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

Chrysostomos Symvoulidis, George Marinos, Athanasios Kiourtis, Argyro Mavrogiorgou and Dimosthenis Kyriazis
Additional contact information
Chrysostomos Symvoulidis: Department of Digital Systems, University of Piraeus, 18534 Piraeus, 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

Future Internet, 2022, vol. 14, issue 4, 1-22

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%.

Keywords: data prefetching; data replication; cloud computing; electronic health records; citizen-centered health (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/14/4/112/pdf (application/pdf)
https://www.mdpi.com/1999-5903/14/4/112/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:14:y:2022:i:4:p:112-:d:785123

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().