Data Population for Intelligent Cubes

The Data population for Intelligent Cubes VLDB property allows you to define if and how Intelligent Cube data is normalized to save memory resources.

When an Intelligent Cube is published, the description information for the attributes (all data mapped to non-ID attribute forms) included on the Intelligent Cube is repeated for every row. For example, an Intelligent Cube includes the attributes Region and Store, with each region having one or more stores. Without performing normalization, the description information for the Region attribute would be repeated for every store. If the South region included five stores, then the information for South would be repeated five times.

You can avoid this duplication of data by normalizing the Intelligent Cube data. In this scenario, the South region description information would only be stored once even though the region contains five stores. While this saves memory resources, the act of normalization requires some processing time. This VLDB property provides the following options to determine if and how Intelligent Cube data is normalized:

Do not normalize Intelligent Cube data: Intelligent Cube data is not normalized. The memory resources required for the Intelligent Cube may be far greater than if one of the other normalization options is performed. This option is best suited for troubleshooting purposes only.

Normalize Intelligent Cube data in Intelligence Server (default): Intelligence Server performs the Intelligent Cube data normalization. This typically processes the normalization faster than the other normalization options, but also requires memory resources of Intelligence Server.
This is a good option if you publish your Intelligent Cubes at times when Intelligence Server use is low. Normalization can then be performed without affecting your user community. You can use schedules to support this strategy. For information on using schedules to publish Intelligent Cubes, see the In-memory Analytics Help .
The other options available for Intelligent Cube normalization all perform the normalization within the database. Therefore, these are all good options if Intelligent Cubes are published when Intelligence Server is in use by the user community, or any time when the memory resources of Intelligence Server must be conserved.
You can see improved performance with the database normalization techniques if the Intelligent Cube is retrieving a large ratio of repeating data. However, normalizing data within the database is typically slower than normalizing the data in Intelligence Server. Each database normalization technique is described below:
- Normalize Intelligent Cube data in database using Intermediate Table Type: This option is no longer available. If you upgraded a project from version 9.0.0 and this option was in use, this option is still used until you manually select a different option. Once you select a different option, you cannot revert to the behavior for this option.
  If you used this option in 9.0.0 and have upgraded to the most recent version of MicroStrategy, it is recommended that you use a different Intelligent Cube normalization technique. If the user account for the data warehouse has permissions to create tables, switch to the option Normalize Intelligent Cube data in the database. This option is described below. If the user account does not have permissions to create tables, switch to the option Normalize Intelligent Cube data in Intelligence Server.
- Normalize Intelligent Cube data in the database: This database normalization is a good option if attribute data and fact data are stored in the same table.
  To use this option, the user account for the database must have permissions to create tables.
- Normalize Intelligent Cube data in the database using relationship tables: This database normalization is a good option if attribute data and fact data are stored in separate tables.
  To use this option, the user account for the database must have permissions to create tables.
- Direct loading of dimensional data and filtered fact data: This database normalization is a good option if attribute data and fact data are stored in separate tables, and the Intelligent Cube includes the majority of the attribute elements for each attribute it uses.
  This is a resource-intensive option, and for very large Intelligent Cubes, enabling this setting may deplete your Intelligence Server's system memory.
  To use this option, the user account for the database must have permissions to create tables. Additionally, using this option can return different results than the other Intelligent Cube normalization techniques. For information on these differences, see Data Differences when Normalizing Intelligent Cube Data Using Direct Loading below.

Data Differences when Normalizing Intelligent Cube Data Using Direct Loading

The option Direct loading of dimensional data and filtered fact data can return different results than the other Intelligent Cube normalization techniques in certain scenarios. Some of these scenarios and the effect that they have on using direct loading for Intelligent Cube normalization are described below:

There are extra rows of data in fact tables that are not available in the attribute lookup table. In this case the VLDB property Preserve all final pass result elements (see Relating Column Data with SQL: Joins) determines how to process the data. The only difference between direct loading and the other normalization options is that the option Preserve all final result pass elements and the option Preserve all elements of final pass result table with respect to lookup table but not relationship table both preserve the extra rows by adding them to the lookup table.
There are extra rows of data in the attribute lookup tables that are not available in the fact tables. With direct loading, these extra rows are included. For other normalization techniques, the VLDB property Preserve all lookup table elements (see Relating Column Data with SQL: Joins) determines whether or not to include these rows.
The Intelligent Cube includes metrics that use OLAP functions. If an Intelligent Cube includes metrics that use OLAP functions, you should use an Intelligent Cube normalization technique other than the direct loading technique to ensure that the data returned is accurate.
OLAP functions are functions such as RunningSum, MovingAvg, and OLAPMax. For information about how to use OLAP functions, see the Functions Reference.

Levels at Which You Can Set This

Database instance, report, and template