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The relevance of the topic considered in the article is to solve the problems of designing management decision support systems for enterprises based on business analytics technology. The research purpose is to analyze the applied methodologies during the design stage of the enterprise information system, to develop principles for using management decision support systems based on business intelligence. The problem statement is to analyze the technologies available on the market, which deal with business analyst systems, their potential use for decision support systems, and to identify the main stages of business analyst for enterprises. Business intelligence (BI) is information that can be obtained from data contained in the operational systems of a firm, enterprise, corporation, or from external sources. The BI can help the management of a company make the best decision in the chosen sphere of human activity faster, and, consequently, win the competition in the market for goods and services. A decision support system (DSS) which uses business intelligence, is an automated structure designed to assist professionals in making decisions in a complex environment and to objectively analyze a subject area. The decision support system is the result of the integration of management information systems and database management systems (DBMS). The internal development of BI is more cost-effective. The methods used are Structured Analysis and Design Technique and Object-oriented methods. The results of the research: the analysis of the possibilities was conducted and recommendations relating to the use of BI within DSS were given. Competition between BI software in business analysts reduces the cost of products created making them accessible to end-users – producers, traders and corporations.

The article is devoted to the description of procedures of economic and mathematical modeling of trends in the field of housing construction taking into account the peculiarities of various countries of the post-Soviet space. The results of analysis of well-known scientific publications on forecasting the dynamics of housing market indicators are presented. It has been shown that most domestic and foreign scientists as the most effective methods of modeling these indicators consider methods of analyzing time trends, in which polynomials of high (in some cases up to the fourth degree) order are used to approximate the available retrospective data. Other common approaches to solving this problem are the use of short-term forecasting based on moving average algorithms, as well as the use of the SARIMA model, which takes into account the trend and seasonal wave. The article shows that these methods do not fully take into account the profound changes in the construction complexes of the post-Soviet states caused by the significant structural transformation of their socio-economic systems. The authors proposed to use econometric models based on regressions with dummy variables to model the main indicators of housing construction, taking into account the complex structure of the external and internal environment of national construction complexes. It has been shown that in a significant number of practical situations, a fairly simple but effective way to take into account the components of the time series of the indicators under consideration in one complex model is to use the model of "change in growth (fall)" when choosing the time of the beginning (end) of a crisis situation as a characteristic point. The results of modeling the main indicators of housing construction for various countries of the post-Soviet space showed that the proposed model when constructing the medium-term forecast allows taking into account the situation component of the analyzed time series.

This paper presents a method and a model framework for R&D changes planning in manufacturing enterprises, implementing digital transformation projects. The relevance of development of such method is evident because of growing number of factors, influencing the decision-making process and simultaneously the complexity of such influence estimation increases. Classical changes planning methods in such cases do not ensure required level of estimation objectivity and credibility. The objects of research are industrial enterprises, actively engaged in research, design and engineering. The subject of research are methods and models for R&D process changes planning in context of digital transformation endeavors, being implemented in the companies. The research objective is to develop a R&D process planning method, enabling to account for corporate changes, related to digital transformation processes. The proposed method is based on the analysis of the discrepancies between the actual enterprise architecture and the target one and search for possible solution to rectify these discrepancies. For quantitative estimation of the changes, an integral indicator "stakeholder satisfaction level" is proposed. This indicator is calculated using a set of models (a model framework), those preparation and application sequence is defined by the considered method. The paper describes the concept of the method, the problems, being solved within each stage, tools used and final outcomes. The example of planning R&D changes in manufacturing enterprise illustrates the method in work and provides for better understanding of the concepts, presented in the paper.

Economic convergence and digitalization are the most important trends of the modern community development. It is their interaction that creates new opportunities for the improvement of competitive capability and performance in the framework of joint business conducted by representatives of the wide variety of segments and sectors of economy. In response to digitalization, an ecosystem becomes the main institutional and organizational form of business in the framework of inter-sectoral economic convergence. The purpose of this article is to define more exactly the concept of an ecosystem as a form of doing joint business in the environment of inter-sectoral economic convergence and digitalization, and to build a classification of ecosystems. The following hypothesis is put forward in the study: an ecosystem as an institutional and organizational form of doing joint business is the result of the concurrent impact of inter-sectoral economic convergence and digitalization thereon, and the "connection of the base product provided to customer by the inter-sectoral economic convergence initiator prior to creating an ecosystem with digital and/or information technology" may be a criterion for classification of economic ecosystems. The novelty of the approach is as follows. The consideration of an ecosystem as a form of doing joint business simultaneously influenced by economic inter- sectoral convergence and digitalization has made it possible to define more exactly the concept of an ecosystem, identify an ecosystem parameter to be applied for classification of ecosystems and the main characteristic thereof the values of which may be used to classify economic ecosystems.

