Artificial intelligence approaches and mechanisms for big data analytics: a systematic study

Author:

Rahmani Amir Masoud12,Azhir Elham3,Ali Saqib4,Mohammadi Mokhtar5,Ahmed Omed Hassan6,Yassin Ghafour Marwan7,Hasan Ahmed Sarkar8,Hosseinzadeh Mehdi910

Affiliation:

1. Future Technology Research Center, National Yunlin University of Science and Technology, Yunlin, Taiwan

2. Department of Computer Science, Khazar University, Baku, Azerbaijan

3. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

4. Department of Information Systems, College of Economics and Political Science, Sultan Qaboos University, Muscat, Oman

5. Department of Information Technology, Lebanese French University, Erbil, Kurdistan Region, Iraq

6. Department of Information Technology, University of Human Development, Sulaymaniyah, Iraq

7. Department of Computer Science, College of Science, University of Halabja, Halabja, Iraq

8. Network Department, Sulaimani Polytechnic University, Sulaymaniyah, Iraq

9. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam

10. Mental Health Research Center, Psychosocial Health Research Institue, Iran University of Medical Sciences, Tehran, Iran

Abstract

Recent advances in sensor networks and the Internet of Things (IoT) technologies have led to the gathering of an enormous scale of data. The exploration of such huge quantities of data needs more efficient methods with high analysis accuracy. Artificial Intelligence (AI) techniques such as machine learning and evolutionary algorithms able to provide more precise, faster, and scalable outcomes in big data analytics. Despite this interest, as far as we are aware there is not any complete survey of various artificial intelligence techniques for big data analytics. The present survey aims to study the research done on big data analytics using artificial intelligence techniques. The authors select related research papers using the Systematic Literature Review (SLR) method. Four groups are considered to investigate these mechanisms which are machine learning, knowledge-based and reasoning methods, decision-making algorithms, and search methods and optimization theory. A number of articles are investigated within each category. Furthermore, this survey denotes the strengths and weaknesses of the selected AI-driven big data analytics techniques and discusses the related parameters, comparing them in terms of scalability, efficiency, precision, and privacy. Furthermore, a number of important areas are provided to enhance the big data analytics mechanisms in the future.

Publisher

PeerJ

Subject

General Computer Science

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