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A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption

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Book cover Advances on Computational Intelligence in Energy

Part of the book series: Green Energy and Technology ((GREEN))

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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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Federal College of Education (Technical), Gombe, Nigeria

    Haruna Chiroma & Usman Ali Abdullahi

  2. Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Perak, Malaysia

    Usman Ali Abdullahi

  3. Centre for Data Science and Analytics, School of Computing & Information Technology, Taylor’s University, Subang, Jaya, Malaysia, Kuala Lumpur, Malaysia

    Ibrahim Abaker Targio Hashem

  4. Department of Computer Science, University of Batna, Fésdis, Algérie

    Younes Saadi

  5. Department of Computer Science, University of Baghdad, Baghdad, Iraq

    Rawaa Dawoud Al-Dabbagh

  6. Department of Information Technology, National Open University of Nigeria, Lagos, Nigeria

    Muhammad Murtala Ahmad

  7. Department of Computer Engineering, University of Maiduguri, Maiduguri, Nigeria

    Gbenga Emmanuel Dada

  8. Department of Mathematical Science, North-West University Kano, Kano, Nigeria

    Sani Danjuma

  9. Department of Information Technology, Bayero University Kano, Kano, Nigeria

    Jaafar Zubairu Maitama

  10. Department of Information Systems, International Islamic University Malaysia, Gombak, Malaysia

    Adamu Abubakar

  11. Department of Cyber Security Science, Federal University of Technology Minna, Minna, Nigeria

    Shafi’i Muhammad Abdulhamid

Authors
  1. Haruna Chiroma
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  2. Usman Ali Abdullahi
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  3. Ibrahim Abaker Targio Hashem
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  4. Younes Saadi
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  5. Rawaa Dawoud Al-Dabbagh
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  6. Muhammad Murtala Ahmad
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  8. Sani Danjuma
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  10. Adamu Abubakar
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  11. Shafi’i Muhammad Abdulhamid
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Corresponding author

Correspondence to Haruna Chiroma .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Tutut Herawan

  2. Federal College of Education (Technical), Gombe, Nigeria

    Haruna Chiroma

  3. Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

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Chiroma, H. et al. (2019). A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption. In: Herawan, T., Chiroma, H., Abawajy, J. (eds) Advances on Computational Intelligence in Energy. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-69889-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-69889-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69888-5

  • Online ISBN: 978-3-319-69889-2

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