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Statistical and Computational Needs for Big Data Challenges

Statistical and Computational Needs for Big Data Challenges
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Author(s): Soraya Sedkaoui (Khemis Miliana University, Algeria & SRY Consulting Montpellier, France)
Copyright: 2022
Pages: 23
Source title: Research Anthology on Big Data Analytics, Architectures, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-3662-2.ch030

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Abstract

The traditional way of formatting information from transactional systems to make them available for “statistical processing” does not work in a situation where data is arriving in huge volumes from diverse sources, and where even the formats could be changing. Faced with this volume and diversification, it is essential to develop techniques to make best use of all of these stocks in order to extract the maximum amount of information and knowledge. Traditional analysis methods have been based largely on the assumption that statisticians can work with data within the confines of their own computing environment. But the growth of the amounts of data is changing that paradigm, especially which ride of the progress in computational data analysis. This chapter builds upon sources but also goes further in the examination to answer this question: What needs to be done in this area to deal with big data challenges?

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