Ways of representing Models may make assumptions on the data they process (see examples below).
Any assumption should be made explicit: if the data coming from a data source
does not meet those assumption, an adaptation/normalization layer should be used
(note: this is different from, and complementary to, the projection/abstraction layer
discussed in another pattern)
Example: Iso-semantic ways of representing ‘quantitative’ data:
Stand alone value with units, with unit conversions
Relative to a reference value or range (above normal; low-normal-high)
Z-score (incorporating mean and SD)
Multiple of mean (MOM)
Multiple of upper limit of normal (ULN)