1 March 11, 2024
Articles
1. Boris Sh. Gurgov
A Decision Support Information System
European Journal of Technology and Design. 2024. 12(1): 3-9.
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2. Nikita S. KurdyukovEuropean Journal of Technology and Design. 2024. 12(1): 3-9.
Abstract:
The article explores a decision support information system. The information system is intended for the management of educational institutions within the Ministry of Education and Science of Russia. The information system is interpreted as a decision support system. The taxonomy of information systems in the field of education is given. The types of management technologies in the field of education are described. The difference between the main goals of commercial and public universities is shown. Management goals set the dynamics of management. A sectoral area has been identified, which is the prerogative of the Ministry of Education. The article describes one of the industry information systems created as part of the assignment of the Ministry of Education. It is designed as a specialized decision support information system with a special interface for ministry employees. The basis for the operation of such a system and its interface is information modeling. The need for additional use of GIS for the operation of such a system is shown. The use of GIS is due to the fact that universities form a geographically distributed system, for the management of which it is necessary to use spatial information. Spatial information is necessary when managing industrial property. The principles of operation of the system are described. The information system diagram is described. The application of information modeling for the operation of the system is shown, using the example of the “property map” geoinformation model. The results of the work are put into practice.
The article explores a decision support information system. The information system is intended for the management of educational institutions within the Ministry of Education and Science of Russia. The information system is interpreted as a decision support system. The taxonomy of information systems in the field of education is given. The types of management technologies in the field of education are described. The difference between the main goals of commercial and public universities is shown. Management goals set the dynamics of management. A sectoral area has been identified, which is the prerogative of the Ministry of Education. The article describes one of the industry information systems created as part of the assignment of the Ministry of Education. It is designed as a specialized decision support information system with a special interface for ministry employees. The basis for the operation of such a system and its interface is information modeling. The need for additional use of GIS for the operation of such a system is shown. The use of GIS is due to the fact that universities form a geographically distributed system, for the management of which it is necessary to use spatial information. Spatial information is necessary when managing industrial property. The principles of operation of the system are described. The information system diagram is described. The application of information modeling for the operation of the system is shown, using the example of the “property map” geoinformation model. The results of the work are put into practice.
Number of views: 12 Download in PDF
Ontological Models of Information Retrieval
European Journal of Technology and Design. 2024. 12(1): 10-15.
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3. Vladimir V. TimofeevEuropean Journal of Technology and Design. 2024. 12(1): 10-15.
Abstract:
The article explores ontological models. A special type of models is considered. related to information retrieval. The ontological model of information retrieval is a specific model. The information design model is the closest model to an ontological model from a number of information models. The ontological model of information retrieval is generalized and allows for information uncertainty. The connection between the semantic model and the ontological model is shown. The semantic model of information retrieval complements the ontological model. Semantic proximity is a mandatory component of the ontological model. The article describes three methods for forming ontological models in information retrieval: indicator method, probabilistic method, fuzzy method. It is shown that logical weight is only an indicator and a qualitative characteristic, while probabilistic weight is a quantitative indicator. The article introduces several types of weighting coefficients for ontological models in information retrieval. The article introduces the definition of an ontological model of information retrieval. Three key indicators of the ontological model are described. It is shown that the user's information needs in many cases are unclear, uncertain and depend on the individual characteristics of the user. The article introduces the concept of ontological proximity. The article shows the difference between contextual metadata and contextual metamodels. The article introduces the concept of direct and contextual information resource in information retrieval. The difference between these resources is shown. The contextual information resource is associated with the ontological model. The types of relationships for direct and contextual search results are shown. The principles of forming a semantic proximity graph, which is used in ontological models, are described.
The article explores ontological models. A special type of models is considered. related to information retrieval. The ontological model of information retrieval is a specific model. The information design model is the closest model to an ontological model from a number of information models. The ontological model of information retrieval is generalized and allows for information uncertainty. The connection between the semantic model and the ontological model is shown. The semantic model of information retrieval complements the ontological model. Semantic proximity is a mandatory component of the ontological model. The article describes three methods for forming ontological models in information retrieval: indicator method, probabilistic method, fuzzy method. It is shown that logical weight is only an indicator and a qualitative characteristic, while probabilistic weight is a quantitative indicator. The article introduces several types of weighting coefficients for ontological models in information retrieval. The article introduces the definition of an ontological model of information retrieval. Three key indicators of the ontological model are described. It is shown that the user's information needs in many cases are unclear, uncertain and depend on the individual characteristics of the user. The article introduces the concept of ontological proximity. The article shows the difference between contextual metadata and contextual metamodels. The article introduces the concept of direct and contextual information resource in information retrieval. The difference between these resources is shown. The contextual information resource is associated with the ontological model. The types of relationships for direct and contextual search results are shown. The principles of forming a semantic proximity graph, which is used in ontological models, are described.
