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Big data analytics: Computational intelligence techniques and application areas

Author

Listed:
  • Iqbal, Rahat
  • Doctor, Faiyaz
  • More, Brian
  • Mahmud, Shahid
  • Yousuf, Usman

Abstract

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment.

Suggested Citation

  • Iqbal, Rahat & Doctor, Faiyaz & More, Brian & Mahmud, Shahid & Yousuf, Usman, 2020. "Big data analytics: Computational intelligence techniques and application areas," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:tefoso:v:153:y:2020:i:c:s0040162517318498
    DOI: 10.1016/j.techfore.2018.03.024
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    Citations

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    Cited by:

    1. Anke Joubert & Matthias Murawski & Markus Bick, 2023. "Measuring the Big Data Readiness of Developing Countries – Index Development and its Application to Africa," Information Systems Frontiers, Springer, vol. 25(1), pages 327-350, February.
    2. Bag, Surajit & Rahman, Muhammad Sabbir & Srivastava, Gautam & Shore, Adam & Ram, Pratibha, 2023. "Examining the role of virtue ethics and big data in enhancing viable, sustainable, and digital supply chain performance," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    3. María Teresa Bastanchury-López & Carmen De-Pablos-Heredero, 2022. "A Bibliometric Analysis on Smart Cities Related to Land Use," Land, MDPI, vol. 11(12), pages 1-21, November.
    4. Secundo, Giustina & Riad Shams, S.M. & Nucci, Francesco, 2021. "Digital technologies and collective intelligence for healthcare ecosystem: Optimizing Internet of Things adoption for pandemic management," Journal of Business Research, Elsevier, vol. 131(C), pages 563-572.
    5. Merhi, Mohammad I., 2021. "Evaluating the critical success factors of data intelligence implementation in the public sector using analytical hierarchy process," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    6. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    7. Doctor, Faiyaz & Budd, Thomas & Williams, Paul. D. & Prescott, Matt & Iqbal, Rahat, 2022. "Modelling the effect of electric aircraft on airport operations and infrastructure," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    8. Li, Lei & Lin, Jiabao & Ouyang, Ye & Luo, Xin (Robert), 2022. "Evaluating the impact of big data analytics usage on the decision-making quality of organizations," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    9. Hassani, Hossein & Beneki, Christina & Silva, Emmanuel Sirimal & Vandeput, Nicolas & Madsen, Dag Øivind, 2021. "The science of statistics versus data science: What is the future?," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    10. Brewis, Claire & Dibb, Sally & Meadows, Maureen, 2023. "Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    11. Modgil, Sachin & Gupta, Shivam & Sivarajah, Uthayasankar & Bhushan, Bharat, 2021. "Big data-enabled large-scale group decision making for circular economy: An emerging market context," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    12. de Souza, Michele & Pereira, Giancarlo Medeiros & Lopes de Sousa Jabbour, Ana Beatriz & Chiappetta Jabbour, Charbel Jose & Trento, Luiz Reni & Borchardt, Miriam & Zvirtes, Leandro, 2021. "A digitally enabled circular economy for mitigating food waste: Understanding innovative marketing strategies in the context of an emerging economy," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    13. Singha, Sumanta & Arha, Himanshu & Kar, Arpan Kumar, 2023. "Healthcare analytics: A techno-functional perspective," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    14. Wang, Huamao & Yao, Yumei & Salhi, Said, 2020. "Tension in big data using machine learning: Analysis and applications," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    15. Walton, Nigel & Nayak, Bhabani Shankar, 2021. "Rethinking of Marxist perspectives on big data, artificial intelligence (AI) and capitalist economic development," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    16. Kazancoglu, Yigit & Sagnak, Muhittin & Mangla, Sachin Kumar & Sezer, Muruvvet Deniz & Pala, Melisa Ozbiltekin, 2021. "A fuzzy based hybrid decision framework to circularity in dairy supply chains through big data solutions," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    17. Ibrahim, Awad Elsayed Awad & Elamer, Ahmed A. & Ezat, Amr Nazieh, 2021. "The convergence of big data and accounting: innovative research opportunities," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    18. Boccali, Filippo & Mariani, Marcello M. & Visani, Franco & Mora-Cruz, Alexandra, 2022. "Innovative value-based price assessment in data-rich environments: Leveraging online review analytics through Data Envelopment Analysis to empower managers and entrepreneurs," Technological Forecasting and Social Change, Elsevier, vol. 182(C).

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