Development of methodology for data and knowledge warehouse design in computer systems for intellectual data processing

Authors

DOI:

https://doi.org/10.15587/2312-8372.2018.123527

Keywords:

computer system, data and knowledge warehouse, categorical-ontological model

Abstract

At present, when developing data warehouses and knowledge of computer systems, it is not possible to use one end-to-end methodology, suitable for collective work and knowledge sharing. Such methodology should be understandable and objective from the point of view of the provability and validity of models at all stages of research and design work. The object of research is development of a methodology for designing efficient data warehouses and knowledge of modern computer systems for intellectual data processing. During the development of the methodology, the categorical-ontological approach developed by the author as a metalanguage of modeling and a means for verification of the design process and its results was used. In this case, the generic ontological model is mathematically rigorous, by imposing constraints on the objects and morphisms of category theory on the concepts and relationships that are presented. As a result of the development and application of this methodology, the semantic and linguistic barriers that arise between the members of the project team in the design of data and knowledge warehouses of computer systems have been overcome. Using a categorical-ontological approach to modeling and design makes it possible to formally substantiate the subjective results of knowledge engineering and use the objects of category theory in the form of design patterns at a high level of abstraction.

Author Biography

Pavlo Sahaida, Donetsk National Technical University, 2, Shybankova sq., Pokrovsk, Donetsk region, Ukraine, 85302

PhD, Associate Professor

Department of Electronic Engineering

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Published

2017-12-28

How to Cite

Sahaida, P. (2017). Development of methodology for data and knowledge warehouse design in computer systems for intellectual data processing. Technology Audit and Production Reserves, 1(2(39), 10–15. https://doi.org/10.15587/2312-8372.2018.123527

Issue

Vol. 1 No. 2(39) (2018): Information and Control Systems

Section

Information Technologies: Original Research

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Copyright (c) 2018 Pavlo Sahaida

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