Iqbal, R and Doctor, F and More, B and Mahmud, S and Yousuf, U (2020) Big Data analytics and Computational Intelligence for Cyber Physical Systems: Recent trends and state of the art applications. Future Generation Computer Systems, 105. pp. 766-778. DOI https://doi.org/10.1016/j.future.2017.10.021
Iqbal, R and Doctor, F and More, B and Mahmud, S and Yousuf, U (2020) Big Data analytics and Computational Intelligence for Cyber Physical Systems: Recent trends and state of the art applications. Future Generation Computer Systems, 105. pp. 766-778. DOI https://doi.org/10.1016/j.future.2017.10.021
Iqbal, R and Doctor, F and More, B and Mahmud, S and Yousuf, U (2020) Big Data analytics and Computational Intelligence for Cyber Physical Systems: Recent trends and state of the art applications. Future Generation Computer Systems, 105. pp. 766-778. DOI https://doi.org/10.1016/j.future.2017.10.021
Abstract
Big data is fueling the digital revolution in an increasingly knowledge driven and connected society by offering big data analytics and computational intelligence based solutions to reduce the complexity and cognitive burden on accessing and processing large volumes of data. In this paper, we discuss the importance of big data analytics and computational intelligence techniques applied to data produced from the myriad of pervasively connected machines and personalized devices offering embedded and distributed information processing capabilities. We provide a comprehensive survey of computational intelligence techniques appropriate for the effective processing and analysis of big data. We discuss a number of exemplar application areas that generate big data and can hence benefit from its effective processing. State of the art research and novel applications in health-care, intelligent transportation and social network sentiment analysis, are presented and discussed in the context of Big data, Cyber Physical Systems (CPS), and Computational Intelligence (CI). We present a data modelling methodology, which introduces a novel biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). The HSTSM modelling approach incorporates a number of soft computing techniques such as: deep belief networks, auto-encoders, agglomerative hierarchical clustering and temporal sequence processing, in order to address the computational challenges arising from analyzing and processing large volumes of diverse data to provide an effective big data analytics tool for diverse application areas. A conceptual cyber physical architecture, which can accommodate and benefit from the proposed methodology, is further presented.
Item Type: | Article |
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Uncontrolled Keywords: | Big Data; Big Data analytics; Cyber Physical Systems; Computational Intelligence; CI and CPS applications; HSTSM |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 24 Nov 2017 15:07 |
Last Modified: | 16 May 2024 19:08 |
URI: | http://repository.essex.ac.uk/id/eprint/20727 |
Available files
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