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A Customizable On-Demand Big Data Health Analytics Platform Using Cloud and Container Technologies

Ghoncheh Fahimimoghaddam

Masters thesis (2021)

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Cite this document: Fahimimoghaddam, G. (2021). A Customizable On-Demand Big Data Health Analytics Platform Using Cloud and Container Technologies (Masters thesis, Polytechnique Montréal). Retrieved from https://publications.polymtl.ca/9964/
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Abstract

RÉSUMÉ: Avec des solutions technologiques qui ne cessent de se développer, il devient difficile d’identifier les bons outils pour les systèmes d’information de santé. Chaque outil a ses propres avantages et inconvénients, et nombre d’entre eux possèdent plusieurs applications. De plus, les méthodes et méthodologies traditionnelles deviennent insuffisantes à mesure que nous évoluons vers un traitement distribué et en temps réel, car la technologie mondiale évolue rapidement. Le projet a été réalisé en collaboration avec la Clinique Médecine Urbaine du Quartier Latin (CMU), clinique privée spécialisée dans le traitement des maladies transmissibles et des dépendances. L’objectif de ce projet est de prototyper et valider des outils et des procédés pour une plateforme de science des données adaptative et évolutive qui permettra des analyses efficaces, flexibles et performantes, ainsi que la gestion des modèles d’analyse et d’apprentissage automatique qui permettent une prise de décision basée sur la recherche. ---------- ABSTRACT: Identifying the right tools for Health Information Systems might be difficult with an everincreasing number of possibilities. The various tools have their benefits and downsides, and many of them have many applications. Moreover, traditional methods and methodologies have become obsolete as we transition toward distributed and real-time processing because the world’s technology evolves fast. The goal of this project, which is being carried out in collaboration with the Clinique Médecine Urbaine du Quartier Latin (CMU), a private clinic specializing in the treatment of communicable diseases and addiction, is to prototype and validate tools and processes for a modern, adaptive, and scalable data science platform that will enable efficient, flexible, and performant analytics, as well as management of analytics and machine learning models that allow for research-based decision making. We evaluate tools, methodologies, and other factors used to build a big data platform by analyzing the benefits and downsides of mature computing tools, management, and visualization components of data analytics platforms for health data. Then, we propose the development of an extensible prototype of an Analytics Sandbox for health data. Our focus is on the scalability and analytics part of the project by employing novel technologies in this study.

Open Access document in PolyPublie
Department: Département de génie informatique et génie logiciel
Academic/Research Directors: Marios-Eleftherios Fokaefs
Date Deposited: 27 Apr 2022 08:22
Last Modified: 27 Apr 2022 08:22
PolyPublie URL: https://publications.polymtl.ca/9964/

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