TITLE: A Big Data Based Framework for Executing Complex Query Over COVID-19 Datasets (COVID-QF) 

ABSTRACT: COVID-19's rapid global spread has driven innovative tools for Big Data
Analytics. These have guided organizations in all fields of the health industry
to track and minimized the effects of virus. Researchers are required to detect
coronaviruses through artificial intelligence, machine learning, and natural
language processing, and to gain a complete understanding of the disease.
COVID-19 takes place in different countries in the world, with which only big
data application and the work of NOSQL databases are suitable. There is a great
number of platforms used for processing NOSQL Databases model like: Spark, H2O
and Hadoop HDFS/MapReduce, which are proper to control and manage the enormous
amount of data. Many challenges faced by large applications programmers,
especially those that work on the COVID-19 databases through hybrid data models
through different APIs and query. In this context, this paper proposes a
storage framework to handle both SQL and NOSQL databases named (COVID-QF) for
COVID-19 datasets in order to treat and handle the problems caused by virus
spreading worldwide by reducing treatment times. In case of NoSQL database,
COVID-QF uses Hadoop HDFS/Map Reduce and Apache Spark. The COVID-QF consists of
three Layers: data collection layer, storage layer, and query Processing layer.
The data is collected in the data collection layer. The storage layer divides
data into collection of data-saving and processing blocks, and it connects the
Connector of the spark with different databases engine to reduce time of saving
and retrieving. While the Processing layer executes the request query and sends
results. The proposed framework used three datasets increased for time for
COVID-19 data (COVID-19-Merging, COVID-19-inside-Hubei and COVID-19-ex-Hubei)
to test experiments of this study. The results obtained insure the superiority
of the COVID-QF framework.
COVID-19's rapid global spread has driven innovative tools for Big Data
Analytics. These have guided organizations in all fields of the health industry
to track and minimized the effects of virus. Researchers are required to detect
coronaviruses through artificial intelligence, machine learning, and natural
language processing, and to gain a complete understanding of the disease.
COVID-19 takes place in different countries in the world, with which only big
data application and the work of NOSQL databases are suitable. There is a great
number of platforms used for processing NOSQL Databases model like: Spark, H2O
and Hadoop HDFS/MapReduce, which are proper to control and manage the enormous
amount of data. Many challenges faced by large applications programmers,
especially those that work on the COVID-19 databases through hybrid data models
through different APIs and query. In this context, this paper proposes a
storage framework to handle both SQL and NOSQL databases named (COVID-QF) for
COVID-19 datasets in order to treat and handle the problems caused by virus
spreading worldwide by reducing treatment times. In case of NoSQL database,
COVID-QF uses Hadoop HDFS/Map Reduce and Apache Spark. The COVID-QF consists of
three Layers: data collection layer, storage layer, and query Processing layer.
The data is collected in the data collection layer. The storage layer divides
data into collection of data-saving and processing blocks, and it connects the
Connector of the spark with different databases engine to reduce time of saving
and retrieving. While the Processing layer executes the request query and sends
results. The proposed framework used three datasets increased for time for
COVID-19 data (COVID-19-Merging, COVID-19-inside-Hubei and COVID-19-ex-Hubei)
to test experiments of this study. The results obtained insure the superiority
of the COVID-QF framework.
A Big Data Based Framework for Executing Complex Query Over COVID-19 Datasets (COVID-QF)
http://arxiv.org/abs/2005.12271v1