Number of views: 14 Download in PDF
Dichotomous Analysis
European Journal of Technology and Design. 2024. 12(1): 16-22.
Number of views: 17 Download in PDF
4. Dmitry I. TkachenkoEuropean Journal of Technology and Design. 2024. 12(1): 16-22.
Abstract:
The article explores little-studied dichotomous analysis. Dichotomous analysis is used in practice to solve many problems. However, to date there has been little research into the theory of dichotomous analysis as a special type of analysis. Dichotomous analysis includes three stages: decomposition of reality, composition of models and study of the resulting models. Decomposition is implemented using dichotomous division. Three types of dichotomous division are described, which produce three types of division results. Dichotomous analysis has different implementations. Dichotomous analysis is divided into: oppositional, aggregative, elemental. Elemental dichotomous analysis is performed using onomasiological division. Onomasiological division allows us to obtain information units or elements of the system under study. The article explores three types of dichotomous decomposition: decomposition to the selection of only parts or elements; decomposition to the selection of parts and constructive connections between them, decomposition to the selection of parts and causal connections between them. The content of the levels of dichotomous division is revealed. A formalization of the dichotomous composition is given. The relationships between the objects of decomposition in dichotomous analysis are described. A structural diagram of dichotomous decomposition is presented. Dichotomous decomposition does not apply to all objects, but only to those that have the property of separation. The dichotomy can be interpreted as a property and as a method. To describe multi-level decomposition, we use the apparatus of tensor algebra. In dichotomous decomposition and composition, paradigmatic and syntagmatic relations are used. The article describes the mechanism for searching for connections in dichotomous decomposition. The Bradford Hill model was used for this purpose. This model is transferred from the field of medicine to the field of information field.
The article explores little-studied dichotomous analysis. Dichotomous analysis is used in practice to solve many problems. However, to date there has been little research into the theory of dichotomous analysis as a special type of analysis. Dichotomous analysis includes three stages: decomposition of reality, composition of models and study of the resulting models. Decomposition is implemented using dichotomous division. Three types of dichotomous division are described, which produce three types of division results. Dichotomous analysis has different implementations. Dichotomous analysis is divided into: oppositional, aggregative, elemental. Elemental dichotomous analysis is performed using onomasiological division. Onomasiological division allows us to obtain information units or elements of the system under study. The article explores three types of dichotomous decomposition: decomposition to the selection of only parts or elements; decomposition to the selection of parts and constructive connections between them, decomposition to the selection of parts and causal connections between them. The content of the levels of dichotomous division is revealed. A formalization of the dichotomous composition is given. The relationships between the objects of decomposition in dichotomous analysis are described. A structural diagram of dichotomous decomposition is presented. Dichotomous decomposition does not apply to all objects, but only to those that have the property of separation. The dichotomy can be interpreted as a property and as a method. To describe multi-level decomposition, we use the apparatus of tensor algebra. In dichotomous decomposition and composition, paradigmatic and syntagmatic relations are used. The article describes the mechanism for searching for connections in dichotomous decomposition. The Bradford Hill model was used for this purpose. This model is transferred from the field of medicine to the field of information field.
Number of views: 17 Download in PDF
Information Structural Modeling
European Journal of Technology and Design. 2024. 12(1): 23-29.
Number of views: 12 Download in PDF
5. Asya I. TodorovaEuropean Journal of Technology and Design. 2024. 12(1): 23-29.
Abstract:
The article explores information structural modeling. Information structural modeling includes two types. The first type of structural modeling is used when studying the surrounding world. The second type of structural modeling is used when constructing new structures and modifying known structures. Information structural modeling is based on the features of data used in computer science. Structural modeling is figurative modeling. It has two features. The first feature of the modeling is that all types of data are treated as areal data. The second feature of structural modeling is that models have a dual formal and graphic or visual form. Structural modeling uses a set-theoretic and systems approach. Structural modeling is applied to data, technologies and systems. These types of structures are different and require different modeling techniques. Many objects have a hierarchical structure. This is due to the hierarchy of the surrounding world and the nesting of objects. Information structural modeling is a complex type of modeling. It is much more complex than formal modeling or symbolic modeling. Information structural modeling has two forms: figurative and formal. When constructing a figurative or visual form, it is necessary to solve the problem of the information content of the image. There is always complete information correspondence between the formal structural model and the modeling object. Between the figurative structural model and the modeling object there is either a complete information correspondence or a partial information correspondence. The article shows an example of reducing a complex set to a hierarchical structure. This example shows that structural modeling reduces the complexity of systems and configurations.
