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August 28, 2008
Bad dimension choice complicates measures and trend analysis
This is oft repeated question - why are 100 dimension star schema / cubes bad. I'll give you a simple mathematical example of how bad dimension choice complicates interpretation of measures and trend analysis.
Consider the natural coordinate system we are all used to - x and y. Distance from origin to a point (x,y) is sqrt(x**2 y**2). It is perfectly symetrical and simple.
Now consider an artificial non orthogonal coordinate system with an additional dimension defined by a=X**2*y, b = x*y**2, c = x*y. The distance of a point to origin (((a b)/c)**2)- 2c. This is not symetrical in a,b and c and difficult to interpret. of course as you add more dimensions its harder.
A special coordinate system like (r,theta,phi) is symmetrical and simpler for distance for origing - but its difficult in general case - e.g. define a cube.
The availability and ability of a tool to support 100 non orthogonal dimensions, and the skill of the cubeDBA to optimize it may get you results not much slower than with a simplest cube, but the meaning of measures, and interpretation of trends become much much slower in such a system, leading to simple reports requiring mdx programs being run with cube used as filesystem.
Posted by Vijay at August 28, 2008 5:15 AM
