Evaluating the Effectiveness of Data Classification Models in Hybrid Cloud Infrastructures

  • Authors

    • Edsger W. Dijkstra Professor, University of Texas at Austin, Netherlands. Author

    DOI:

    https://doi.org/10.67228/30715717/IJDEIC-2022PI9T3N

    Published 06-12-2022

  • Data Classification, Hybrid Cloud Infrastructure, Cloud Computing, Machine Learning, Deep Learning, Security And Privacy, Scalability, Data Management, Cloud Architecture, Model Evaluation

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    E. W. Dijkstra, “Evaluating the Effectiveness of Data Classification Models in Hybrid Cloud Infrastructures”, IJDEIC, vol. 5, no. 1, pp. 01–13, Jun. 2022, doi: 10.67228/30715717/IJDEIC-2022PI9T3N.

  • Abstract

    In the era of hybrid cloud computing, managing and processing data efficiently across distributed infrastructures is critical. Data classification models, which categorize data into meaningful groups, play an essential role in organizing, securing, and analyzing data in cloud environments. This paper explores the effectiveness of various data classification models when deployed within hybrid cloud infrastructures. By evaluating different machine learning and deep learning techniques, we aim to assess their performance in terms of accuracy, scalability, and resource efficiency. The study also considers the challenges faced by data classification models in hybrid cloud settings, including security, privacy, and latency issues. Experimental results demonstrate how hybrid cloud environments impact the performance of these models and offer insights into optimizing classification systems. The findings provide valuable guidance for organizations looking to leverage hybrid cloud architectures for efficient data management and intelligent analytics.

  • References

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