COMPARATIVE ANALYSIS OF METHODS FOR ENHANCING THE EFFICIENCY OF PRODUCTION SYSTEMS FOR BIG DATA PROCESSING

A.G. Gurin, A.V. Grigoriev

About the authors

A.G. Gurin, A.V. Grigoriev

Гурин А.Г. – соискатель, аспирант, ФГБОУ ВО ДонНТУ, кафедра «Программная инженерия им. Л.П. Фельдмана», 283001, Донецк, ул. Артема, 58. Область научных интересов: моделирование интеллектуальных систем, онтологии, тензорные исчисления, полугруппы, нейронные сети.

Григорьев А.В. – к.т.н. доцент, ФГБОУ ВО ДонНТУ, кафедра «Программная инженерия им. Л.П. Фельдмана», 283001, Донецк, ул. Артема, 58. Область научных интересов: интеллектуальные системы автоматизации проектирования, формальные грамматики, онтологии, нейронные сети.

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UDC: 004.825
DOI: 10.24412/2413-7383-2025-4-39-15-26
Language: Russian

Abstract: An analysis of logical inference in production systems for big data processing tasks has been conducted. Despite the effectiveness of classical production systems, the combination of two approaches remains understudied: the use of AND-OR trees for intuitive logic representation and the application of tensor representations for performance enhancement. The research demonstrates the complementarity of these approaches, justifying the feasibility of creating an adaptive meta-algorithm that dynamically selects the method based on the characteristics of the problem being solved. For educational systems, such a hybrid approach is particularly promising, as it allows for combining high data processing performance with complete interpretability of the decisions made.

Keywords: production systems, AND-OR-trees, tensor representations, ontologies, education system.

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Issue: 4(39)'2025
Section: ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
How to cite: A.G. Gurin, A.V. Grigoriev. COMPARATIVE ANALYSIS OF METHODS FOR ENHANCING THE EFFICIENCY OF PRODUCTION SYSTEMS FOR BIG DATA PROCESSING // Problems of Artificial Intelligence. - 2025. - № 4 (39). - P. 15-26. - https://paijournal.guiaidn.ru/en/2025/4(39)-2.html

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