M.V. Horodetskyi, PhD student, O.S. Kaleniuk, Cand. Sc.
National Technical University of Ukraine
“Igor Sikorsky Kyiv Polytechnic Institute”
37, Pr-t Beresteysʹkyy,Kyiv, Ukraine, 03056,
e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Èlektron. model. 2025, 47(6):03-10

https://doi.org/10.15407/emodel.47.06.003

ABSTRACT

Modern geometric deformation methods, such as polypoint transformations, are widely used in computer graphics, modeling, and engineering simulations. The obvious solution for lifting the performance limitations would be using parallel or distributed computations. Polypoint transformations are not inherently sequential or otherwise limited in parallelization, however, the potential benefits of parallel computation in the context of polypoint transforamtion have not yet been studied. This work studies the prospects of parallel computation of the polypoint transformations based on intersecting planes.

The study focuses on analyzing the efficiency of parallel computations for transforming large 3D models. We investigate the relationship between execution time and thread count, deriving two approximation models (rational and hyperbolic) that closely fit experimental data. A comparison with Amdahlʼs Law reveals that 90% of the algorithm can be parallelized, achieving a speedup of up to 7,5× using 24 threads.

KEYWORDS

Amdahlʼs Law, algorithm optimization, multicore processors, parallel computing, polygonal geometry, polypoint transformations.

REFERENCES

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6. Sydorenko, Yu.V., Kaleniuk, O.S. & Horodetskyi M.V. (2024). Polypoint Transformation Dependency on the Polyfiber Configuration. Control Systems and Computers, 4 (308), 3-9. 
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7. Sidorenko Yu, Zalevskaya O. & Shaldenko O.V. (2022). Calculation of the area of the transformed object at polypoint transformations. Applied geometry and engineering graphics, (102), 65-75
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8. Badaiev, Y.I. & Sidorenko Yu. (2019). Geometric modeling of complex objects on the basis of tile mapping displays of direct cuts. Modern problems of modeling, (16), 17-24. 
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9. Horodetskyi M.V., Sydorenko Іu.V., (2025). Methods of defining geometry of an object in three-dimensional space for polypoint transformations. Èlektron. model, 47(3), 03-11.
   https://doi.org/10.15407/emodel.47.03.003
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Full text: PDF

D.A. Hnatiuk

Èlektron. model. 2025, 47(6):11-33

https://doi.org/10.15407/emodel.47.06.011

ABSTRACT

The use of machine learning methods for detecting anomalies in server-based software systems operating in real time is analyzed. In particular, LSTM for analyzing event logs and XGBoost for classifying structured event features. A systematization of modern methods of monitoring and analyzing event logs based on the use of machine learning methods is carried out. The advantages and disadvantages of individual methods are determined, and the effectiveness of their combined use for detecting anomalies in server software systems is substantiated. Particular attention is paid to the optimization of machine learning methods for detecting non-standard events in logging. They use mechanisms of attention, data caching, and methods of automated feature detection, which allow for real-time analysis of the event stream. The results of the analysis confirm the high potential of hybrid models for improving the stability, reliability, and performance of server software systems, allowing us to outline promising areas for further research in the field of anomaly detection.

KEYWORDS

anomaly monitoring methods, event logs, combined approach for anomaly detection, caching, high-load environments.

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Full text: PDF

N.V. Sytnyk, O.O. Denisova, I.S. Zinovieva

Èlektron. model. 2025, 47(6):34-57

https://doi.org/10.15407/emodel.47.06.034

ABSTRACT

The concept of creating a knowledge graph for harmonizing educational and professional standards in the context of training specialists in higher education institutions is presented. The main purpose of the knowledge graph is to provide a holistic approach to the analysis and integration of various standards applied in education and the professional environment. A competence-oriented approach is used to construct the knowledge graph, which allows identifying the key competences necessary for the development of qualifications in various professional fields. The proposed graph covers educational standards, in accordance with the list of university specialties, and professional standards in accordance with the National Classification of Professions, which opens up opportunities for further research in this area. The development of such a graph will enable a comprehensive, integrated approach to the centralized storage of relevant information at the national level and the study of the interrelationships between professional and educational standards. This, in turn, will improve the quality of training for specialists, ensure greater flexibility and adaptability in responding to changes in the requirements of the labor market and professional environment, and improve the quality and relevance of educational programs.

KEYWORDS

educational and professional standards, graph technologies, knowledge graph, NoSQL graph database, Neo4j DBMS.

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D.P. Sinko, K.D. Sinko

Èlektron. model. 2025, 47(6):58-68

https://doi.org/10.15407/emodel.47.06.058

ABSTRACT

It is considered how to use Python in the author's approach, which was suggested in the work. [1]. The results of numerical modeling showed that the Random Forest and CatBoost methods did the best job of predicting factors of point to potential cluster partitioning. Based on the modeling results, conclusions were made that allow architects of complex cluster cybernetic systems to use the proposed approach as a working tool to prevent critical system conditions associated with network partitioning.

KEYWORDS

split brain problem, cluster splitting problem (CSP), partitioning, ML algorithms, cluster.

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