Multi-Dimensionality In VisiCube
Multi-Dimensionality
Multi-dimensional data modeling is popular today and for good reason.
It is a very powerful and flexible way to model actual phenomena in the real world.
Any measurement (of a phenomenon) can be classified (or identified) through
myriad attributes (who, what, where, when, how, etc). Each of these attributes
are used as individual dimensions in this model. And the number of such
attributes is unlimited.
Hypercubes
The term hypercube is utilized to communicate the nature of this model.
A hypercube is a cube-like logical model in which all measurements are
organized into a multidimensional space.
|
Record
# |
Year
dimension |
Sex
dimension |
State
dimension |
Height
measure |
Weight
measure |
| 1 | 2004 | Male | Idaho | 74 | 182 |
| 2 | 2004 | Female | Ohio | 62 | 128 |
| 3 | 2003 | Male | Ohio | 70 | 174 |
| 4 | 2004 | Female | Idaho | 65 | 159 |
| 5 | 2004 | Male | Idaho | 71 | 147 |
| 6 | 2003 | Male | Idaho | 74 | 203 |
|
The "Cube" in VisiCube
In building your data universe (the VisiCube project in which you will explore that data),
your measurements are captured in a hypercube which supports analysis through any
number of independent dimensions of that data.
However, VisiCube does this with a minimum of imposition, doing its best
to buffer you from the complexities of that model.
Specifically, the hypercube data model is built for you automatically
(though you can override its structure) upon creation of a project
and presented to you in a straight-forward, intuitive manner thereafter.
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