When we talk about Big Data, What do we really mean? Toward a more precise definition of Big Data.

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Frontiers in Big Data Pub Date : 2024-09-10 eCollection Date: 2024-01-01 DOI:10.3389/fdata.2024.1441869
Xiaoyao Han, Oskar Josef Gstrein, Vasilios Andrikopoulos
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引用次数: 0

Abstract

Despite the lack of consensus on an official definition of Big Data, research and studies have continued to progress based on this "no consensus" stance over the years. However, the lack of a clear definition and scope for Big Data results in scientific research and communication lacking a common ground. Even with the popular "V" characteristics, Big Data remains elusive. The term is broad and is used differently in research, often referring to entirely different concepts, which is rarely stated explicitly in papers. While many studies and reviews attempt to draw a comprehensive understanding of Big Data, there has been little systematic research on the position and practical implications of the term Big Data in research environments. To address this gap, this paper presents a Systematic Literature Review (SLR) on secondary studies to provide a comprehensive overview of how Big Data is used and understood across different scientific domains. Our objective was to monitor the application of the Big Data concept in science, identify which technologies are prevalent in which fields, and investigate the discrepancies between the theoretical understanding and practical usage of the term. Our study found that various Big Data technologies are being used in different scientific fields, including machine learning algorithms, distributed computing frameworks, and other tools. These manifestations of Big Data can be classified into four major categories: abstract concepts, large datasets, machine learning techniques, and the Big Data ecosystem. This study revealed that despite the general agreement on the "V" characteristics, researchers in different scientific fields have varied implicit understandings of Big Data. These implicit understandings significantly influence the content and discussions of studies involving Big Data, although they are often not explicitly stated. We call for a clearer articulation of the meaning of Big Data in research to facilitate smoother scientific communication.

当我们谈论大数据时,我们真正指的是什么?更准确地定义大数据。
尽管对大数据的官方定义缺乏共识,但多年来,基于这种 "无共识 "的立场,研究和调查仍在继续推进。然而,由于缺乏对大数据的明确定义和范围,导致科学研究和交流缺乏共同点。即使具有流行的 "V "型特征,大数据仍然难以捉摸。该术语含义广泛,在研究中的用法各不相同,往往指代完全不同的概念,而论文中也很少明确说明这一点。虽然许多研究和综述都试图对大数据有一个全面的理解,但对大数据一词在研究环境中的定位和实际意义却鲜有系统的研究。针对这一空白,本文对二手研究进行了系统性文献综述(SLR),以全面概述大数据在不同科学领域的应用和理解。我们的目标是监测大数据概念在科学领域的应用情况,确定哪些技术在哪些领域盛行,并调查对该术语的理论理解与实际使用之间的差异。我们的研究发现,不同的科学领域正在使用各种大数据技术,包括机器学习算法、分布式计算框架和其他工具。大数据的这些表现形式可分为四大类:抽象概念、大型数据集、机器学习技术和大数据生态系统。本研究发现,尽管对 "V "的特征达成了普遍共识,但不同科学领域的研究人员对大数据有着不同的隐含理解。这些隐含的理解极大地影响了涉及大数据的研究内容和讨论,尽管这些理解往往没有明确表述。我们呼吁在研究中更清晰地阐明大数据的含义,以促进更顺畅的科学交流。
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
CiteScore
5.20
自引率
3.20%
发文量
122
审稿时长
13 weeks
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