Named Sets

Named sets return a data set based on a defined logic. They are primarily used to create data sets that are often requested from the cube.

There are two types of named sets – static named sets and dynamic named sets.

Note: Spotfire currently only supports named sets that return a certain single level in a user hierarchy or an attribute hierarchy (i.e., a 1-level hierarchy). That is, Spotfire can only handle results that can be seen as a single column.

Static named sets are evaluated once during creation – they are never re-evaluated. Dynamic named sets, on the other hand, are evaluated each time a query references it and is evaluated in a current context. For example, a dynamic set may show the Top 50 customers. This dynamic set could either be displayed over different countries in the world, or within a single country, when using standard cube reporting tools.

When you are working with dynamic named sets in Spotfire, the content of a dynamic named set column depends on the current context, that is, it depends on the current limitations and filtering. Therefore, the axis expression must be evaluated after the current filtering is applied, when dynamic sets are used in visualizations.

This is different from how axes traditionally are evaluated in Spotfire, but the 'evaluate after'-setting is applied automatically when you are placing a dynamic named set column on an axis. See Details on Advanced Settings help section in the Spotfire Analyst User Guide (available under Help > Help Topics) for more information about axis evaluation settings.

If you limit the selected data to male customers only, and use a set showing Top 50 customers, you will get the top 50 male customers. If limitations make the number of available male customers lower than 50, then this is the number you will end up with. All of these calculations are handled by the cube.

Note: If you are going to import data from a Microsoft SQL Server Analysis Services cube, and named sets are included in the selection, the sets will be used to limit the data retrieved on import and to calculate the measures. For example, if the selection of data to import contains data from Germany and France, and a Top 50 customers set is used in the import, the Top 50 customers from Germany and France are imported. Filtering out Germany from the imported data may result in less than 50 values, because the set will now show only those customers that were imported from France. The same selection of data when working in-database will add the set as a dimension and the filtering will let you see the Top 50 values from France.