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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Book Chapter |
| Language: | English English |
| Published: |
Springer Verlag
2019
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| Subjects: | |
| 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 |
| Summary: | 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. |
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