A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption

Haruna, Chiroma and Abdullahi, Usman Ali and Targio Hashem, Ibrahim Abaker and Saadi, Younes and Al-Dabbagh, Rawaa Dawoud and Ahmad, Muhammad Murtala and Emmanuel Dada, Gbenga and Danjuma, Sani and Maitama, Jaafar Zubairu and Abubakar, Adamu and Abdulhamid, Shafi’i Muhammad (2019) A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption. In: Green Energy and Technology. Springer, Cham, 1 . Springer Verlag, Switzerland, pp. 1-20. ISBN 978-3-319-69889-2

Preview	PDF Download (8MB) | Preview
Preview	PDF (chapter Scopus) Download (112kB) | Preview

Abstract

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amount of energy, especially during the training phase. The transmission of big data between service providers, users and data centres emits carbon dioxide as a result of high power consumption. This chapter proposes a theoretical framework for big data analytics using computational intelligent algorithms that has the potential to reduce energy consumption and enhance performance. We suggest that researchers should focus more attention on the issue of energy within big data analytics in relation to computational intelligent algorithms, before this becomes a widespread and urgent problem.

Item Type:	Book Chapter
Additional Information:	7132/74314
Subjects:	Q Science > Q Science (General) > Q300 Cybernetics > Q350 Information theory
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Adamu Abubakar
Date Deposited:	23 Nov 2019 13:15
Last Modified:	23 Nov 2019 13:15
URI:	http://irep.iium.edu.my/id/eprint/74314

Actions (login required)

View Item