A Recommendation System for Adapting Single Machine Problems to Distributed Systems within MapReduce

Authors: Kamila Orynbekova, Shirali Kadyrov, Andrey Bogdanchikov, Saidakmal Oktamov, Saidakmal Oktamov (BEEI 8370)
A Novel Recommendation System for Adapting Single Machine Problems to Distributed Systems within MapReduce

This research introduces a novel recommendation system for adapting single machine problems to distributed systems within the MapReduce framework, integrating knowledge and text-based approaches. Categorizing common problems by five MapReduce categories, the study develops and tests a tutorial with promising results. Expanding the dataset, machine learning models recommend solutions on 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 using MapReduce, presenting a novel framework for tailored recommendations and thereby enhancing scalability and efficiency in data processing workflows. Additionally, it fosters innovation in distributed computing paradigms.

Bulletin of Electrical Engineering and Informatics
https://beei.org

Supported by Master Program of Electrical and Computer Engineering, Universitas Ahmad Dahlan, https://mee.uad.ac.id #yogyakarta
Admission: https://mee.uad.ac.id/pendaftaran/

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