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RAPID-ML promotes the concept of realization modeling, whereby canonical Data Structures can either be fully embedded (accessed via a single Collection Resource or Object Resource) or linked (factored out across multiple resources), enabling clients to get data in an economic, or lazy, manner.

Realization modeling also enables Property Sets to be defined for purposes of filtering out unwanted properties from the canonical types, for layering on Cardinality overrides and other Constraints that make sense in the application or service context.  RAPID-ML realization modeling overcomes traditional, one-size-fits-no-one arguments against canonical data usage and enables emergent data type standardization across APIs, making these more interoperable, easier to learn and to integrate.

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