| Literature DB >> 35495546 |
Komal Vekaria1, