Abstract
A category as well as a model is a mixture of graphical information and algebraic operations. Therefore, category language seems to be the most general to describe the models. It can provide us with the features that must characterize both the DSL language and the Modeling Method concept.
The theory of categories works with patterns or forms in which each of these forms describe different aspects of the real world. Category theory offers both, a language, and a lot of conceptual tools to efficiently handle models.
An important aspect of modeling is building complex functions from a given set of simple functions, using different operations on functions such as composition and repeat composition. Category theory is exactly the right algebra for such constructions.
The category theory creates the premises for the development of intelligent workflow modeling tools equipped with advanced analysis tools and automated learning mechanisms adapted to analyze and improve processes. The model allows on-line process information extraction, automatic learning from these data and self-improvement.
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Crăciunean, DC., Karagiannis, D. (2018). Categorical Modeling Method of Intelligent WorkFlow. In: Groza, A., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2018. Lecture Notes in Computer Science(), vol 11308. Springer, Cham. https://doi.org/10.1007/978-3-030-05918-7_11
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