The article explores information structural modeling. Information structural modeling includes two types. The first type of structural modeling is used when studying the surrounding world. The second type of structural modeling is used when constructing new structures and modifying known structures. Information structural modeling is based on the features of data used in computer science. Structural modeling is figurative modeling. It has two features. The first feature of the modeling is that all types of data are treated as areal data. The second feature of structural modeling is that models have a dual formal and graphic or visual form. Structural modeling uses a set-theoretic and systems approach. Structural modeling is applied to data, technologies and systems. These types of structures are different and require different modeling techniques. Many objects have a hierarchical structure. This is due to the hierarchy of the surrounding world and the nesting of objects. Information structural modeling is a complex type of modeling. It is much more complex than formal modeling or symbolic modeling. Information structural modeling has two forms: figurative and formal. When constructing a figurative or visual form, it is necessary to solve the problem of the information content of the image. There is always complete information correspondence between the formal structural model and the modeling object. Between the figurative structural model and the modeling object there is either a complete information correspondence or a partial information correspondence. The article shows an example of reducing a complex set to a hierarchical structure. This example shows that structural modeling reduces the complexity of systems and configurations.
Number of views: 12 Download in PDF
Symbolic and Figurative Information Units
European Journal of Technology and Design. 2024. 12(1): 30-35.
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6. Viktor Ya. TsvetkovEuropean Journal of Technology and Design. 2024. 12(1): 30-35.
Abstract:
The article examines two important classes of information units. The multidimensionality of the use of different information units is shown. The article explores symbolic information and figurative information units. The similarities and differences in these groups are shown. The common feature is the systematization of information units into three types: symbols (elements), words and sentences. The divisibility criterion of the original information set determines the type of information unit. The difference between structural and semantic information units is shown. An onomasiological method for obtaining information units in the information field is described. A semasiological method for constructing models using information units in the information field is described. Information units are used in two directions: to detail the description of natural phenomena; for modeling or design. The difference between procedural and object information units is shown. An analysis of information units belonging to different areas is given: computer linguistics, computer language, complex system, information field. A set-theoretic description of information units of different directions is given. There is structural similarity between symbolic information units. It lies in the fact that information units of different types are divided into symbols, words and sentences. The article proves that information units can be considered as the result of analysis. The typological similarity between symbolic information units in linguistics, programming and systems analysis is shown. Figurative information units are more informative compared to symbolic information units. The qualitative difference between the figurative information units pixel and voxel is shown.
The article examines two important classes of information units. The multidimensionality of the use of different information units is shown. The article explores symbolic information and figurative information units. The similarities and differences in these groups are shown. The common feature is the systematization of information units into three types: symbols (elements), words and sentences. The divisibility criterion of the original information set determines the type of information unit. The difference between structural and semantic information units is shown. An onomasiological method for obtaining information units in the information field is described. A semasiological method for constructing models using information units in the information field is described. Information units are used in two directions: to detail the description of natural phenomena; for modeling or design. The difference between procedural and object information units is shown. An analysis of information units belonging to different areas is given: computer linguistics, computer language, complex system, information field. A set-theoretic description of information units of different directions is given. There is structural similarity between symbolic information units. It lies in the fact that information units of different types are divided into symbols, words and sentences. The article proves that information units can be considered as the result of analysis. The typological similarity between symbolic information units in linguistics, programming and systems analysis is shown. Figurative information units are more informative compared to symbolic information units. The qualitative difference between the figurative information units pixel and voxel is shown.
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Complex Multi-Category Systems
European Journal of Technology and Design. 2024. 12(1): 36-41.
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7. Viktor Ya. Tsvetkov, Evgeniy E. ChekharinEuropean Journal of Technology and Design. 2024. 12(1): 36-41.
Abstract:
The article explores the field of complex systems. The shortcomings of the existing theory of complex systems are noted. The article explores a special type of complex systems: large complex systems. Two concepts are introduced: a complex multicategory system and a local complex system. Large complex systems are divided into homogeneous and heterogeneous. Large heterogeneous complex systems include categorically different integral local complex systems as subsystems. A subsystem of an ordinary complex system does not have integrity, but is dependent on the main system. A local complex system has integrity. It can be used stand-alone or in combination with other systems. The content of complex systems is revealed based on comparison with simple systems. The article gives a formal description of a number of simple systems. A formal description of complex systems is given on the basis of the development of a description of simple systems. Four types of simple and complex systems are considered. The article identifies three types of emergence of complex systems and three types of structure of the system components. It is shown that emergence is a characteristic of complexity. The presence of a class of large complex systems is noted. This class includes a subclass of heterogeneous or hybrid systems. For this subclass, the concept of multicategory complex systems is introduced. the introduction of the term “multi-categorical complex systems” is justified. a number of properties and dependencies in complex systems are studied. The features of many categorical complex systems are described. The article gives a formal description of a complex multi-category system based on a systems approach. It is shown that the structure of a complex multicategory system is described by a multigraph. A feature of complex multi-category systems is the possibility of using corporate management technologies for local complex systems with the additional condition of their complementary behavior. The introduced models expand the application of the theory of complex systems in practical activities.
