What happens if the measure is unrelated to the dimensions used in query? Typically, Analysis Services will show the default measure, and the value will be the same for all members. Using the Reseller Sales example, if each row storing a sales amount also stores a pointer to a product table, a date table, or a sales territory table, then queries that include members from those dimension will resolve correctly. Measures produce valid results when the fact table that contains the numeric source data also contains pointers to dimension tables that are used in the query. In this example Reseller Sales aggregates to various levels along the Sales Territory hierarchy. Whether the query specifies individual products, rolls up to a category, or is sliced by time or geography, the measure should produce an operation that is valid for the dimensions included in the query. For example, a measure that calculates Reseller Sales will be backed by a Sum operator, and it will add the sales amounts for each dimension member included in the query. Measures are context-sensitive, operating on numeric data in a context that is determined by whichever dimension members happen to be included in the query. Alternatively, you can also define a measure using MDX. Structurally, a measure is often mapped to a source column in a fact table, with the column providing the values used to load the measure. Measures represent some aspect of organizational activity, expressed in monetary terms (such as revenue, margins, or costs) or as counts (inventory levels, number of employees, customers, or orders), or as a more complex calculation that incorporates business logic.Įvery cube must have at least one measure, but most have many, sometimes numbering in the hundreds.
Repurpose an existing measure group in other cubes in the same database or in different Analysis Services databases.Ī measure represents a column that contains quantifiable data, usually numeric, that can be aggregated. To define semiadditive behavior, use the Add Business Intelligence Wizard. For example, you would not want to add balances from the same account over consecutive days. You might want to aggregate balances by customer and region, but not time. A common example is a bank account balance. Semiadditive behavior refers to aggregations that are valid for some dimensions but not others. Understand the aggregation methods that can be assigned to a measure. Partition configuration is partly determined by the properties you set on measure group objects. Properties on a measure group allow you to specify caching behaviors, storage, and processing directives that operate collectively at the measure group level. In a multidimensional model, a measure group equates to a fact table in the source data warehouse. If you used the Cube Wizard to start your cube, you might need to change the aggregation method, apply a data format, set the visibility of the measure in client applications, or possibly add a measure expression to manipulate the data before values are aggregated. LinkĬreate Measures and Measure Groups in Multidimensional ModelsĬhoose from one of several approaches for creating measures and measure groups. It also contains the following table, with links to procedural steps for creating and configuring measures and measure groups. This topic describes Measures and Measure Groups. A cube cannot exist without at least one of each. Both measures and measure groups are an essential component of a cube.
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A cube includes measures in measure groups, business logic, plus a collection of dimensions that give context for evaluating the numerical data that a measure provides.
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