STUDY OF THE PRINCIPLES OF BUILDING DATABASES OF HIGH-TECH MACHINE-BUILDING PRODUCTS BASED ON THE BASIC NATO CALS DATABASE MODEL
DOI:
https://doi.org/10.31649/1999-9941-2021-52-3-36-43Keywords:
CALS-concept, NATO CALS data model, database, ER-diagram, modeling language, PostgreSQL, semantic breakdownAbstract
The need for the development of high-tech engineering in Ukraine requires the introduction of information technology at all stages of the life cycle of engineering products. This requires the implementation of the concept of Continuous Acquisition and Life-cycle Support (CALS), which has long been used in technologically advanced countries for the above purposes. Collection and processing of data generated by a sample of high-tech engineering products will be useful to reduce operating costs, increase service life, increase the speed of repair work, optimize according to various criteria of modernization of high-tech engineering products, promote the development of new similar samples of engineering products.
The aim of the article is to determine the scientific basis for using the basic NATO CALS data model to build databases of samples of high-tech engineering products, including samples of weapons and military equipment. This goal is achieved by comparing the recommendations of NATO guidelines and Ukrainian opportunities to build information systems by machine-building enterprises and operators of their products. It is shown that the main theoretical approaches for building models of these samples of high-tech engineering products, given in the NATO guidelines on CALS-technologies and systems, are relevant. However, more modern and accessible tools are needed to build data models and database models, and the principles of semantic product breakdown and identification of entities, links and attributes can be taken from NATO guidelines almost without additions. As prospects for further research, it is proposed to conduct a comparative analysis of notations, modeling languages and software products that implement them for modeling data of high-tech engineering products.
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