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RETRACTED ARTICLE: The big data visualization technology based ecosystem cycle on high speed network

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This article was retracted on 13 September 2022

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Abstract

Big Data is becoming a key strategy in the business sector, with the increasing number of corporate customer data tracking and collection practices, and the proliferation of multimedia content, such as the proliferation of multimedia and camera modules, and the proliferation of multimedia content. In this paper, we proposed a Big Data ecosystem cycle to create the data utilization strategies and analytic manpower for the success of data utilization with the data uptake. Big Data growth and investment with visualizations are critical to creating a significant role in the development of various Big Data business models. First of all, it is necessary to improve the data for innovation (Releasing Data for Innovation) and data visualization, to minimize the risk of privacy, and to mitigate the risk of privacy leaks to facilitate the promotion of Big Data utilization. It needs to comply with the standardization of Big Data requirements based on ISO/IEC geurigo 1 SC JTC data, and it has necessary to identify the scale of the existing architectures for the sake of accommodating and supporting the architecture that embrace the benefits of legacy architectures.

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Authors and Affiliations

  1. Department of LINC, Dongguk University, Seoul, South Korea

    Hye-Sun Kim

  2. Department of Humanitas College, Kyung Hee University, Seoul, South Korea

    Hwa-Young Jeong

  3. Department of Computer Science & Engineering, College of Engineering, Dongguk University, Seoul, South Korea

    Hae-Jong Joo

Authors
  1. Hye-Sun Kim
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  2. Hwa-Young Jeong
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  3. Hae-Jong Joo
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Corresponding author

Correspondence to Hwa-Young Jeong.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s11042-022-13872-2

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Kim, HS., Jeong, HY. & Joo, HJ. RETRACTED ARTICLE: The big data visualization technology based ecosystem cycle on high speed network. Multimed Tools Appl 78, 28903–28916 (2019). https://doi.org/10.1007/s11042-019-08056-4

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  • DOI: https://doi.org/10.1007/s11042-019-08056-4

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