What do we know about the big data researches? A systematic review from 2011 to 2017

Rouhani, Saeed ORCID: 0000-0002-4580-522X, Rotbei, Sayna and Hamidi, Homa (2017) What do we know about the big data researches? A systematic review from 2011 to 2017. Journal of Decision Systems, 26 (4). pp. 368-393. ISSN 1246-0125

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7968 Rouhani (2018) What do we know about the big data researches A systematic review from 2011 to 20176 - What do we know about the big data researches A systematic review from 2011 to 2017.pdf - Accepted Version
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Abstract

Big data are defined as a new phenomenon that can be novel step for improving social life and business condition. Analysing the big data’s researches to extract insights by systematic literature review is the main objective of this research. For synthesis systematically, data from 123 articles are extracted and kinds of studies that were usually done on big data area are investigated. The Systematic Review showed: the most studies were published in 2014, also the main journal that published big data’s article was ‘Big Data Research’ and country with highest investigate about big data were ‘United State and China’. Beside, most researches were done with analytic background. The main research method was experimental and major research type was case study. Our study proved that the majority of researches carried out around big data focused on data management, and most of them identify ‘volume and variety’ of as significant challenges of big data. Likewise, ‘business analytics’ was described in the major benefits.

Item Type: Article
Article Type: Article
Uncontrolled Keywords: Big data; Systematic review; Big data researches
Subjects: Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions: Schools and Research Institutes > School of Business and Technology > Engineering Technologies
Research Priority Areas: Applied Business & Technology
Depositing User: Marta Kemp
Date Deposited: 16 Jan 2020 16:17
Last Modified: 16 Jan 2020 16:30
URI: http://eprints.glos.ac.uk/id/eprint/7968

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