The article explores the field of complex systems. The shortcomings of the existing theory of complex systems are noted. The article explores a special type of complex systems: large complex systems. Two concepts are introduced: a complex multicategory system and a local complex system. Large complex systems are divided into homogeneous and heterogeneous. Large heterogeneous complex systems include categorically different integral local complex systems as subsystems. A subsystem of an ordinary complex system does not have integrity, but is dependent on the main system. A local complex system has integrity. It can be used stand-alone or in combination with other systems. The content of complex systems is revealed based on comparison with simple systems. The article gives a formal description of a number of simple systems. A formal description of complex systems is given on the basis of the development of a description of simple systems. Four types of simple and complex systems are considered. The article identifies three types of emergence of complex systems and three types of structure of the system components. It is shown that emergence is a characteristic of complexity. The presence of a class of large complex systems is noted. This class includes a subclass of heterogeneous or hybrid systems. For this subclass, the concept of multicategory complex systems is introduced. the introduction of the term “multi-categorical complex systems” is justified. a number of properties and dependencies in complex systems are studied. The features of many categorical complex systems are described. The article gives a formal description of a complex multi-category system based on a systems approach. It is shown that the structure of a complex multicategory system is described by a multigraph. A feature of complex multi-category systems is the possibility of using corporate management technologies for local complex systems with the additional condition of their complementary behavior. The introduced models expand the application of the theory of complex systems in practical activities.
Number of views: 14 Download in PDF
Extended Implicative Relations
European Journal of Technology and Design. 2024. 12(1): 42-48.
Number of views: 14 Download in PDF
8. European Journal of Technology and Design. 2024. 12(1): 42-48.
Abstract:
The article explores extended implicative relations. Extended implicative relations use extended implication. Extended implication describes: relation, consequence, causation and operation. The article shows that extended implication can serve as a complexity assessment tool. The content of implicative information relations is revealed. Implicative information relations are a type of information relations. Implicative information relations describe statics and dynamics in the information field. Statics is about the relationships between information models and their parts. The dynamics of information implicative relations lie in the relationships between the inputs and outputs of information processes. The dynamics of information implicative relations lie in the relationships between the states of information situations and the states of objects in the information field. The formalism for describing implicate information relations and implicate relations is approximately the same in that case unless coordination and configuration parameters are applied. Implicative operational relations allow the assessment of procedural complexity. The difference between simple and complex implicative relations is shown. Complexity estimates for arguments and operations are shown. Taking into account the coordination and configuration of initial objects or sets allows us to expand the concept of implication and introduce the concept of “morphological implication”. Morphological implication is used to describe the transformation operations of a company. The result of morphological implication depends on the relationships between the original sets or configurations. Morphological implication is used in spatial logic. In spatial logic, the results of implicative operations are diverse, since they depend on factors that ordinary logic does not take into account.
The article explores extended implicative relations. Extended implicative relations use extended implication. Extended implication describes: relation, consequence, causation and operation. The article shows that extended implication can serve as a complexity assessment tool. The content of implicative information relations is revealed. Implicative information relations are a type of information relations. Implicative information relations describe statics and dynamics in the information field. Statics is about the relationships between information models and their parts. The dynamics of information implicative relations lie in the relationships between the inputs and outputs of information processes. The dynamics of information implicative relations lie in the relationships between the states of information situations and the states of objects in the information field. The formalism for describing implicate information relations and implicate relations is approximately the same in that case unless coordination and configuration parameters are applied. Implicative operational relations allow the assessment of procedural complexity. The difference between simple and complex implicative relations is shown. Complexity estimates for arguments and operations are shown. Taking into account the coordination and configuration of initial objects or sets allows us to expand the concept of implication and introduce the concept of “morphological implication”. Morphological implication is used to describe the transformation operations of a company. The result of morphological implication depends on the relationships between the original sets or configurations. Morphological implication is used in spatial logic. In spatial logic, the results of implicative operations are diverse, since they depend on factors that ordinary logic does not take into account.
Number of views: 14 Download in PDF