the information in a dimension may be organized to describe a progression from summary to detail. As you learned in 7, the same set of data may be explored in many different ways, some of which do not hinge on this kind of representation. A product, for example, has a color. In an attribute hierarchy, color would be included at the product level. However, color may be a particularly low cardinality attribute. It may be useful to summarize products by color in a cube, or to drill from color to product. When this kind of summarization is useful, either to define a cube or to support a drill-down process, it will be necessary to work around the attribute-hierarchy focus of some OLAP tools. Following the same approach used for competing hierarchies, an alternative view of the schema is declared for which the product hierarchy is specified as:
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All Products Product Color Product This alternate view of the source star is used as the basis of cubes that will aggregate based on color, or for cubes that will support drilling along this path.
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Type 1 Changes
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Where cubes are automatically generated and maintained, the type 1 change can pose a unique challenge. It is often difficult or impossible to apply incremental updates to a cube that contains a type 1 attribute. This is exactly the same dilemma that is faced when trying to maintain an aggregate star that contains a type 1 attribute, as described in 15. Tools that automatically generate cubes will discard and rebuild them when there is a change to a type 1 attribute. The difficulty posed by the type 1 attribute was described in detail in 15. When a type 1 attribute changes, it may be necessary to restate some of the facts in the cube using
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16 Design and Business Intelligence 401
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the new value. Knowing how to do this requires access to the original detail, including before and after values. This information is generally lost once the source star is updated, so that it is subsequently impossible to update the cube accordingly. Unless cube updates are handled simultaneously with updates to the base data, the type 1 change will require the cube to be discarded and rebuilt. This can be an unwelcome and time-consuming process. For many designers, this is reason enough to avoid the type 1 change altogether, or at least bar type 1 attributes from appearing in cubes. While this resolves the technical challenge, it limits analytic flexibility. In many business situations, a type 1 change is the appropriate way to handle a change to source data. Such an approach will be impossible if type 1 attributes are barred from the design. The alternative is to accept the type 1 attribute as part of the design and to plan for complete rebuilds of necessary cubes each time data is refreshed.
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To get the most out of a dimensional design, it is essential to understand your business intelligence tools. Their capabilities may influence schema design or suggest the need to provide different levels of access for users with different skill sets. When working with SQL generators SQL-generating tools provide a semantic layer that maps a business view to a physical view. For new designs, don t plan to make use of features that are better handled in the model itself. Save those only for legacy models. Do make use of features like adding non-additive facts to the semantic layer and hiding surrogate keys. Multiple semantic layers are acceptable in providing different perspectives on a model, supporting alternative ways to link the same tables, or providing a separate and safe view for novice users and a flexible view for expert users. Some dimensional features pose problems for SQL generators Some SQL-generating tools cannot generate a drill-across query. Supplying a merged fact table provides a workaround for this limitation. SQL generators may be unable to create cross-browse queries when there are multiple fact tables; in this case, separate semantic layers may be required. Some tools may function better if snowflakes are used rather than conformed dimensions. If the tool is central to the architecture, this argues for a snowflake design, but fully explore the other impacts. Dimension browse queries can trip up SQL generators if they link a mini-dimension to the main dimension. Views may be of some limited use here, at the cost of complicating the semantic layer. It is often impossible to prevent a user from summing a semi-additive fact across its non-additive dimension. If novice users must access the data directly, it may be useful to build a derived schema that is limited to a single value of the dimension in question. Expert users can access the full schema.
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