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Proposals from QueriesLocate this document in the navigation structure

Use

By choosing Proposals → Proposals from Queries, you can select existing BEx queries for the selected InfoCubes and view proposals for a minimum and maximum aggregate for each InfoCube. The name of the proposed aggregate is derived from 'MIN' or 'MAX' and a sequential number.

Note

We recommend that you use this function the first time you optimize the InfoCube. If you have already executed queries, use the other options for optimizing, because the number of times a query has been executed and the individual navigational steps are also taken into account.

The minimal aggregate 'MIN' corresponds to the smallest aggregate possible. This only contains the data that is needed for the initial drilldown on a query.

Note

The query has to be set to hierarchical reading or reading on demand.

Activating a minimal aggregate accelerates the drilldown defined in the BEx Analyzer.

The maximum aggregate 'MAX' represents an aggregate that supports every navigational step of a query. This is based on the assumption that you want to perform a drilldown for the query using all characteristics (all free characteristics too). Generally this is rarely the case. More frequently, the aim is to be able to use an aggregate of this type in all possible navigation steps of the query. In any case, check whether it makes sense to use the maximum aggregate. This is not the case if the volume of data for an aggregate of this type corresponds to the size of the InfoCube.

If the components (the characteristics and attributes of a newly defined aggregate that are not summarized) are identical to an aggregate that has already been proposed, they will not be added to the list. Instead, the number of calls is increased by one.

Generally the number of calls an aggregate has, the more useful it is: The higher the number of calls for an aggregate, the higher the number of queries in which it can be used.

You can modify or delete the proposed aggregates.

See also:

Proposals from BW Statistics

Optimizing Proposed Aggregates.