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

  • Haruna ChiromaEmail author
  • Usman Ali Abdullahi
  • Ibrahim Abaker Targio Hashem
  • Younes Saadi
  • Rawaa Dawoud Al-Dabbagh
  • Muhammad Murtala Ahmad
  • Gbenga Emmanuel Dada
  • Sani Danjuma
  • Jaafar Zubairu Maitama
  • Adamu Abubakar
  • Shafi’i Muhammad Abdulhamid
Part of the Green Energy and Technology book series (GREEN)


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.


Big data analytics Energy Cluster systems Computational intelligent algorithms Artificial neural networks Cuckoo search algorithm 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Haruna Chiroma
    • 1
    Email author
  • Usman Ali Abdullahi
    • 1
    • 2
  • Ibrahim Abaker Targio Hashem
    • 3
  • Younes Saadi
    • 4
  • Rawaa Dawoud Al-Dabbagh
    • 5
  • Muhammad Murtala Ahmad
    • 6
  • Gbenga Emmanuel Dada
    • 7
  • Sani Danjuma
    • 8
  • Jaafar Zubairu Maitama
    • 9
  • Adamu Abubakar
    • 10
  • Shafi’i Muhammad Abdulhamid
    • 11
  1. 1.Department of Computer ScienceFederal College of Education (Technical)GombeNigeria
  2. 2.Department of Computer and Information SciencesUniversiti Teknologi PETRONASPerakMalaysia
  3. 3.Centre for Data Science and Analytics, School of Computing & Information Technology, Taylor’s University, Subang, Jaya, MalaysiaKuala LumpurMalaysia
  4. 4.Department of Computer ScienceUniversity of BatnaFésdisAlgérie
  5. 5.Department of Computer ScienceUniversity of BaghdadBaghdadIraq
  6. 6.Department of Information TechnologyNational Open University of NigeriaLagosNigeria
  7. 7.Department of Computer EngineeringUniversity of MaiduguriMaiduguriNigeria
  8. 8.Department of Mathematical ScienceNorth-West University KanoKanoNigeria
  9. 9.Department of Information TechnologyBayero University KanoKanoNigeria
  10. 10.Department of Information SystemsInternational Islamic University MalaysiaGombakMalaysia
  11. 11.Department of Cyber Security ScienceFederal University of Technology MinnaMinnaNigeria

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