Recommender‐as‐a‐service with chatbot guided domain‐science knowledge discovery in a science gateway

Vekaria, Komal Bhupendra; Calyam, Prasad; Sivarathri, Sai Swathi; Wang, Songjie; Zhang, Yuanxun; Pandey, Ashish; Chen, Cong; Xu, Dong; Joshi, Trupti; Nair, Satish S.

doi:10.1002/cpe.6080

Cited by 8 publications

(7 citation statements)

References 29 publications

(49 reference statements)

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“…Our work in this article builds on early prior work on intelligent agents that showed their importance in the context of science gateways while enabling data consumers to keep up with the emerging resources/technologies in various domains, such as neuroscience and bioinformatics. 32 We also leverage work in Reference 33, where the authors assessed the critical need to improve the adoption and diffusion among science gateway users by providing intelligent agents that are capable of using fuzzy logic for improving computational workflows for researchers. Lastly, the work in Reference 4 leverages chatbot technology as an intricate part of a recommender system to guide researchers and educators, specifically in the neuroscience domain, to improve their predefined workflow through RESTful web services.…”

Section: Middleware and Guided Interfaces For Science Gatewaysmentioning

confidence: 99%

“…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. The knowledge bases we have included involve the collection of documents from the COVID-19 Open Research Dataset (CORD-19) as well as drug/gene terms that have been collected from COVID-19 Vaccine Tracker 53 and Virtual Incident Procurement (ViPR).…”

Section: Sample Microservicesmentioning

confidence: 99%

“…Science gateways previously have been able to alleviate the aforementioned challenge for data consumers across scientific domains, including bioinformatics, neuroscience, physics, chemistry, and material science. 3,4 They are capable of hiding the complexities for domain users via easy-to-use domain application interfaces to access distributed computing resources and emerging datasets that tailor their needs for a variety of scientific tasks related to research and education. They also allow data providers (e.g., administrators, developers) to manage and provide access to data consumers distributed computing resources and large-scale datasets for large-scale data management.…”

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confidence: 99%

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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: Middleware and Guided Interfaces For Science Gatewaysmentioning

confidence: 99%

Section: Sample Microservicesmentioning

confidence: 99%

mentioning

confidence: 99%

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

Oruche

Milman

Alarcon

et al. 2022

Concurrency and Computation

Self Cite

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“…Vekaria et al 6 present an OnTimeRecommend recommender system that aims at improving research productivity. They integrate and demonstrate benefits for beginners and experts users in domain‐specific knowledge discovery in neuroscience (CyNeuro) and in bioinformatics (KBCommons).…”

Section: Artificial Intelligence and Machine Learningmentioning

confidence: 99%

Editorial: Science Gateways Special Issue 2020

Kalyanam

Stankovski

2021

Concurrency and Computation

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“…User Engagement: For the ease of non-expert cloud users, who often struggle to deploy cyberinfrastructure in an efficient manner for data analytics and to gain from the experiences of cyber expert users, we developed intuitive GUIs and structure [87] [88] to capture users requirements and expertise effectively within KbCommons portal [18].…”

Section: Contributions Summarymentioning

confidence: 99%

Trusted resource allocation in volunteer edge-cloud computing for scientific applications

Pandey¹

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Data-intensive science applications in fields such as e.g., bioinformatics, health sciences, and material discovery are becoming increasingly dynamic and demanding with resource requirements. Researchers using these applications which are based on advanced scientific workflows frequently require a diverse set of resources that are often not available within private servers or a single Cloud Service Provider (CSP). For example, a user working with Precision Medicine applications would prefer only those CSPs who follow guidelines from HIPAA (Health Insurance Portability and Accountability Act) for implementing their data services and might want services from other CSPs for economic viability. With the generation of more and more data these workflows often require deployment and dynamic scaling of multi-cloud resources in an efficient and high-performance manner (e.g., quick setup, reduced computation time, and increased application throughput). At the same time, users seek to minimize the costs of configuring the related multi-cloud resources. While performance and cost are among the key factors to decide upon CSP resource selection, the scientific workflows often process proprietary/confidential data that introduces additional constraints of security postures. Thus, users have to make an informed decision on the selection of resources that are most suited for their applications while trading off between the key factors of resource selection which are performance, agility, cost, and security (PACS). Furthermore, even with the most efficient resource allocation across multi-cloud, the cost to solution might not be economical for all users which have led to the development of new paradigms of computing such as volunteer computing where users utilize volunteered cyber resources to meet their computing requirements. For economical and readily available resources, it is essential that such volunteered resources can integrate well with cloud resources for providing the most efficient computing infrastructure for users. In this dissertation, individual stages such as user requirement collection, user's resource preferences, resource brokering and task scheduling, in lifecycle of resource brokering for users are tackled. For collection of user requirements, a novel approach through an iterative design interface is proposed. In addition, fuzzy interference-based approach is proposed to capture users' biases and expertise for guiding their resource selection for their applications. The results showed improvement in performance i.e. time to execute in 98 percent of the studied applications. The data collected on user's requirements and preferences is later used by optimizer engine and machine learning algorithms for resource brokering. For resource brokering, a new integer linear programming based solution (OnTimeURB) is proposed which creates multi-cloud template solutions for resource allocation while also optimizing performance, agility, cost, and security. The solution was further improved by the addition of a machine learning model based on naive bayes classifier which captures the true QoS of cloud resources for guiding template solution creation. The proposed solution was able to improve the time to execute for as much as 96 percent of the largest applications. As discussed above, to fulfill necessity of economical computing resources, a new paradigm of computing viz-a-viz Volunteer Edge Computing (VEC) is proposed which reduces cost and improves performance and security by creating edge clusters comprising of volunteered computing resources close to users. The initial results have shown improved time of execution for application workflows against state-of-the-art solutions while utilizing only the most secure VEC resources. Consequently, we have utilized reinforcement learning based solutions to characterize volunteered resources for their availability and flexibility towards implementation of security policies. The characterization of volunteered resources facilitates efficient allocation of resources and scheduling of workflows tasks which improves performance and throughput of workflow executions. VEC architecture is further validated with state-of-the-art bioinformatics workflows and manufacturing workflows.

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Recommender‐as‐a‐service with chatbot guided domain‐science knowledge discovery in a science gateway

Cited by 8 publications

References 29 publications

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

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

Editorial: Science Gateways Special Issue 2020

Trusted resource allocation in volunteer edge-cloud computing for scientific applications

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