Novel Hybrid Edge-Cloud Framework for Efficient and Sustainable Omics Data Management

Adam, R; Catchpoole, DR; Simoff, SS; Kennedy, PJ; Nguyen, QV

Novel Hybrid Edge-Cloud Framework for Efficient and Sustainable Omics Data Management

Adam, R Catchpoole, DR Simoff, SS Kennedy, PJ Nguyen, QV

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Publisher:: Innovative Healthcare Institute
Publication Type:: Journal Article
Citation:: Innovations in Digital Health Diagnostics and Biomarkers, 2024, 4, (2024), pp. 81-88
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Adam, R
dc.contributor.author	Catchpoole, DR
dc.contributor.author	Simoff, SS
dc.contributor.author	Kennedy, PJ
dc.contributor.author	Nguyen, QV
dc.date.accessioned	2025-01-17T03:58:37Z
dc.date.available	2025-01-17T03:58:37Z
dc.date.issued	2024-01-01
dc.identifier.citation	Innovations in Digital Health Diagnostics and Biomarkers, 2024, 4, (2024), pp. 81-88
dc.identifier.issn	2688-8130
dc.identifier.uri	http://hdl.handle.net/10453/183801
dc.description.abstract	<jats:sec> <jats:title>Introduction</jats:title> <jats:p>The healthcare landscape is rapidly evolving through the integration of diverse data sources such as electronic health records, omics, and genomic data into patient profiles, enhancing personalized medicine and system interoperability. However, this transformation faces challenges in data integration and analysis, compounded by technologic advancements and the increasing volume of health data.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>This study introduces a novel hybrid edge-cloud framework designed to manage the surge of multidimensional genomic and omics data in the healthcare sector. It combines the localized processing capabilities of edge computing with the scalable resources of cloud computing. Evaluations involved using simulated cytometry datasets to demonstrate the architecture’s effectiveness.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>The implementation of the hybrid edge-cloud framework demonstrated improvements in key performance metrics. Network efficiency was enhanced by reducing data transfer latency through localized edge processing. Operational costs were minimized using advanced compression techniques, with the Zstandard (ZSTD) codec significantly reducing data size and improving upload times. The framework also ensured enhanced data privacy by leveraging edge-based anonymization techniques, which process sensitive information locally before transfer to the cloud. These findings highlight the framework’s ability to optimize large-scale omics data management through innovative approaches, achieving significant gains in scalability and security.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusion</jats:title> <jats:p>Integrating edge computing into a cloud-based omics data management framework significantly enhances processing efficiency, reduces data size, and speeds up upload times. This approach offers a transformative potential for omics and genomic data processing in healthcare, with a balanced emphasis on efficiency, cost, and privacy.</jats:p> </jats:sec>
dc.language	en
dc.publisher	Innovative Healthcare Institute
dc.relation.ispartof	Innovations in Digital Health Diagnostics and Biomarkers
dc.relation.isbasedon	10.36401/iddb-24-5
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Novel Hybrid Edge-Cloud Framework for Efficient and Sustainable Omics Data Management
dc.type	Journal Article
utslib.citation.volume	4
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2025-01-17T03:58:36Z
pubs.issue	2024
pubs.publication-status	Published
pubs.volume	4
utslib.citation.issue	2024

Abstract:

Introduction The healthcare landscape is rapidly evolving through the integration of diverse data sources such as electronic health records, omics, and genomic data into patient profiles, enhancing personalized medicine and system interoperability. However, this transformation faces challenges in data integration and analysis, compounded by technologic advancements and the increasing volume of health data. Methods This study introduces a novel hybrid edge-cloud framework designed to manage the surge of multidimensional genomic and omics data in the healthcare sector. It combines the localized processing capabilities of edge computing with the scalable resources of cloud computing. Evaluations involved using simulated cytometry datasets to demonstrate the architecture’s effectiveness. Results The implementation of the hybrid edge-cloud framework demonstrated improvements in key performance metrics. Network efficiency was enhanced by reducing data transfer latency through localized edge processing. Operational costs were minimized using advanced compression techniques, with the Zstandard (ZSTD) codec significantly reducing data size and improving upload times. The framework also ensured enhanced data privacy by leveraging edge-based anonymization techniques, which process sensitive information locally before transfer to the cloud. These findings highlight the framework’s ability to optimize large-scale omics data management through innovative approaches, achieving significant gains in scalability and security. Conclusion Integrating edge computing into a cloud-based omics data management framework significantly enhances processing efficiency, reduces data size, and speeds up upload times. This approach offers a transformative potential for omics and genomic data processing in healthcare, with a balanced emphasis on efficiency, cost, and privacy.

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http://hdl.handle.net/10453/183801