首页|Studies from University of Groningen Add New Findings in the Area of Machine Lea rning (When we talk about Big Data, What do we really mean? Toward a more precis e definition of Big Data)
Studies from University of Groningen Add New Findings in the Area of Machine Lea rning (When we talk about Big Data, What do we really mean? Toward a more precis e definition of Big Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting out of Leeuwarden, Netherla nds, by NewsRx editors, research stated, “Despite the lack of consensus on an of ficial definition of Big Data, research and studies have continued to progress b ased 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 communicati on lacking a common ground.” Our news journalists obtained a quote from the research from University of Groni ngen: “Even with the popular ‘V’ characteristics, Big Data remains elusive. The term is broad and is used differently in research, often referring to entirely d ifferent concepts, which is rarely stated explicitly in papers. While many studi es and reviews attempt to draw a comprehensive understanding of Big Data, there has been little systematic research on the position and practical implications o f the term Big Data in research environments. To address this gap, this paper pr esents a Systematic Literature Review (SLR) on secondary studies to provide a co mprehensive overview of how Big Data is used and understood across different sci entific domains. Our objective was to monitor the application of the Big Data co ncept in science, identify which technologies are prevalent in which fields, and investigate the discrepancies between the theoretical understanding and practic al usage of the term. Our study found that various Big Data technologies are bei ng used in different scientific fields, including machine learning algorithms, d istributed computing frameworks, and other tools. These manifestations of Big Da ta can be classified into four major categories: abstract concepts, large datase ts, machine learning techniques, and the Big Data ecosystem. This study revealed that despite the general agreement on the ‘V’ characteristics, researchers in d ifferent scientific fields have varied implicit understandings of Big Data.”
University of GroningenLeeuwardenNet herlandsEuropeCyborgsEmerging TechnologiesMachine LearningTechnology
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