Probabilistic Network Models for the Study of Typical Technological Schemes of Information Processing in Analytical Centers
Abstract
The aim of the work is to automate the study of the information and analytical materials (IAM) preparation based on the development of functional and network probabilistic models for the analysis of typical technological schemes to produce IAM in computing centers based on local computer networks. The main research methods are probability theory methods, theory of random processes and theory of queuing. Based on the theory of random processes, a mathematical apparatus has been developed for the analytical study of probabilistic network models for analyzing the time characteristics of IAM preparation processes. Results: Functional equations and calculation ratios have been obtained that make it possible to carry out a multivariate analysis of various schemes for preparing documents and to identify bottlenecks in the main typical technologies for preparing IAM.
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2. Korostelev A.A., Poltoretsky D.A. Automated analytical systems in analytical management activities. Azimuth of Scientific Research: Pedagogy and Psychology. 2012;(1):38-41. Available at: https://www.elibrary.ru/item.asp?id=20841427 (accessed 03.07.2022). (In Russ., abstract in Eng.)
3. Golubinskij E.Yu. Methods of analytical monitoring of information-analytical material quality prepared for government authorithies’ interest. Information Systems and Technologies. 2013;(4):69-76. Available at: https://www.elibrary.ru/item.asp?id=20468025 (accessed 03.07.2022). (In Russ., abstract in Eng.)
4. Ovsyannikov A.A., Golubinskiy E.Y. Forming of the quality characteristics system of informational-analytical materials. Information Systems and Technologies. 2012;(5):73-81. Available at: https://www.elibrary.ru/item.asp?id=17862625 (accessed 03.07.2022). (In Russ., abstract in Eng.)
5. Slivitsky A.B., Dyachkova T.A. [Modern information and analytical technologies for decision support]. Russia: trends and prospects of development. 2022. Vol. 17. Part 3. Moscow: INION RAN; 2022. p. 560-569. Available at: https://www.elibrary.ru/item.asp?id=49700565 (accessed 03.07.2022). (In Russ.)
6. Slivitsky B.A., Slivitsky A.B. [Theoretical and methodological foundations for the analysis of socio-economic systems]. Materials Athanasian Readings. 2022;(1):67-76. Available at: https://www.elibrary.ru/item.asp?id=48044225 (accessed 03.07.2022). (In Russ.)
7. Dadabayeva R.A. The role of information-analytical systems in digital economy. Modern Problems of Law, Economics and Management. 2019:(2):64-70. Available at: https://www.elibrary.ru/item.asp?id=41562961 (accessed 03.07.2022). (In Russ., abstract in Eng.)
8. Kovaleva T.Yu. Information analytical management systems of cluster spatial development in regions: Analysis and priorities for improvement. Perm University Herald. Economy. 2020;15(1):84-109. (In Russ., abstract in Eng.) doi: https://doi.org/10.17072/1994-9960-2020-1-84-109
9. Dubrovskaya Yu.V., Kozonogova E.V., Molodchik A.V. On algorithmization and automation of regional strategizing. Upravlenets – The Manager. 2019;10(4):65-74. (In Russ., abstract in Eng.) doi: https://doi.org/10.29141/2218-5003-2019-10-4-6
10. Khoroshevsky V.G., Pavsky V.A., Pavsky K.V. Calculating robustness indices of distributed computer systems. Bulletin of Tomsk State University. Management, computer engineering and Informatics. 2011;(2):81-88. Available at: https://www.elibrary.ru/item.asp?id=16452670 (accessed 03.07.2022). (In Russ., abstract in Eng.)
11. Bubnov V.P., Safonov V.I., Shardakov K.S. Overview of existing models of non-stationary queuing systems and methods for their calculation. Systems of Control, Communication and Security. 2020;(3):65-121. (In Russ., abstract in Eng.) doi: https://doi.org/10.24411/2410-9916-2020-10303
12. Sigolov G.G., Liupersol'skii A.M. [Method for calculating transients in network queuing models]. Avtomatika i vychislitel'naia tekhnika. 1990;(3):40-43. (In Russ.)
13. Tyrva A.V., Khomonenko A.D. The method for software complexes testing scheduling on design and development phases. St. Petersburg Polytechnical University Journal. Computer Science. Telecommunication and Control Systems. 2009;(4):125-131. Available at: https://www.elibrary.ru/item.asp?id=12977014 (accessed 03.07.2022). (In Russ., abstract in Eng.)
