Lv, Z;
Song, H;
Basanta-Val, P;
Steed, A;
Jo, M;
(2017)
Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics.
IEEE Transactions on Industrial Informatics
, 13
(4)
pp. 1891-1899.
10.1109/TII.2017.2650204.
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Abstract
The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.
Type: | Article |
---|---|
Title: | Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/TII.2017.2650204 |
Publisher version: | https://doi.org/10.1109/TII.2017.2650204 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Science & Technology, Technology, Automation & Control Systems, Computer Science, Interdisciplinary Applications, Engineering, Industrial, Computer Science, Engineering, Big data, massive data, network, ALGORITHM, SYSTEMS, RISK |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10043069 |



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