"Bring-your-own" plug-in management for next-generation science gateway applications

Vekaria, Komal Bhupendra

doi:10.32469/10355/94312

Cited by 1 publication

(1 citation statement)

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“…The process of using our plug‐in management middleware is broadly applicable to any intelligent agent implementations that are driven by, for example, recommender modules, knowledge bases, and Jupyter Notebooks within various scientific domains such as neuroscience and bioinformatics 59,60 . In the context of this study, we apply our Vidura plug‐in management middleware to OnTimeEvidence for the COVID‐19 domain.…”

Section: Ontimeevidence Science Gatewaymentioning

confidence: 99%

Science gateway adoption using plug‐in middleware for evidence‐based healthcare data management

Oruche

Milman

Alarcon

et al. 2022

Concurrency and Computation

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There is a growing need for next-generation science gateways to increase the accessibility of emerging large-scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID-19-related challenges. Such science gateways that enable access to distributed computing resources for large-scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio-technical approach for developing a next-generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID-19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence-based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug-in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot-assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks. K E Y W O R D Sdiffusion of innovations, intelligent agents, intelligent middleware, microservices, science gateways INTRODUCTIONManaging the emerging collections of large-scale medical datasets such as scientific publications and electronic health records (EHRs) can be a challenging task for medical data consumers (e.g., clinicians, medical researchers) who need to make timely decisions for combating COVID-19-related issues. Data consumers are constantly faced with complex tasks that are labor-intensive and require domain-specific knowledge discovery over medical information for critical decision making. When synthesizing scientific literature for knowledge discovery, data consumers often rely clinical methodologies such as levels of evidence 1,2 to improve information reliability and reduce the quantity of literature by prioritizing scientific rigor (e.g., expert opinions to systematic reviews and meta-analyses). The challenge of manually filtering high-volume of literature based on evidence-based methods presents the need from data consumers to adopt next-generation science gateways to gain access to emerging large-scale datasets and resources for developing timely pandemic-related solutions.Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Section: Ontimeevidence Science Gatewaymentioning

confidence: 99%