14. Biryukova A.A., Gusev K.V., Leontiev A.S. A method of supporting managerial decision-making in crisis situations based on automated management systems. Informatization and communication. 2022;(6):65-74. (In Russ., abstract in Eng.) doi: https://doi.org/10.34219/2078-8320-2022-13-6-65-74
15. Starikov P.P., Drozdov A.V., Shchetinin G.A. Development of a standard telecommunication control system. Informatization and communication. 2021;(6):144-149. (In Russ., abstract in Eng.) doi: https://doi.org/10.34219/2078-8320-2021-12-6-144-149
16. Andrianova E.G., Golovin S.A., Zykov S.V., Lesko S.A., Chukalina E.R. Review of modern models and methods of analysis of time series of dynamics of processes in social, economic and socio-technical systems. Rossiiskii tekhnologicheskii zhurnal = Russian Technological Journal. 2020;8(4):7-45. (In Russ., abstract in Eng.) doi: https://doi.org/10.32362/2500-316X-2020-8-4-7-45
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18. El Emam K., Melo W., Machado J.C. The prediction of faulty classes using object-oriented design metrics. Journal of Systems and Software. 2001;56(1):63-75. doi: https://doi.org/10.1016/S0164-1212(00)00086-8
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20. Vorobovich N.P. Analytical techniques for calculation of the network model time parameters. Bulletin of KSAU. 2010;(4):6-10. Available at: https://www.elibrary.ru/item.asp?id=15199354 (accessed 03.07.2022). (In Russ., abstract in Eng.)
21. Potekhina E.V., Khripunova P.V. Evolution of the main methods of network planning and management. Social policy and sociology. 2022;21(1):38-45. (In Russ., abstract in Eng.) doi: https://doi.org/10.17922/2071-3665-2022-21-1-38-45
22. Romero D., Larsson L., Rönnbäck A.Ö., Stahre J. Strategizing for Production Innovation. In: Lödding H., Riedel R., Thoben K.D., von Cieminski G., Kiritsis D. (eds.). Advances in Production Management Systems. The Path to Intelligent, Collaborative and Sustainable Manufacturing. APMS 2017. IFIP Advances in Information and Communication Technology. Vol. 513. Cham: Springer; 2017. p. 3-12. doi: https://doi.org/10.1007/978-3-319-66923-6_1
23. Wunder T. Mindsets for Linking Strategy and Sustainability: Planetary Boundaries, Social Foundations, and Sustainable Strategizing. In: Wunder T. (ed.) Rethinking Strategic Management. CSR, Sustainability, Ethics & Governance. Cham: Springer; 2019. p. 1-40. doi: https://doi.org/10.1007/978-3-030-06014-5_1
24. Pundhir S., Kumari V., Ghose U. Performance Interpretation of Supervised Artificial Neural Network Highlighting Role of Weight and Bias for Link Prediction. In: Sanyal G., Travieso-González C.M., Awasthi S., Pinto C.M.A., Purushothama B.R. (eds.) International Conference on Artificial Intelligence and Sustainable Engineering. Lecture Notes in Electrical Engineering. Vol. 836. Singapore: Springer; 2022. p. 109-119. doi: https://doi.org/10.1007/978-981-16-8542-2_9
25. Plish V.E., Suslov V.Y., Truten' A.E. Information-Analytical Systems as Intelligent Partners of Decision-Makers. Cybernetics and Systems Analysis. 2004;40(3):438-450. doi: https://doi.org/10.1023/B:CASA.0000042002.83378.b5
Published
2022-10-24
How to Cite
LEONTIEV, Alexander Savelievich; GOLOVIN, Sergey Anatolevich; GUSEV, Kirill Vyacheslavovich.
Probabilistic Network Models for the Study of Typical Technological Schemes of Information Processing in Analytical Centers.
Modern Information Technologies and IT-Education, [S.l.], v. 18, n. 3, p. 516-527, oct. 2022.
ISSN 2411-1473. Available at: <http://sitito.cs.msu.ru/index.php/SITITO/article/view/886>. Date accessed: 26 apr. 2025.
doi: https://doi.org/10.25559/SITITO.18.202203.516-527.
Section
Theoretical Questions of Computer Science, Computer Mathematics
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