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Part V
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which are measurements over time that compare actual business results to predefined goals, and includes several data mining algorithms for more advanced statistical analysis. A shared OLAP solution must support access to data by many users in two important ways. First, sensitive data must be secured adequately. Analysis Services not only includes security to control access to specific databases but also supports securing data at more granular level, such as limiting access to selected dimensions, sections of a dimension, or even particular cells in a cube. Second, users must be able to write data back to the cube. Analysis Services provides support for write-back, often a requirement for budgeting solutions, but you ll need to use a client application that also supports write-back to take advantage of this feature. Multidimensionality is the one rule that must absolutely be met for a product to be accepted as OLAP compliant. Where a relational database is a two-dimensional representation of data stored in tables containing rows and columns, an OLAP database stores data in a multidimensional structure called a cube. Unlike the geometrical cube which is limited to three dimensions, an OLAP cube can have n dimensions. In practice, a cube requires at least one dimension but usually has seven to twelve dimensions. This range is considered only a design guideline to avoid confusing users with too much information. A dimension is a set of logically related labels that give context to the values you analyze. Often, you can recognize a dimension when you see or hear the word by used in the description of a request for information. For example, when I want to get sales by year by product, I would create a query with two dimensions Time and Product. (Technically, Sales is in a third dimension for measures, but that s a special type of dimension that is designed differently.) Dimensions have members, which represent the labels that are presented during analysis. For example, the result of the query to get sales by year by product might return 2003, 2004, and 2005, each of which is a member of the Time dimension. Likewise, Bikes, Accessories, and Clothing are members of the Product dimension. Whereas a dimension comes from one or more related tables, members come from the same column in the dimension table, which is defined as an attribute in Analysis Services. Traditionally, OLAP products organize members of a dimension into a hierarchy that contains multiple levels. In the Time dimension, days roll up to months, months roll up to quarters, and quarters roll up to years. Days, Months, Quarters, and Years are levels of the hierarchy which each contain its own set of members. In Analysis Services 2000, you build dimensions by mapping dimension columns to hierarchy levels. Analysis Services breaks with tradition by mapping dimension columns to attributes that can then be organized into a user hierarchy. A user hierarchy might be a natural grouping of attributes, like Day, Month, Quarter, and Year, in which an attribute has a many-to-one relationship with one other attribute in the hierarchy. Or a user hierarchy
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22
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could be a grouping that users find helpful for analysis, regardless of the cardinality of the relationship between attributes. For example, if a user hierarchy includes Color and Size attributes in the Product dimension, the size L could be related both to Blue and to Red, although of course the values associated with Blue L products could be different from Red L products. Lastly, the Information rule in FASMI refers to the volume of data that an OLAP product can support. Of course, an OLAP product s ability to manage large data volumes is dependent to a significant degree on available hardware resources, such as RAM and disk space. Analysis Services has been architected specifically for scalability. You can select the physical storage mode appropriate for the data volumes you must support and create partitions to locate data on separate drives or even separate servers as well as to minimize the time required to add new data. Once you have built an Analysis Services database and made it available for users to query, you need to implement processes for keeping cube data up-to-date with minimal downtime. Analysis Services provides several options for managing updates, such as incremental processing to load only the newest data and limit the time required to recalculate aggregations. Analysis Services can even be configured to respond to queries while data is being updated.
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