Comprehensive Analysis of Various Big Data Classification Techniques: A Challenging Overview

Hemn Barzan Abdalla () and Belal Abuhaija
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Hemn Barzan Abdalla: Department of Computer Science, Wenzhou-Kean University, Wenzhou, P. R. China
Belal Abuhaija: Department of Computer Science, Wenzhou-Kean University, Wenzhou, P. R. China

Journal of Information & Knowledge Management (JIKM), 2023, vol. 22, issue 01, 1-22

Abstract: Data over the internet has been increasing everyday, and automatic mining of essential information from an enormous amount of data has become a challenging task today for an organisation with a huge dataset. In recent years, the prominent technology in the domain of Information Technology (IT) is big data, which is unstructured data that solves the computational complexity of classical database systems. The data is fast and big and typically derived from multiple and independent sources. The three main challenges are data accessing, semantics, and domain knowledge for various big data utilisations and complexities raised by big data volumes. One of the major limitations is the classification of big data. This paper introduces well-defined classification methodologies employed for big data classification. This paper reviews 50 research papers based on classification methods of big data, and such methodologies are primarily categorised into six different categories, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Fuzzy-based method, Bayesian-based method, Random Forest, and Decision Tree. In addition, detailed analysis and discussion are carried out by considering classification techniques, dataset utilised, evaluation metrics, semantic similarity measures, and publication year. In addition, research gaps and issues for several traditional big data classification techniques are explained to expand investigators’ works to provide effective big data management.

Keywords: Data mining; big data; semantic similarity measures; support vector machine; K-nearest neighbor (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219649222500836

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