A review of sector-specific big data analytics models

Roimah Dollah, Hazleen Aris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Proliferation of mobile applications and platforms, including online social media has given birth to big data. These data that result from the unprecedented development in mobile technologies are regarded as the 'new oil' of the century, which is attributed to the contained value and changes that they can bring about. Various sectors are making attempts at leveraging the potential of big data and a number of sector-specific big data analytics (BDA) models have emerged as a result. In this study, a review on these sector-specific BDA models was performed to understand the current state of their implementation with respect to the five well-known layers found from the literature. The objective of this study is twofold. Firstly, to identify the sectors where BDA models have been proposed and secondly, to identify the implementation details with regard to the layers and respective components that constitute these models. Analysis results showed that the models vary in terms of the number of explicit layers that constitute them and the way the components are arranged into respective layers. It is also found that data storage layer that theoretically exists in the literature is not present explicitly in most models reviewed. Finding from this study helps to understand the difference between theoretical and practical implementation of BDA models, which will be beneficial in shaping future research on BDA models.

Original languageEnglish
Title of host publication2017 IEEE Conference on Big Data and Analytics, ICBDA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages72-80
Number of pages9
ISBN (Electronic)9781538607909
DOIs
Publication statusPublished - 07 Feb 2018
Event2017 IEEE Conference on Big Data and Analytics, ICBDA 2017 - Kuching, Malaysia
Duration: 16 Nov 201717 Nov 2017

Publication series

Name2017 IEEE Conference on Big Data and Analytics, ICBDA 2017
Volume2018-January

Other

Other2017 IEEE Conference on Big Data and Analytics, ICBDA 2017
CountryMalaysia
CityKuching
Period16/11/1717/11/17

Fingerprint

Big data
Data storage equipment
Oils
Oil
Proliferation
Mobile applications
Mobile technology
Social media

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems and Management

Cite this

Dollah, R., & Aris, H. (2018). A review of sector-specific big data analytics models. In 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017 (pp. 72-80). (2017 IEEE Conference on Big Data and Analytics, ICBDA 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBDAA.2017.8284110
Dollah, Roimah ; Aris, Hazleen. / A review of sector-specific big data analytics models. 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 72-80 (2017 IEEE Conference on Big Data and Analytics, ICBDA 2017).
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Dollah, R & Aris, H 2018, A review of sector-specific big data analytics models. in 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017. 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 72-80, 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017, Kuching, Malaysia, 16/11/17. https://doi.org/10.1109/ICBDAA.2017.8284110

A review of sector-specific big data analytics models. / Dollah, Roimah; Aris, Hazleen.

2017 IEEE Conference on Big Data and Analytics, ICBDA 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 72-80 (2017 IEEE Conference on Big Data and Analytics, ICBDA 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Dollah R, Aris H. A review of sector-specific big data analytics models. In 2017 IEEE Conference on Big Data and Analytics, ICBDA 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 72-80. (2017 IEEE Conference on Big Data and Analytics, ICBDA 2017). https://doi.org/10.1109/ICBDAA.2017.8284110