The number of dimensions in the data source and data target may vary. The following example illustrates a case where there are more dimensions in the data source outline than in the data target outline:
Source Target Product Product Cola Cola Market Market East East Year 1999 1998 1997
You can map member 1997 of the Year dimension to Void in the data target. First, define the areas of the data source to share with the data target:
Source Target @DESCENDANTS(Market), 1997 @DESCENDANTS(Market)
Then, map the data source member to Void in the data target:
Source Target 1997 Void
“Void” is displayed automatically; manually entering “Void” may cause errors.
If you do not include at least one member from the extra dimension in the area definition, you will receive an error message when you attempt to validate the partition.
When you map a member from an extra dimension, the partition results reflect data only for the mapped member. In the above example, the Year dimension contains three members: 1999, 1998, and 1997. If you map member 1997 from the data source to the data target, the partition results reflect Product and Market data only for 1997. Product and Market data for 1998 and 1999 will not be extracted. |
The following example illustrates a case where the data target includes more dimensions than the data source:
Source Target Product Product Cola Cola Market East Year Year 1997 1997
In such cases, first define the shared areas of the data source and the data target:
Source Target @IDESCENDANTS(Product) @IDESCENDANTS(Product), East
You can then map member East from the Market dimension of the data target to Void in the data source:
Source Target Void East
If member East from the Market dimension in the data target is not included in the target areas definition, you will receive an error message when you attempt to validate the partition.