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Next-Generation Big Data Analytics: State of the Art, Challenges, and Future Research Topics

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. Green open access

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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 > Provost and Vice Provost Offices
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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