Pandemic Symptoms Real-Time Ranking Platform
Date Issued
2021-11-23
Author(s)
Aasa, Jesper
Abstract
COVID-19 takes an increasing share of everyday life and imposes the need for an exploratory data analysis executed by both, professionals and the general public. The primary focus of this paper is designing and implementing a system for processing the vast amount of case data available to obtain overall statistics for symptoms and rank them in real-time. Processing the current data and
providing a mechanism to process new data generated in real-time from diverse and many sources is one of the current challenges.
Our solution to tackle the challenge is to execute the processing in a massively parallel way enabled by CUDA along with principles and constructs for efficient parallel programming, which are eminent due to the volume and velocity of data, thus, checking the validity of a research question is it possible to process Covid-19 big data challenges more efficiently with GPU-based parallel constructs.
providing a mechanism to process new data generated in real-time from diverse and many sources is one of the current challenges.
Our solution to tackle the challenge is to execute the processing in a massively parallel way enabled by CUDA along with principles and constructs for efficient parallel programming, which are eminent due to the volume and velocity of data, thus, checking the validity of a research question is it possible to process Covid-19 big data challenges more efficiently with GPU-based parallel constructs.
Subjects
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