Big data analytics: Computational intelligence techniques and application areas

• Published in: Technological forecasting & social change 2020-04, Vol.153, p.119253, Article 119253
• Main Authors: Iqbal, Rahat, Doctor, Faiyaz, More, Brian, Mahmud, Shahid, Yousuf, Usman
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Big Data; Big data analytics; Case studies; CI applications; Cities; Commercialization; Computational intelligence; Data analysis; Intelligence; Modern society; Protection; Smart cities; Smart city; State machines; World problems
• ISSN: 0040-1625; 1873-5509
• DOI: 10.1016/j.techfore.2018.03.024

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. •We highlight the importance of Big Data in modern life and economy.•We investigate the benefits of computational intelligence techniques in big data analytics.•We present a data modelling methodology called Hierarchical Spatial-Temporal State Machine•We explore the potential of the powerful combination of Big Data and Computational intelligence•We identify a number of areas where novel applications in real world problems can be developed.