The Big Data Research Ecosystem: An Analytical Literature Study

The Big Data Research Ecosystem: An Analytical Literature Study

Moses John Strydom, Sheryl Buckley
ISBN13: 9781799877059|ISBN10: 1799877051|EISBN13: 9781799877486
DOI: 10.4018/978-1-7998-7705-9.ch090
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MLA

Strydom, Moses John, and Sheryl Buckley. "The Big Data Research Ecosystem: An Analytical Literature Study." Research Anthology on Artificial Intelligence Applications in Security, edited by Information Resources Management Association, IGI Global, 2021, pp. 2027-2057. https://doi.org/10.4018/978-1-7998-7705-9.ch090

APA

Strydom, M. J. & Buckley, S. (2021). The Big Data Research Ecosystem: An Analytical Literature Study. In I. Management Association (Ed.), Research Anthology on Artificial Intelligence Applications in Security (pp. 2027-2057). IGI Global. https://doi.org/10.4018/978-1-7998-7705-9.ch090

Chicago

Strydom, Moses John, and Sheryl Buckley. "The Big Data Research Ecosystem: An Analytical Literature Study." In Research Anthology on Artificial Intelligence Applications in Security, edited by Information Resources Management Association, 2027-2057. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-7705-9.ch090

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Abstract

Big data is the emerging field where innovative technology offers new ways to extract value from an unequivocal plethora of available information. By its fundamental characteristic, the big data ecosystem is highly conjectural and is susceptible to continuous and rapid evolution in line with developments in technology and opportunities, a situation that predisposes the field to research in very brief time spans. Against this background, both academics and practitioners oddly have a limited understanding of how organizations translate potential into actual social and economic value. This chapter conducts an in-depth systematic review of existing penchants in the rapidly developing field of big data research and, thereafter, systematically reviewed these studies to identify some of their weaknesses and challenges. The authors argue that, in practice, most of big data surveys do not focus on technologies, and instead present algorithms and approaches employed to process big data.

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