ISSN 0236-235X (P)
ISSN 2311-2735 (E)

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Higher Attestation Commission (VAK) - К1 quartile
Russian Science Citation Index (RSCI)


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Publication date:
19 December 2023

Journal articles №2 2019

21. A specialized enterprise service bus for unified information space of oil and gas industry [№2 за 2019 год]
Authors: N.G. Markov ( - The School of Computer Science & Robotics of the Tomsk Polytechnic University, Department of Automation and Robotics (Professor), Ph.D; I.V. Evsyutkin ( - The School of Computer Science & Robotics of the Tomsk Polytechnic University, Department of Automation and Robotics (Assistant), ;
Abstract: The paper shows that many modern companies operate a large number of heterogeneous information systems for various purposes. Therefore, their integration is relevant. In this regard, it is important to develop a linking software system, which should be specialized for specific conditions of a company. One of the research areas in this field is studying capabilities of a service-oriented architecture concept in relation to building corporate information systems and integrating them with the already operated in-formation systems of a company in order to create a unified information space. A key component in a service-oriented architecture is an enterprise service bus (ESB). This is a middleware for centralized and unified event-oriented messaging between different information systems. The paper analyzes the most popular existing ESB and integration platforms for developing new ESB. It is shown that the most part of the existing ESB is functionally excessive, has high price and re-quires additional development taking into account the specifics of a company when developing ESB on a platform basis. Therefore, creation of new specialized ESB is likely to succeed. It is shown that a framework that is sufficient for developing a specialized ESB for the companies of oil and gas industry is .NET framework. The paper considers functionality of the created specialized ESB and its program implementation features. There are the results of the efficiency research of a specialized ESB and its comparative analysis with ESB of the most popular producers. The paper shows the results of approba-tion of an specialized ESB when solving the problem of integrating various information systems for production management of an oil and gas extraction company using a service-oriented architecture model for the unified information space of the company.
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22. Forecasting time series of infectious morbidity [№2 за 2019 год]
Authors: S.A. Tarasova ( - Kursk State Medical University (Senior Lecturer), Ph.D;
Abstract: The paper presents the topicality and the extent of prior investigation of the problem of forecasting in-fectious morbidity of the population. It also proposes one of the methods of forecasting population’s infectious morbidity based on the classical time series decomposition. Typically, the structure of infectious morbidity time series consists of a trend and a seasonal com-ponent with one or two peaks depending on the type of infection, as well as a residual component, which must satisfy the conditions of randomness, independence and normal distribution of levels with a mathematical expectation equal to zero. When these conditions are fulfilled, the classical decomposi-tion methods identify both the long-term tendency of the process development and seasonal changes. The technique assumes algorithmic and analytical alignment of time series, finding seasonal variations as averaged normalized deviations of actual series levels from the trend line. It does not imply a resid-ual component in seasonality indices, which provides more accurate forecasts of deterministic compo-nents of the time series. The algorithm consists of the following stages. At the first stage, moving averages align the time se-ries, which allows reducing a residual component and obtaining a combination of a trend and seasonal component of the time series. The second stage includes generation of a trend equation using the meth-od of least squares. The trend equation reflects a long-term tendency of the dynamics. The third stage includes calculation of seasonality indices, which show the degree of the seasonal time series deviation from the trend. At the fourth stage, the forecasting model is checked for adequacy. At the fifth stage in-cludes forecasting infectious morbidity for future periods based on extrapolation of the trend and tak-ing into account seasonality indices. The study represents an adequate model for forecasting the population’s morbidity of acute respira-tory viral infections in Russia; its verification has shown sufficient accuracy and reliability of further forecasts.
Keywords: the forecast, forecasting, mathematical model, modeling, time series, trend, seasonality, decomposition, infectious morbidity
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