Case Study: Spark GPU-Enabled Framework to Control COVID-19 Spread Using Cell-Phone Spatio-Temporal Data

作     者：Hussein Shahata Abdallah Mohamed H.Khafagy Fatma A.Omara 

作者机构：Department of Computer ScienceFayoum UniversityFayoum63514Egypt Department of Computer ScienceCairo UniversityGiza1261Egypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2020年第65卷第11期

页      面：1303-1320页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Big data Coronavirus(COVID-19) parallel K-Means GPU Spark 

摘      要：Nowadays,the world is fighting a dangerous form of Coronavirus that represents an emerging *** its early appearance in China Wuhan city,many countries undertook several strict regulations including lockdowns and social distancing ***,these procedures have badly impacted the world *** and isolating positive/probable virus infected cases using a tree tracking mechanism constitutes a backbone for containing and resisting such fast spreading *** 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 *** 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 in-contact suspected-cases using parallel clustering and classification ***,the daily change of the clusters size and its centroids will be used to predict new regions of infection,as well as,new ***,this approach will support infection avoidance by alerting people approaching areas of high probability of infection using their mobile GPS *** 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 *** processing of such a big data system requires intensive computing ***,GPU tasks carried out to achieve high performance and accelerate the data *** to the sim