Amalina, Fairuz and Hashem, Ibrahim Abaker Targio and Azizul, Zati Hakim and Fong, Ang Tan and Firdaus, Ahmad and Imran, Muhammad and Anuar, Nor Badrul (2020) Blending big data analytics: Review on challenges and a recent study. IEEE Access, 8. pp. 3629-3645. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2019.2923270.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | University Malaya Research Fund Assistance (BKP) (BKS058-2017), Fundamental Research Grant Scheme under Ministry of Education Malaysia (FRGS/1/2018/ICT03/UM/02/3), King Saud Universit (RG-1435-051) |
| Uncontrolled Keywords: | Big Data; Data analysis; Tools; Social networking (online); Computer languages; Companies; Big data analytics; Data analytics; Deep learning; Machine learning |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 17 Jan 2023 02:15 |
| Last Modified: | 17 Jan 2023 02:15 |
| URI: | http://eprints.um.edu.my/id/eprint/37976 |
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