Step 2 - Define Datasets

The next step introduces the Datasets, multi-dimensional structures containing a supported summary measure. Data marts typically contain many datasets.

Sometimes called hypercubes, datasets are the basic build blocks of the multi-dimensional data model. They are measures defined on the cross product of a dimension collection. To define a dataset, select a subset of dimensions and a measure to store in each dataset cell.

This screen shows a dataset being defined by eight dimensions. Each dimension has number of editable attributes controlling how it will appear to the user.

Dataset measures can be record counts, summarized field values, or summarized computed values - virtual measures.

This screen shows the specification of a virtual measure. Calculations can combine any field in the dataset source table with standard arithmetic operators, built in functions, constants, and custom operators.

Dataset measures must be natively summarized over the dimensions, such as record count, or be supported internally by Highlander. Ratio measures for example are supported if their numerator and denominator are supported. This opens most ratio analysis to Highlander.