Understanding Explicit Dimension Sets

You create an explicit dimension set to form a distinct group of an analytic model's dimensions. Explicit dimension sets improve the calculation efficiency of multidimensional data cubes. You can create one or more explicit dimension sets within an analytic model. Explicit dimension sets may contain completely different or some of the same dimensions as one another. The analytic calculation set instantiates valid combinations of members called explicit tuples from explicit dimension sets.

See Understanding Implicit Tuples and Explicit Tuples.

A model can contain explicit dimension supersets and explicit dimension subsets. An explicit dimension superset is a set of dimensions that contains the same dimensions as its subset; however, the superset contains one or more dimensions than its subset. A subset is the inverse of a superset: it contains the same dimensions as its superset; however, the subset contains one or more fewer dimensions than its superset. An analytic model can contain multiple supersets and subsets.

Explicit dimension sets are applied to individual data cubes when calculating data, and are also used for exporting data. To determine which explicit dimension set it uses on a data cube, the analytic calculation engine first reads which dimensions are attached to the data cube, and then analyzes and iterates through the explicit dimension sets in priority order. The set at the lowest numbered row in the Explicit Dimension Sets dialog box receives the highest priority.

See Editing or Adding New Explicit Dimension Sets.

Note:

Explicit dimension subsets must exist at a lower priority than their supersets. However, it is not necessary for an explicit dimension subset to exist in the row directly beneath its superset. For example, suppose that a superset exists in row 1. Its subset can be in row 3; it does not have to be in row 2. The explicit dimension set in row 2 can contain dimensions that are not included in other explicit dimension sets.

Using the priority order, the analytic calculation engine matches the first explicit dimension set that has either the same or fewer dimensions as are attached to the data cube, and then continues down the priority order for the remaining dimensions. The analytic calculation engine uses single dimensions if any of these dimensions remain unmatched after it has iterated through all explicit dimension sets.

For example, suppose these dimensions are attached to the OVERHEAD_COSTS data cube:

  • CHANNELS

  • CUSTOMERS

  • TAXES

  • EXPENSES

  • MONTHS

  • PRODUCTS

  • REGIONS

The analytic model contains these explicit dimension sets:

  • SET_1: MONTHS, REGIONS (first priority)

  • SET_2: TAXES, PRODUCTS (second priority)

  • SET_3: CHANNELS, CUSTOMERS, PRODUCTS (third priority)

  • SET_4: CHANNELS, CUSTOMERS (fourth priority)

  • SET_5: TAXES, EXPENSES (fifth priority)

The analytic calculation engine iterates through the explicit dimension sets, beginning with SET_1, and then continues down the priority order for the remaining dimensions, matching the following:

  • SET_1: MONTHS, REGIONS (first priority)

  • SET_2: TAXES, PRODUCTS (second priority)

  • SET_4: CHANNELS, CUSTOMERS (fourth priority)