Development of heterogeneous data processing method in organizational and technical systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2025.322629

Keywords:

heterogeneous data, unimodal functions, multimodal functions, destabilizing factors, heterogeneous grouping

Abstract

The object of the study is heterogeneous data in organizational-technical systems. The subject of the study is the process of heterogeneous data processing. The problem of this study is enhancing the efficiency of heterogeneous data processing in organizational-technical systems while ensuring a predefined level of reliability, regardless of the volume of incoming data. A method for heterogeneous data processing in organizational-technical systems has been developed. The originality of the method lies in the use of additional improved procedures, which allow:

– achieving the placement of the initial population of agents in the combined algorithm swarm and their initial position in the search space, considering the uncertainty level of input data circulating in the organizational-technical system. This is achieved using correction coefficients;

– accounting for the initial velocity of each agent in the combined algorithm swarm, enabling search prioritization in the corresponding search space (across elements and components of the organizational-technical system);

– determining the feasibility of decisions in heterogeneous data processing, considering external factors, which reduces the solution search time;

– ability to calculate the required computational resources, determining the additional resources needed in case existing computational capacity is insufficient.

A practical implementation of the proposed method was tested on heterogeneous data processing in an operational military task force, demonstrating: a 14–20 % increase in decision-making efficiency due to the integration of additional procedures; a decision reliability level maintained at 0.9

Author Biographies

Salman Rasheed Owaid, Al Taff University College

PhD, Assosiate Professor, Lecturer of Department

Department of Computer Engineering

Svitlana Kashkevich, State University “Kyiv Aviation Institute”

Senior Lecturer

Department of Intelligent Cybernetic Systems

Andrii Shyshatskyi, State University “Kyiv Aviation Institute”

Doctor of Technical Sciences, Senior Researcher, Professor

Department of Intelligent Cybernetic Systems

Hryhorii Radzivilov, Military Institute of Telecommunications and Informatization named after Heroes of Kruty

PhD, Professor, Deputy Head of the Institute for Research

Oleg Sova, National University of Defense of Ukraine

Doctor of Technical Sciences, Professor, Head of Center

Simulation Modeling Center

Artur Zarubenko, Military Institute of Telecommunications and Informatization named after Heroes of Kruty

PhD, Deputy Head of Department

Department of Telecommunication Systems and Networks

Andrii Veretnov, Central Scientifically-Research Institute of Armaments and Military Equipment of the Armed Forces of Ukraine

PhD, Leading Researcher

Research Department

Roman Lazuta, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”

Junior Researcher

Scientific and Organizational Department

Research Center

Institute of Special Communications and Information Protection

Oleksii Noskov, Military Institute of Telecommunications and Informatization named after Heroes of Kruty

Adjunct

Scientific and Organizational Department

Anastasiia Voznytsia, State University “Kyiv Aviation Institute”

