Artificial intelligence approaches and mechanisms for big data analytics: a systematic study release_mf6hdb5dqzepnapxyzeacojauu

by Amir Masoud Rahmani, Elham Azhir, Saqib Ali, Mokhtar Mohammadi, Omed Hassan Ahmed, Marwan Yassin Ghafour, Sarkar Hasan Ahmed, Mehdi Hosseinzadeh

Published in PeerJ Computer Science by PeerJ.

2021   Volume 7, e488

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.
In application/xml+jats format

Archived Files and Locations

application/pdf  944.5 kB
file_fgv74q2gmrdb3ormakcrqcdshe
peerj.com (publisher)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2021-04-14
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In Keepers Registry
ISSN-L:  2376-5992
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 5e27e46f-aad0-49e7-aa59-1bec6ee476e4
API URL: JSON