High-Performance Mining of COVID-19 Open Research Datasets for Text Classification and Insights in Cloud Computing Environments

TL;DR

This paper presents a hybrid cloud system utilizing Aneka PaaS middleware to efficiently process and categorize COVID-19 research articles, significantly reducing processing time and enabling scalable scholarly data analysis.

Contribution

It introduces a novel hybrid cloud framework with parallel processing for large-scale COVID-19 literature analysis using machine learning.

Findings

Reduced processing time for large datasets

Achieved linear scalability in performance

Effective categorization of COVID-19 research articles

Abstract

COVID-19 global pandemic is an unprecedented health crisis. Since the outbreak, many researchers around the world have produced an extensive collection of literatures. For the research community and the general public to digest, it is crucial to analyse the text and provide insights in a timely manner, which requires a considerable amount of computational power. Clouding computing has been widely adopted in academia and industry in recent years. In particular, hybrid cloud is gaining popularity since its two-fold benefits: utilising existing resource to save cost and using additional cloud service providers to gain assess to extra computing resources on demand. In this paper, we developed a system utilising the Aneka PaaS middleware with parallel processing and multi-cloud capability to accelerate the ETL and article categorising process using machine learning technology on a hybrid cloud. The result is then persisted for further referencing, searching and visualising. Our performance evaluation shows that the system can help with reducing processing time and achieving linear scalability. Beyond COVID-19, the application might be used directly in broader scholarly article indexing and analysing.