@Article{cmc.2020.011313,
AUTHOR = {Hussein Shahata Abdallah, Mohamed H. Khafagy, Fatma A. Omara},
TITLE = {Case Study: Spark GPU-Enabled Framework to Control  COVID-19 Spread Using Cell-Phone Spatio-Temporal Data},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1303--1320},
URL = {http://www.techscience.com/cmc/v65n2/39875},
ISSN = {1546-2226},
ABSTRACT = {Nowadays, the world is fighting a dangerous form of Coronavirus that 
represents an emerging pandemic. Since its early appearance in China Wuhan city, many
countries undertook several strict regulations including lockdowns and social distancing 
measures. Unfortunately, these procedures have badly impacted the world economy. 
Detecting and isolating positive/probable virus infected cases using a tree tracking 
mechanism constitutes a backbone for containing and resisting such fast spreading 
disease. For helping this hard effort, this research presents an innovative case study based 
on big data processing techniques to build a complete tracking system able to identify the 
central areas of infected/suspected people, and the new suspected cases using health 
records integration with mobile stations spatio-temporal data logs. The main idea is to 
identify the positive cases historical movements by tracking their phone location for the 
last 14 days (i.e., the virus incubation period). Then, by acquiring the citizen’s mobile 
phone locations for the same period, the system will be able to measure the Euclidean
distances between positive case locations and other nearby people to identify the incontact suspected-cases using parallel clustering and classification techniques. Moreover, 
the daily change of the clusters size and its centroids will be used to predict new regions 
of infection, as well as, new cases. Moreover, this approach will support infection 
avoidance by alerting people approaching areas of high probability of infection using their 
mobile GPS location. This case study has been developed as a simulation system 
consisting of three components; positive cases/citizens movement’s data generation 
subsystem, big data processing platform including CPU/GPU tasks, and data 
visualization/map geotagging subsystem. The processing of such a big data system 
requires intensive computing tasks. Therefore, GPU tasks carried out to achieve high 
performance and accelerate the data processing. According to the simulated system
results, data partitioning and processing speed up measures have been examined.},
DOI = {10.32604/cmc.2020.011313}
}