A novel recommender system for adapting single machine problems to distributed systems within MapReduce
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Bulletin of Electrical Engineering and Informatics
Abstract
This research introduces a novel recommender system for adapting singlemachine 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 model's 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.
Description
Keywords
Distributed system, Knowledge-based approach, Machine learning model
Citation
Orynbekova K , Kadyrov Sh ,Bogdanchikov A , Oktamov S /A novel recommender system for adapting single machine problems to distributed systems within MapReduce / Bulletin of Electrical Engineering and Informatics / 2024