Rapid Development of a Data Visualization Service in an Emergency Response
IEEE Transactions on Services Computing
; 2022.
Article
in English
| Scopus | ID: covidwho-1788797
ABSTRACT
We present the design and development of a data visualization service (RAMPVIS) in response to the urgent need to support epidemiological modeling workflows during the COVID-19 pandemic. Facing a set of demanding requirements and several practical challenges, our small team of volunteers had to rely on existing knowledge and components of services computing, while thinking on our feet in configuring services composition and adopting suitable approaches to services engineering. Through developing the RAMPVIS service, we have gained useful experience of ensuring conformation to services computing standards, enabling rapid development and early deployment, and facilitating effective and efficient maintenance and operation with limited resources. This experience can be valuable to the ongoing effort for combating the COVID-19 pandemic, and provides a blueprint for visualization service development when future needs for visual analytics arise during emergency response. IEEE
agents; COVID-19; Data models; data visualization; emergency response; Emergency services; epidemiological modeling; ontology; open source; Pandemics; RAMPVIS; rapid deployment; REST; service composition; Service computing; services computing; services engineering; template-based development; Visual analytics; Web services; Open systems; Ontology's; Open-source; Pandemic; Rapid deployments; Services composition; Template-based; Visualization
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
IEEE Transactions on Services Computing
Year:
2022
Document Type:
Article
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