In the conditions of fierce competition, satisfaction of all customer needs provides a trading enterprise with a sustainable competitive advantage. With the traditional structure of the assortment, there is a decrease in both the potential and real level of profit, the loss of competitive positions in promising markets, and, therefore, there is a decrease in the stability of the enterprise. The development of an analysis system to determine the specifics of the product range, optimize the range, and adapt it to the conditions of the Russian market is undoubtedly an urgent task. This article provides an overview of trade and IT companies that use data mining technologies. The survey showed that many companies are using data mining technology to improve customer service, turnover and sales in stores. In this regard, the management of Familia decided to develop its own software that will combine the analysis of turnover and sales in the company's stores in order to increase sales and improve the placement of goods in stores so that the client buys the necessary things, increasing the company's profit. The paper shows the possibility of combining several data mining methods in one system; shows the results of the analysis system and shows the effectiveness of the developed analysis system at Familia. The uniqueness of the developed software is the combination of data mining algorithms into one software product. The developed analysis system, based on the joint work of two data mining algorithms K-means and Apriori, allows you to manage the range of trade enterprises, reducing company losses.

The relevance of the topic considered in the article is to solve the problems of designing management decision support systems for enterprises based on business analytics technology. The research purpose is to analyze the applied methodologies during the design stage of the enterprise information system, to develop principles for using management decision support systems based on business intelligence. The problem statement is to analyze the technologies available on the market, which deal with business analyst systems, their potential use for decision support systems, and to identify the main stages of business analyst for enterprises. Business intelligence (BI) is information that can be obtained from data contained in the operational systems of a firm, enterprise, corporation, or from external sources. The BI can help the management of a company make the best decision in the chosen sphere of human activity faster, and, consequently, win the competition in the market for goods and services. A decision support system (DSS) which uses business intelligence, is an automated structure designed to assist professionals in making decisions in a complex environment and to objectively analyze a subject area. The decision support system is the result of the integration of management information systems and database management systems (DBMS). The internal development of BI is more cost-effective. The methods used are Structured Analysis and Design Technique and Object-oriented methods. The results of the research: the analysis of the possibilities was conducted and recommendations relating to the use of BI within DSS were given. Competition between BI software in business analysts reduces the cost of products created making them accessible to end-users – producers, traders and corporations.

We have proposed a method for constructing dynamically redefined structures for the purpose of modeling abrupt changes in biological processes. The method provides for the analysis of scenarios with a control action, which is aimed at optimizing the profit from the exploitation of biological resources. The situations are described by three differential equations, which are numerically solved on adjacent time intervals. The state of the predicate set controls the selection of dynamically overridden coefficients. We carry out comparisons of all predicates on the basis of averaged individual indicators of generations. Threshold states in the dynamics of population size are a consequence of the selection of events as special nonequilibrium states that change the regulation algorithm. Our method makes it possible to implement dangerous qualitative changes in the scenarios of biological resource management, when the stable modes of their existence are suddenly lost. For practical problems, we have algorithmically implemented computational scenarios for two different processes such as the collapse of fish stocks under expert control of the fishery and a rapid outbreak of pests. The situation of the collapse of the fish population in the scenario with control develops in two stages and is a consequence of the experts 'desire to optimize the operation with uncertainty in an expert’s assessment of a state of a fishery. To confirm the relevance of our models, comparisons are made with the graphs of the development of the two real processes, as the spontaneous population explosion and the stock crisis during optimization of the sea cod fishery.

Article provides automated processing of interval data on dynamics life expectancy in Russians over a long period of time (more than 100 years). Software product for approximation of dynamic processes in the demographic sphere that occur under unstable trend conditions is developed. Software product is based at the author's model of spline approximation the interval data by linear polynomials. The algorithm of program uses method for finding the moments of joining linear splines using reasonable properties of solving minimax problem for dynamic series whose values are interval data: the lower limit of the interval is fixed life expectancy of men, the upper limit is fixed life expectancy of women. As result of computational experiments, the jump-type curve approximation obtained. The jump-type curve preserves important properties of modeled series and has good approximation properties. The special feature of developed software product is the ability to get an approximating function in a matter of seconds, which has high accuracy and allows you to analyze profile of demographic situation in Russia, taking into account life expectancy of men and women over a long period. Were identified key turning points (mid 20th century and beginning of 21st century, when there was decline in life expectancy of men and his essential distance from a more stable indicator for women), after which dynamics of indicators have shown an increasing trend of both indices and reducing the gap between them.

