A novel recommender system for adapting single machine problems to distributed systems within MapReduce

Kamila Orynbekova, Shirali Kadyrov, Andrey Bogdanchikov, Saidakmal Oktamov

Abstract


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 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.

Keywords


Distributed system; Knowledge-based approach; Machine learning model; MapReduce; Recommender system; Text-based approach

Full Text:

PDF


DOI: https://doi.org/10.11591/eei.v14i1.8370

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).