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Big Data Analysis for Data Visualization: A Review

Author

Listed:
  • Zhwan M. Khalid

    (Information System Engineering Dept., Erbil polytechnic University, KRG-Iraq)

  • Subhi R.M Zeebaree

    (Duhok Polytechnic University, KRG-Iraq)

Abstract

One of the main characteristics of scaling data is complexity. Heterogeneous data contributes to data integration and the process of big data problems. Both of them are essential and difficult to visualize and interpret large-scale databases since they require considerable data processing and storage capacity. The data age, where data grows exponentially, is a significant struggle to extract data in a manner that the human mind can grasp. This paper reviews and provides data visualization and the Heterogeneous Distributed Storage description and their challenges using different methods through some previous researches. Besides, the results of reviewed research works are compared, and the fundamental shift in the world of large data visualization of virtual reality is discussed.

Suggested Citation

  • Zhwan M. Khalid & Subhi R.M Zeebaree, 2021. "Big Data Analysis for Data Visualization: A Review," International Journal of Science and Business, IJSAB International, vol. 5(2), pages 64-75.
    • Handle: RePEc:aif:journl:v:5:y:2021:i:2:p:64-75
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    References listed on IDEAS

    as
    1. Kavi B. Obaid & Subhi R. M. Zeebaree & Omar M. Ahmed, 2020. "Deep Learning Models Based on Image Classification: A Review," International Journal of Science and Business, IJSAB International, vol. 4(11), pages 75-81.
    2. Kurdistan Wns HamaAli & Subhi R. M. Zeebaree, 2021. "Resources Allocation for Distributed Systems: A Review," International Journal of Science and Business, IJSAB International, vol. 5(2), pages 76-88.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Rahul Basole & Elliot Bendoly & Aravind Chandrasekaran & Kevin Linderman, 2022. "Visualization in Operations Management Research," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 172-187, October.
    2. Hindreen Rashid Abdulqadir & Nawzat Sadiq Ahmed, 2021. "Fog Computing Analysis Based on Internet of Thing: A Review," International Journal of Science and Business, IJSAB International, vol. 5(3), pages 137-147.
    3. Maksymilian Mądziel & Tiziana Campisi, 2023. "Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database," Energies, MDPI, vol. 16(3), pages 1-18, February.

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