The search and extraction of targeted information about promising and breakthrough technologies for ensuring chemical safety is an important element in the analysis of large volumes of unstructured scientific and technical data. Existing approaches to processing large amounts of unstructured data can lead to distortion of the original information. New approaches to the search and extraction of target information based on the typification of the display of visualized large volumes of data of scientific and technical programs are proposed. It is proposed to overcome the disadvantages of existing approaches by using the representation of multi-attribute objects based on the multiset formalism, which allows one to simultaneously take into account all combinations of attribute values, as well as a different number of values for each of them. Multi-feature objects presented as multisets are proposed to be divided into relevant and irrelevant in terms of similarity to the reference multiset based on various metrics. This approach makes it possible to level the features of the initial data and opens up opportunities for solving new problems of studying large volumes of unstructured information of various nature. The results of the computational experiments in the chemical engineering field have shown the effectiveness of the proposed methodological approaches to the search and extraction of target information from large volumes of unstructured data of scientific and technical programs.
Human capital reproduction is formed under the influence of environmental, social, economic and regional factors. There is a need for scientific and methodological approaches to the comprehensive analysis of the state of the ecosystem of human capital reproduction in order to form an effective model of sustainable development. The system of criteria for sustainable development in the conditions of digitalization based on socioeconomic and environmental factors was formed. Algorithmic support for the system of comprehensive assessment of the state of the ecosystem of human capital reproduction, taking into account the effects of global identification of negative externalities in the management of natural resources, has been developed.
The managerial decisions making tasks in human capital reproduction complex systems are solved on the basis of models built on experimental data. It is problematic to take into account all the factors affecting the human capital reproduction. Existing approaches are not focused on building models for the human capital reproduction with incomplete information. Algorithms for inductive modeling are developed for the human capital reproduction systems characteristics functional description. The software is developed to implement the proposed algorithms for the human capital reproduction intellectual analysis based on the metric spaces of multisets.
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