A novel recommender system for adapting single machine problems to distributed systems within MapReduce
Orynbekova K. Kadyrov S. Bogdanchikov A. Oktamov S.
February 2025Institute of Advanced Engineering and Science
Bulletin of Electrical Engineering and Informatics
2025#14Issue 1687 - 695 pp.
This research introduces a novel recommender system for adapting single-machine problems to distributed systems within the MapReduce (MR) framework, integrating knowledge and text-based approaches. Categorizing common problems by five MR categories, the study develops and tests a tutorial with promising results. Expanding the dataset, machine learning models recommend solutions for distributed systems. Results demonstrate the logistic regression models effectiveness, with a hybrid approach showing adaptability. The study contributes to advancing the adaptation of single-machine problems to distributed systems MR, presenting a novel framework for tailored recommendations, thereby enhancing scalability and efficiency in data processing workflows. Additionally, it fosters innovation in distributed computing paradigms.
Distributed system , Knowledge-based approach , Machine learning model , MapReduce , Recommender system , Text-based approach
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Department of Computer Sciences, Faculty of Engineering and Natural Sciences, Suleyman Demirel University (SDU), Almaty, Kazakhstan
Department of General Education, New Uzbekistan University, Tashkent, Uzbekistan
Department of Computer Sciences
Department of General Education
10 лет помогаем публиковать статьи Международный издатель
Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026