PERFORMANCE ANALYSIS OF COVID 19 IN CLOUD COMPUTING ENVIRONMENTS

Abstract

The COVID-19 pandemic has highlighted the urgent need for efficient and scalable solutions for analyzing large volumes of open research datasets. To address this challenge, we propose a cloud-based platform for mining and analyzing COVID-19 open research datasets using natural language processing (NLP), machine learning, and data visualization techniques. The proposed system comprises several components, including data ingestion, data preprocessing, text classification, in sights and visualization, and cloud computing infrastructure. Our system leverages cloud-based storage services, such as Amazon S3, and cloud computing infrastructure, such as Amazon EC2, to process large volumes of data and perform complex computations. The main contributions of this paper include a detailed description of the proposed system architecture, the data ingestion and preprocessing techniques used, the text classification algorithms employed, and the insights and visualization techniques used to generate meaningful patterns and trends in the data. We believe that our proposed system has the potential to significantly impact COVID-19 research and support the development of new treatments, vaccines, and public health policies.