Pandemic Symptoms Real-Time Ranking Platform
Date Issued
2021
Author(s)
Ivanovski, Aleksandar
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 GPUbased parallel constructs.
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 GPUbased parallel constructs.
Subjects
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