A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

IF 7.7 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Big Data Mining and Analytics Pub Date : 2022-01-25 DOI:10.26599/BDMA.2021.9020028

Isaac Kofi Nti;Juanita Ahia Quarcoo;Justice Aning;Godfred Kusi Fosu

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引用次数: 24

Abstract

The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. The articles were screened to 140 based on the study proposed novel taxonomy. The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA. The related applications fields, challenges, and most importantly the openings for future research, are detailed.

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机器学习在大数据分析中的应用、挑战和前景

数字技术掌握在全世界每一位公民手中，这就提供了前所未有的海量数据。使用大数据分析（BDA）工具和机器学习（ML）算法实时处理这些海量数据的能力带来了许多回报。然而，大量免费的BDA工具、平台和数据挖掘工具使得为正确的任务选择合适的工具变得很有挑战性。本文使用关键词搜索对BDA中的ML进行了全面的小型文献综述；共发现1512篇已发表的文章。根据研究提出的新分类法，这些文章被筛选到140篇。研究结果表明，深度神经网络（15%）、支持向量机（15%），人工神经网络（14%）、决策树（12%）和集成学习技术（11%）在BDA中得到了广泛应用。详细介绍了相关的应用领域、挑战，最重要的是未来研究的前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Big Data Mining and Analytics Computer Science-Computer Science Applications

CiteScore

20.90

自引率

2.20%

发文量

期刊介绍： Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.