Ahmed Alwaisi, Shaimaa Safaa and Abbood, Maan Nawaf and Jalil, Luma Fayeq and Kasim, Shahreen and Mohd Fudzee, Mohd Farhan and Hadi, Ronal and Ismail, M. A. (2021) A review on big data stream processing applications: contributions, benefits, and limitations. JOIV : International Journal on Informatics Visualization, 5 (4). pp. 456-460. ISSN 2549-9610. (Published)
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Abstract
The amount of data in our world has been rapidly keep growing from time to time. In the era of big data, the efficient processing and analysis of big data using machine learning algorithm is highly required, especially when the data comes in form of streams. There is no doubt that big data has become an important source of information and knowledge in making decision process. Nevertheless, dealing with this kind of data comes with great difficulties; thus, several techniques have been used in analyzing the data in the form of streams. Many techniques have been proposed and studied to handle big data and give decisions based on off-line batch analysis. Today, we need to make a constructive decision based on online streaming data analysis. Many researchers in recent years proposed some different kind of frameworks for processing the big data streaming. In this work, we explore and present in detail some of the recent achievements in big data streaming in term of contributions, benefits, and limitations. As well as some of recent platforms suitable to be used for big data streaming analytics. Moreover, we also highlight several issues that will be faced in big data stream processing. In conclusion, it is hoped that this study will assist the researchers in choosing the best and suitable framework for big data streaming projects.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Big data; machine learning; Spark; Kafka; data streaming |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Faculty of Computing |
| Depositing User: | Dr. Mohd Arfian Ismail |
| Date Deposited: | 12 Sep 2022 03:13 |
| Last Modified: | 12 Sep 2022 03:13 |
| URI: | http://umpir.ump.edu.my/id/eprint/33988 |
| Download Statistic: | View Download Statistics |
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