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Research and Design of Big Data Unified Analysis Platform Based on Cloud Computing

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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1283))

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Abstract

By introducing the business characteristics of the big data era and the challenges faced by the current big data era, this paper makes a detailed research and design of the big data unified analysis platform based on cloud computing, including the architecture system of the big data analysis platform, the software architecture of the big data analysis platform, the network architecture of the big data analysis platform, and the big data unified analysis platform This paper analyzes the competitive advantages of the unified big data analysis platform based on cloud computing. The big data unified analysis platform based on cloud computing will more effectively support the development of telecom operators in the future.

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Acknowledgements

Science and Technology Research Project of Jiangxi Education Department (161631); Design and implementation of online interactive learning system for teacher qualification in teachers’ colleges and universities.

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Authors and Affiliations

  1. Faculty of Science and Technology, Gannan Normal University, Ganzhou, 341000, China

    Zhen Zhiming

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  1. Zhen Zhiming
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Correspondence to Zhen Zhiming .

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Editors and Affiliations

  1. David Goldman Informatics Centre, University of Sunderland, Sunderland, UK

    John MacIntyre

  2. University of Shanghai for Science and Technology, Shanghai, China

    Jinghua Zhao

  3. Shenzhen University, Shenzen, Guangdong, China

    Xiaomeng Ma

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Zhiming, Z. (2021). Research and Design of Big Data Unified Analysis Platform Based on Cloud Computing. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1283. Springer, Cham. https://doi.org/10.1007/978-3-030-62746-1_129

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