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HealthFetch: An Influence-Based, Context-Aware Prefetch Scheme in Citizen-Centered Health Storage Clouds

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  • Chrysostomos Symvoulidis

    (Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece
    BYTE Computer S.A., 11741 Athens, Greece)

  • George Marinos

    (Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece)

  • Athanasios Kiourtis

    (Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece)

  • Argyro Mavrogiorgou

    (Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece)

  • Dimosthenis Kyriazis

    (Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece)

Abstract

Over the past few years, increasing attention has been given to the health sector and the integration of new technologies into it. Cloud computing and storage clouds have become essentially state of the art solutions for other major areas and have started to rapidly make their presence powerful in the health sector as well. More and more companies are working toward a future that will allow healthcare professionals to engage more with such infrastructures, enabling them a vast number of possibilities. While this is a very important step, less attention has been given to the citizens. For this reason, in this paper, a citizen-centered storage cloud solution is proposed that will allow citizens to hold their health data in their own hands while also enabling the exchange of these data with healthcare professionals during emergency situations. Not only that, in order to reduce the health data transmission delay, a novel context-aware prefetch engine enriched with deep learning capabilities is proposed. The proposed prefetch scheme, along with the proposed storage cloud, is put under a two-fold evaluation in several deployment and usage scenarios in order to examine its performance with respect to the data transmission times, while also evaluating its outcomes compared to other state of the art solutions. The results show that the proposed solution shows significant improvement of the download speed when compared with the storage cloud, especially when large data are exchanged. In addition, the results of the proposed scheme evaluation depict that the proposed scheme improves the overall predictions, considering the coefficient of determination ( R 2 > 0.94) and the mean of errors (RMSE < 1), while also reducing the training data by 12%.

Suggested Citation

  • Chrysostomos Symvoulidis & George Marinos & Athanasios Kiourtis & Argyro Mavrogiorgou & Dimosthenis Kyriazis, 2022. "HealthFetch: An Influence-Based, Context-Aware Prefetch Scheme in Citizen-Centered Health Storage Clouds," Future Internet, MDPI, vol. 14(4), pages 1-22, April.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:4:p:112-:d:785123
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    References listed on IDEAS

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    1. Webby, Richard & O'Connor, Marcus, 1996. "Judgemental and statistical time series forecasting: a review of the literature," International Journal of Forecasting, Elsevier, vol. 12(1), pages 91-118, March.
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