The article presents a method of fuzzy cognitive modeling for heterogeneous electromechanical systems (HEMSs) in the management of innovative design solutions. During the operation of the HEMSs, as a result of their operational aging, the properties of the windings parametric matrices and the HEMSs vector space properties change. Periodic testing of the HEMSs vector space allows obtaining reliable information about the current technical condition of the HEMSs, about its changes during operation and about the risks of operating capability loss. At the same time (I) the presence of proportional changes in signals during sequential testing indicates the homogeneous operational aging of the HEMSs and its rate; (II) a disproportionate change in one of the signals indicates the damage or the development of a heterogeneous aging process; (III) a change in signals with a change in the angular position of the rotor indicates worn bearings or damage of the HEMSs rotor. The article presents the HEMSs model, describes the method for the topological research of the vector space and the method for forming the diagnostic matrices. The deviations of their elements are fuzzy due to the uncertainty of the load, influencing environmental factors and unstable supply voltages. Therefore, for predictive estimation of the HEMSs state, it is proposed to use fuzzy relational cognitive models that allow implementing a completely fuzzy approach to modeling problem situations in these systems. The presented data confirm the growth of the HEMSs heterogeneity under conditions of uncertainty of external influences. The proposed method for predictive estimation of the HEMSs state, based on fuzzy relational cognitive models, provides resistance to an increase in the uncertainty of the estimation results for various models of system dynamics due to a reasonable set of fuzzy vector-matrix operations.

Currently, when modeling complex technological processes in cyber-physical systems, procedures for creating so-called "digital twins" (DT) have become widespread. DT are virtual copies of real objects which reflect their main properties at various stages of the life cycle. The use of digital twins allows real-time monitoring of the current state of the simulated system, and also provides additional opportunities for engineering and deeper customization of its components to improve the quality of products. The development of the "digital twin" technology is facilitated by the ongoing Fourth Industrial Revolution, which is characterized by the massive introduction of cyber-physical systems into production process. These systems are based on the use of the latest technologies for data processing and presentation and have a complex structure of information chain between its components. When creating digital twins of such systems elements, it is advisable to use programming languages, that allow visualization of simulated processes and provide a convenient and developed apparatus for working with complex mathematical dependencies. The Python programming language has similar characteristics. In the article, as an example of a cyber- physical system, a chemical-technological system based on a horizontal-grate machine is considered. This system is designed to implement the process of producing pellets from the apatite-nepheline ore mining wastes. The article describes various aspects of creating a digital twin of its elements that carry out the chemical-technological drying process in relation to a single pellet. The digital twin is implemented using the Python 3.7.5 programming language and provides the visualization of the process in the form of a three-dimensional interactive model. Visualization is done using the VPython library. The description of the digital twin software operation algorithm is given, as well as the type of the information system interface, the input and output information type, the results of modeling the investigated chemical-technological process. It is shown that the developed digital twin can be used in three versions: independently (Digital Twin Prototype), as an instance of a digital twin (Digital Twin Instance), and also as part of a digital twins set (Digital Twin Aggregate).

The article discusses the issues of automated teaching of programming. Programming is one of the fastest growing and promising industries in the modern world. Based on information from recruitment agencies, there is now a shortage of highly specialized programmers, and it will only increase. Currently, employers have increased requirements for the qualification of programmers. Therefore, teaching programming in courses or advanced training of programmers is especially relevant. Automation makes learning more affordable. The role of automated learning on-line is increasing at this time. The article analyzes the principles of construction and typical elements of existing training courses. And it also analyzes methods of increasing the efficiency automated learning that can be done online. Creating circumstances under which the student received the necessary practical skills is an actual issue with such training. These are the skills of writing and debugging correct code in a programming language in the absence or with minimal presence of a teacher. Checking the code by the teacher, searching for errors and identifying inefficient code is an important point in full-time programming training. At this point, the student receives quick feedback from the teacher. Training tasks should be created so that code validation can be performed automatically. The article suggests changes and additions that will increase the effectiveness of existing automated courses of teaching of programming. The analysis of existing software code verification systems was performed. And the verification methods that are applicable in training were identified. Automatic verification of program code can take learning to a new higher level.

The relevance of the study is dictated by the introduction of digitalization in all spheres of human life, and timely protection of information and personal data of citizens in the first place. The objective of the study was the need to transform the methods and approaches of information protection during its transmission, creation and storage. Methodological arsenal of the study is presented by scientific methods of cognition of the studied phenomenon content, the structuring of its components and the system of generalization, and analysis of the causal relationship between the visualization functionality and information security of management decisions. The author analyzed the main virtualization technologies for digital business transformation and concluded that there is the need to improve the legal framework in this area. The significance of this article lies in the fact that the use of the virtualization method will increase the level of business security with minimal losses. Current GOST R 56938-2016 "Information protection when using virtualization technologies" does not fully reflect the issues of information protection in terms of its visualization, which leads to the need to improve the legal framework when using virtualization technologies for data protection. It is essential to pay special attention to cloud storage, collaboration and communication services, remote project management programs, cybersecurity solutions, and CRM systems. This is particularly relevant today during the emergence of virtual workplaces and transferring employees to remote work from home.