- Title
- Blending big data analytics : review on challenges and a recent study
- Creator
- Amalina, Fairuz; Targio Hashem, Ibrahim; Azizul, Zati; Fong, Ang; Imran, Muhammad
- Date
- 2020
- Type
- Text; Journal article; Review
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184377
- Identifier
- vital:16463
- Identifier
-
https://doi.org/10.1109/ACCESS.2019.2923270
- Identifier
- ISBN:2169-3536 (ISSN)
- Abstract
- With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future. © 2013 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- IEEE Access Vol. 8, no. (2020), p. 3629-3645
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- http://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright @ IEEE
- Rights
- Open Access
- Subject
- 40 Engineering; 46 Information and Computing Sciences; Big data analytics; data analytics; deep learning; machine learning
- Full Text
- Reviewed
- Funder
- This work was supported in part by the University Malaya Research Fund Assistance (BKP) under Grant BKS058-2017, in part by the Fundamental Research Grant Scheme under Ministry of Education Malaysia, under Grant FRGS/1/2018/ICT03/UM/02/3, and in part by the Deanship of Scientific Research, King Saud University, through the Research Group Project under Grant RG-1435-051.
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