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Not only does use of an Analysis Services cube ensure everyone uses the same calculation definition every time, Analysis Services is specifically designed for performing sophisticated analytical calculations on large volumes of data, such as time series analysis, currency conversions, and budget allocations, to name a few Analysis Services also supports key performance indicators (KPIs),.

Take a moment to review the code in this class, and then save the file. Notice the Read() method that is called by Reporting Services to retrieve the values from the data source. Like the data reader constructor code, the code in this method is implementation-specific.

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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.

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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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