Big Data and Data Science: A Scientometrics Approach

Anita Papic and Endrina Eskic
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Anita Papic: Faculty of social sciences and humanities in Osijek, Croatia
Endrina Eskic: Faculty of social sciences and humanities in Osijek, Croatia

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Abstract: Big data can be defined as collection of data from traditional and digital sources inside and outside of certain organization which can be used for analyzes and discoveries. This paper gives theoretical insight into the emerging field of big data and data science as multidisciplinary science with main focus on data. Data science field aroused at intersection of several other well established fields such as social sciences, statistics, information science, computer science and design. Also in the paper are described data science techniques such as data mining, machine learning and data visualization. The special emphasize is given to business intelligence (BI) which encompasses strategies and technologies that companies use for business data analytics. In the empirical part of the paper the scientometrics analyze was conducted to find out which publications publish about big data and data science the most, which regions, institutions and authors are the most productive in the field of big data and data science and in which scientific disciplines are big data and data science employed the most.

Keywords: big data; business intelligence; data science; scientometrics (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:tkp:mklp18:233-240

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