Abstract
In today’s organizations nowadays exists big gap between business and information technologies. Information technology strategy planning is multistage process and information system development relies on implementation of each stage of IS lifecycle. A knowledge-based IS engineering proposed system modelling and decision-making tools and methods, which helps to expand more precise and comprehensive subject area corresponding to the project. Participants of IS project such as developer or programmer is allowed to use not only the knowledge of the project, which is collected in traditional CASE tool storage, but also the knowledge storage, where subject area knowledge is collected according to formal criteria. There have been made many efforts for the analysis of Unified Modelling Language (UML) models generation of diverse knowledge-based models combining frameworks, workflow patterns, modelling languages and natural language specifications. Knowledge-based subsystem as CASE tool component with Enterprise Meta-Model (EMM) and Enterprise Model (EM) within can significantly help with UML dynamic models generation using transformation algorithms. Application of the knowledge-based transformation algorithms grants the possibility to operate additional models validation methods that EMM determines. The main purpose of the paper is to present how EM can be used in UML models generation process. This paper combines results from previous researches and summarizes part of them. There is described importance of knowledge-based IS engineering in business and IT alignment process. There is also described UML models elements roles variations after generation from EM. The most valuable result of this research is presentation of UML Sequence model generation from EM process, by defining transformation algorithm and illustrating it by a particular example what proves EM sufficiency for whole generation process.
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Veitaite, I., Lopata, A. (2020). Knowledge-Based Transformation Algorithms of UML Dynamic Models Generation from Enterprise Model. In: Dzemyda, G., Bernatavičienė, J., Kacprzyk, J. (eds) Data Science: New Issues, Challenges and Applications. Studies in Computational Intelligence, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-39250-5_3
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