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

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 explor...

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Main Authors:	Haruna, Chiroma, Abdullahi, Usman Ali, Targio Hashem, Ibrahim Abaker, Saadi, Younes, Al-Dabbagh, Rawaa Dawoud, Ahmad, Muhammad Murtala, Emmanuel Dada, Gbenga, Danjuma, Sani, Maitama, Jaafar Zubairu, Abubakar, Adamu, Abdulhamid, Shafi’i Muhammad
Format:	Book Chapter
Language:	English English
Published:	Springer Verlag 2019
Subjects:	Q350 Information theory
Online Access:	http://irep.iium.edu.my/74314/ http://irep.iium.edu.my/74314/ http://irep.iium.edu.my/74314/ http://irep.iium.edu.my/74314/1/Advances%2Bon%2BComputational%2BIntelligence%2Bi.pdf http://irep.iium.edu.my/74314/7/73214_A%20Theoretical%20Framework%20for%20Big%20Data%20Analytics_Scopus.pdf

id	iium-74314
recordtype	eprints
spelling	iium-743142019-11-23T05:15:20Z http://irep.iium.edu.my/74314/ A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption Haruna, Chiroma Abdullahi, Usman Ali Targio Hashem, Ibrahim Abaker Saadi, Younes Al-Dabbagh, Rawaa Dawoud Ahmad, Muhammad Murtala Emmanuel Dada, Gbenga Danjuma, Sani Maitama, Jaafar Zubairu Abubakar, Adamu Abdulhamid, Shafi’i Muhammad Q350 Information theory 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. Springer Verlag 2019-07-13 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/74314/1/Advances%2Bon%2BComputational%2BIntelligence%2Bi.pdf application/pdf en http://irep.iium.edu.my/74314/7/73214_A%20Theoretical%20Framework%20for%20Big%20Data%20Analytics_Scopus.pdf 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 https://link.springer.com/chapter/10.1007/978-3-319-69889-2_1 https://doi.org/10.1007/978-3-319-69889-2_1
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	Q350 Information theory
spellingShingle	Q350 Information theory Haruna, Chiroma Abdullahi, Usman Ali Targio Hashem, Ibrahim Abaker Saadi, Younes Al-Dabbagh, Rawaa Dawoud Ahmad, Muhammad Murtala Emmanuel Dada, Gbenga Danjuma, Sani Maitama, Jaafar Zubairu Abubakar, Adamu Abdulhamid, Shafi’i Muhammad A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption
description	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.
format	Book Chapter
author	Haruna, Chiroma Abdullahi, Usman Ali Targio Hashem, Ibrahim Abaker Saadi, Younes Al-Dabbagh, Rawaa Dawoud Ahmad, Muhammad Murtala Emmanuel Dada, Gbenga Danjuma, Sani Maitama, Jaafar Zubairu Abubakar, Adamu Abdulhamid, Shafi’i Muhammad
author_facet	Haruna, Chiroma Abdullahi, Usman Ali Targio Hashem, Ibrahim Abaker Saadi, Younes Al-Dabbagh, Rawaa Dawoud Ahmad, Muhammad Murtala Emmanuel Dada, Gbenga Danjuma, Sani Maitama, Jaafar Zubairu Abubakar, Adamu Abdulhamid, Shafi’i Muhammad
author_sort	Haruna, Chiroma
title	A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption
title_short	A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption
title_full	A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption
title_fullStr	A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption
title_full_unstemmed	A theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption
title_sort	theoretical framework for big data analytics based on computational intelligent algorithms with the potential to reduce energy consumption
publisher	Springer Verlag
publishDate	2019
url	http://irep.iium.edu.my/74314/ http://irep.iium.edu.my/74314/ http://irep.iium.edu.my/74314/ http://irep.iium.edu.my/74314/1/Advances%2Bon%2BComputational%2BIntelligence%2Bi.pdf http://irep.iium.edu.my/74314/7/73214_A%20Theoretical%20Framework%20for%20Big%20Data%20Analytics_Scopus.pdf
first_indexed	2023-09-18T21:45:16Z
last_indexed	2023-09-18T21:45:16Z
_version_	1777413407949979648

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

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