So our data mart example can be used to construct a cube with three independent,
hierarchical dimensions (Location, Time, and Item) and one dependent dimension for
UnitsSold and Revenue measures. The neat thing about the cube structure is that it
enables aggregate values for the measures to be efficiently stored and accessed for all
levels of the hierarchies. When analyzing a cube, you can choose to consolidate or
rollup these aggregates (e.g., roll up sales figures for cities to regional sales figures).
You can also do the opposite, drilling down to a finer level of granularity. Moreover
you can slice and dice the cube whichever way you like by taking a subcube of it (e.g.,
if you restrict the item category in Figure 16.4 to software, you get the front slice of
the cube)