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5. Close the Explore Data window, and then open the SSAS cube. 6. On the Cube Structure tab right-click anywhere in the Measures pane, click New Measure, select Units Forecast in the Source Table drop-down list, select Sales Units in the Source Column list, and then click OK. Because the new measure group is not yet associated with any dimensions, a red wavy line appears beneath the measure group name to indicate an error exists. You ll fix this error later in this procedure by defining dimension relationships on the Dimension Usage tab. 7. Right-click anywhere in the Dimensions pane, click Add Cube Dimension, select Scenario, and then click OK. 8. Click the Dimension Usage tab, scroll horizontally to the right to locate the Units Forecast measure group, and then click the intersection of Units Forecast and Product, and then click the ellipsis button. 9. Select Regular in the Select Relationship Type drop-down list, select Category in the Granularity Attribute drop-down list, select CategoryKey in the MeasureGroupColumns drop-down list, and then click OK. You ll forecast sales units by product category and by quarter. Product categories are found in the Product dimension, but they re not at the lowest level of the dimension. Likewise, calendar quarters are found in the Time.Calendar dimension, but, again, they re not at the lowest level of the dimension. When you define the relationship between a dimension and a measure group, you can choose any attribute in the dimension as long as a corresponding key column is available in the measure group s fact table. 10. Click the intersection of Units Forecast and Time, and then click the ellipsis button. 11. Select Regular in the Select Relationship Type drop-down list, select Fiscal Quarter in the Granularity Attribute drop-down list, select FiscalQuarter in the MeasureGroupColumns drop-down list, and then click OK. 12. Click the intersection of Units Forecast and Scenario, and then click the ellipsis button. 13. Select Regular in the Select Relationship Type drop-down list, select Scenario in the Granularity Attribute drop-down list, select ScenarioKey in the MeasureGroupColumns drop-down list, and then click OK.
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With one measure and all relationships to pertinent dimensions defined, you should now have all you need to save and process the cube. If you were to try, however, you would see a message informing you that key attributes in the fact table cannot be found in the related dimension tables. Before processing a cube from a fact table with no data, you must manually reconfigure the error processing for the corresponding measure group in the cube. 14. Click the Cube Structure tab, click Units Forecast in the Measures pane, and then, in the Properties window, select (custom) in the ErrorConfiguration property s drop-down list. 15. Click the plus sign to expand the ErrorConfiguration property, and then select IgnoreError in the KeyNotFound property s drop-down list. Your screen looks like this:
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16. Deploy the project, click the Browser tab, click Reconnect, click anywhere in the grid, and then click the Clear Results button in the toolbar. 17. Expand the Units Forecast measure group, drag the Sales Units measure into the center of the grid, expand the Scenario dimension, and then drag the Scenario attribute to the rows axis.
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Your screen looks like this:
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Not surprisingly, the browser is empty. The important points are that the measure group exists and that it has placeholders where you can add forecast values.
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Writing Values Back to a Cube
In Analysis Services, you can write values only to the lowest level of a cube. In other words, you cannot change the value of an aggregation. If you could change the value of an aggregation, you would be able to make the cube internally inconsistent. Technically, Analysis Services writes incremental change values to a special writeback table in a relational database. Analysis Services then dynamically combines the writeback values with any values from the fact table. Note
For application developers, Analysis Services includes a tool the Update Cube MDX statement that will allocate a given high-level input value to create the necessary lowest-level values. A client application can thus appear to write back at a high level. In reality, however, the values being written back to the cube are always at the lowest level of the cube.
Part III:
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