A semantic-driven model harvesting technology designed to improve the understanding and quality of large complex models.
Motivation
Models are abstractions of entities or processes. They are created in order to bring into focus some relevant aspects of those entities or processes that are currently being analyzed. Models aim at eliminating the irrelevant data (noise) and enlighten what is informational in respect to the current goal.
With the increase of models complexity, models themselves can be a subject to examination and analysis. The purpose of model examination may vary between different users depending on their viewpoint and intended use. In addition users prefer to study models in an intuitive manner that fits their semantic perception.
In order to enable MDD it is essential to support incremental migration from legacy code to models with different levels of structural and behavioral abstractions. Our IBM System Grokking Technology technology can be used to assist in this process by helping users to discover higher levels of abstractions.
