DB2 OLAP Server: theory and practices by IBM Redbooks

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It is self-evident to those experienced using multidimensional databases that most intersections will not contain data. This reflects the fact of the inherent sparseness of all business datasets (see Figure 11). Chapter 4. Tuning, good design practices, and useful tips 45 Dimension Members ================================== 172 21 27 Measures Time Mat Structure 32 Product 209 O rganizations 32765 M atrix Size = (172 * 21 * 27 * 32 * 209 * 32765) intersections 21,370,660,375,680 intersection points!

Modeling involves the creation of a prototype outline that models the dimensionality of the user requirements. This outline should be kept to as few dimensions and members as possible but must adequately reflect the dimensionality and the depth within the dimensions being communicated by the user. 34 DB2 OLAP Server - Theory and Practices Next the database will be populated with test data. Once test data is available, use a client access tool such as the spreadsheet add-in and work with the user to ensure that the model will provide the reporting and analysis information required.

Recall that relational storage systems were designed to store only data generated by the business. For example, the idea of storing the fact that no picnic baskets were sold in the Yukon in January is absurd to a relational database schema design. In a multidimensional structure, the intersection of “picnic baskets with the Yukon with January” exists by declaration. In the final analysis the DB2 OLAP storage structures were specifically designed to enable designers to deal effectively with this inherent sparseness, and the inherent sparseness of DB2 OLAP arrays is managed by tagging dimensions as Sparse or Dense.

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