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

TL;DR

This paper introduces COVID-QF, a big data framework integrating SQL and NoSQL databases using Hadoop, Spark, and HDFS to efficiently manage and query COVID-19 datasets, reducing processing times and improving data handling.

Contribution

The paper presents a novel storage framework, COVID-QF, that combines SQL and NoSQL databases with big data tools to optimize COVID-19 data processing and querying.

Findings

COVID-QF reduces data processing times for COVID-19 datasets.

The framework effectively manages large-scale COVID-19 data from multiple sources.

Experimental results show COVID-QF outperforms existing data handling methods.

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.