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Graph Database System for COVID-19 Vaccine Supply
Lecture Notes in Networks and Systems ; 522:217-226, 2023.
Article in English | Scopus | ID: covidwho-2240230
ABSTRACT
The COVID-19 virus has been spreading at an alarming rate causing life-threatening conditions in many human beings. Since vaccines to prevent this disease have been allowed for public usage, it has become extremely important to quickly immunize people to prevent fatalities, which subsequently implies the necessity of an efficient vaccine supply system. In any supply system, technology can enable the transfer and processing of large amounts of data in a quick and secure manner, for all entities involved in the process. It is useful for planning, execution, and analysis. It is helpful for tracking and real-time updates so that the journey of a commodity to be supplied is known to all entities at any given time, and this can be useful to catch any faults or for improving the process. The vaccines often need to be supplied over long distances and thus, there is an evident need to have a database system to model the supply of these vaccines effectively. For this, relational databases have been used for a long time to create a structured and well-defined model. However, when it comes to efficiency and flexibility, modern technology like graph databases can be a better fit while still keeping the structure of data in mind. In this paper, we propose a graph database system for the supply of COVID-19 vaccines and describe its advantages when compared to a traditional relational database system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Full text: Available Collection: Databases of international organizations Database: Scopus Topics: Vaccines Language: English Journal: Lecture Notes in Networks and Systems Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Topics: Vaccines Language: English Journal: Lecture Notes in Networks and Systems Year: 2023 Document Type: Article