Powercube Optimization Methods; Rollup Functions - IBM Cognos User Manual

PowerCube Optimization Methods

Several types of optimization methods are available for use in your models.

Rollup Functions

The rollup functions specify how measure values are evaluated in the reporting
components.
There are three rollup types:
v
v
v
Method
Auto-Partition
Categories
Data Passes
Direct Create
Regular Rollup
Measure values are summarized from lower to higher category levels. Cognos
Transformer applies these functions when the cube is created. The reporting
components apply them at run time.
Time State Rollup
Cognos Transformer represents the state of a measure at specific times.
For example, if a model tracks the number of active customers at the end of
each quarter, you can set up a time state measure to report the number of
customers active at a specific time. This is more useful than the quarterly sum of
the number of customers served during each month in a quarter.
Duplicates Rollup
Cognos Transformer evaluates duplicate records in the source data.
Description
Enables the Auto-Partition tab, where you can set the parameters
for Cognos Transformer to devise a partitioning scheme.
This is the default optimization setting.
Minimizes the number of categories in a cube. Cognos
Transformer adds only categories that are referenced in the
source data or specifically designated to be included. Categories
optimization requires an extra data pass for each cube to find the
categories required for that cube.
Optimizes the number of passes through the temporary working
files during the creation of a cube. Cognos Transformer assumes
that all categories are required in the resulting cube, and does
not pass through the source data to determine which categories
are required. Although included, unreferenced categories are not
visible in your reporting component.
Adds all categories in the model to the cube before the data
sources are processed. Records that do not generate new
categories are then directly updated to the cube.
This optimization method is best used with models that are
expected to generate few new categories, and where all
categories are expected to be added to the cube.
Note: This setting is not available for individual cubes in a cube
group.
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Appendix D. Reference