PhD Student

References

  1. Bashkyrov, O. M., Kostyna, O. M., Shyshatskyi, A. V. (2015). Rozvytok intehrovanykh system zviazku ta peredachi danykh dlia potreb Zbroinykh Syl. Ozbroiennia ta viyskova tekhnika, 1, 35–39. Available at: http://nbuv.gov.ua/UJRN/ovt_2015_1_7
  2. Dudnyk, V., Sinenko, Y., Matsyk, M., Demchenko, Y., Zhyvotovskyi, R., Repilo, I. et al. (2020). Development of a method for training artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3 (2 (105)), 37–47. https://doi.org/10.15587/1729-4061.2020.203301
  3. Sova, O., Shyshatskyi, A., Salnikova, O., Zhuk, O., Trotsko, O., Hrokholskyi, Y. (2021). Development of a method for assessment and forecasting of the radio electronic environment. EUREKA: Physics and Engineering, 4, 30–40. https://doi.org/10.21303/2461-4262.2021.001940
  4. Pievtsov, H., Turinskyi, O., Zhyvotovskyi, R., Sova, O., Zvieriev, O., Lanetskii, B., Shyshatskyi, A. (2020). Development of an advanced method of finding solutions for neuro-fuzzy expert systems of analysis of the radioelectronic situation. EUREKA: Physics and Engineering, 4, 78–89. https://doi.org/10.21303/2461-4262.2020.001353
  5. Zuiev, P., Zhyvotovskyi, R., Zvieriev, O., Hatsenko, S., Kuprii, V., Nakonechnyi, O. et al. (2020). Development of complex methodology of processing heterogeneous data in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (9 (106)), 14–23. https://doi.org/10.15587/1729-4061.2020.208554
  6. Shyshatskyi, A. (2020). Complex Methods of Processing Different Data in Intellectual Systems for Decision Support System. International Journal of Advanced Trends in Computer Science and Engineering, 9 (4), 5583–5590. https://doi.org/10.30534/ijatcse/2020/206942020
  7. Yeromina, N., Kurban, V., Mykus, S., Peredrii, O., Voloshchenko, O., Kosenko, V. et al. (2021). The Creation of the Database for Mobile Robots Navigation under the Conditions of Flexible Change of Flight Assignment. International Journal of Emerging Technology and Advanced Engineering, 11 (5), 37–44. https://doi.org/10.46338/ijetae0521_05
  8. Shyshatskyi, A., Stasiuk, T., Odarushchenko, E., Berezanska, K., Demianenko, H. (2023). Method of assessing the state of hierarchical objects based on bio-inspired algorithms. Advanced Information Systems, 7 (3), 44–48. https://doi.org/10.20998/2522-9052.2023.3.06
  9. Ko, Y.-C., Fujita, H. (2019). An evidential analytics for buried information in big data samples: Case study of semiconductor manufacturing. Information Sciences, 486, 190–203. https://doi.org/10.1016/j.ins.2019.01.079
  10. Ramaji, I. J., Memari, A. M. (2018). Interpretation of structural analytical models from the coordination view in building information models. Automation in Construction, 90, 117–133. https://doi.org/10.1016/j.autcon.2018.02.025
  11. Pérez-González, C. J., Colebrook, M., Roda-García, J. L., Rosa-Remedios, C. B. (2019). Developing a data analytics platform to support decision making in emergency and security management. Expert Systems with Applications, 120, 167–184. https://doi.org/10.1016/j.eswa.2018.11.023
  12. Chen, H. (2018). Evaluation of Personalized Service Level for Library Information Management Based on Fuzzy Analytic Hierarchy Process. Procedia Computer Science, 131, 952–958. https://doi.org/10.1016/j.procs.2018.04.233
  13. Chan, H. K., Sun, X., Chung, S.-H. (2019). When should fuzzy analytic hierarchy process be used instead of analytic hierarchy process? Decision Support Systems, 125, 113114. https://doi.org/10.1016/j.dss.2019.113114
  14. Osman, A. M. S. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620–633. https://doi.org/10.1016/j.future.2018.06.046
  15. Gödri, I., Kardos, C., Pfeiffer, A., Váncza, J. (2019). Data analytics-based decision support workflow for high-mix low-volume production systems. CIRP Annals, 68 (1), 471–474. https://doi.org/10.1016/j.cirp.2019.04.001
  16. Harding, J. L. (2013). Data quality in the integration and analysis of data from multiple sources: some research challenges. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-2/W1, 59–63. https://doi.org/10.5194/isprsarchives-xl-2-w1-59-2013
  17. Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24 (1), 65–75. https://doi.org/10.1016/s0020-7373(86)80040-2
  18. Koval, M., Sova, O., Shyshatskyi, A., Artabaiev, Y., Garashchuk, N., Yivzhenko, Y. et al. (2022). Improving the method for increasing the efficiency of decision-making based on bio-inspired algorithms. Eastern-European Journal of Enterprise Technologies, 6 (4 (120)), 6–13. https://doi.org/10.15587/1729-4061.2022.268621
  19. Maccarone, A. D., Brzorad, J. N., Stonel, H. M. (2008). Characteristics and Energetics of Great Egret and Snowy Egret Foraging Flights. Waterbirds, 541. https://doi.org/10.1675/1524-4695-31.4.541
  20. Litvinenko, O., Kashkevich, S., Shyshatskyi, A., Dmytriieva, O., Neronov, S., Plekhova, G. et al.; Shyshatskyi, A. (Ed.) (2024). Information and control systems: modelling and optimizations. Kharkiv: TECHNOLOGY CENTER PC, 180. https://doi.org/10.15587/978-617-8360-04-7
Development of heterogeneous data processing method in organizational and technical systems

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Published

2025-02-24

How to Cite

Owaid, S. R., Kashkevich, S., Shyshatskyi, A., Radzivilov, H., Sova, O., Zarubenko, A., Veretnov, A., Lazuta, R., Noskov, O., & Voznytsia, A. (2025). Development of heterogeneous data processing method in organizational and technical systems. Eastern-European Journal of Enterprise Technologies, 1(4 (133), 64–71. https://doi.org/10.15587/1729-4061.2025.322629

Issue

Vol. 1 No. 4 (133) (2025): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2025 Salman Rasheed Owaid, Svitlana Kashkevich, Andrii Shyshatskyi, Hryhorii Radzivilov, Oleg Sova, Artur Zarubenko, Andrii Veretnov, Roman Lazuta, Oleksii Noskov, Anastasiia Voznytsia

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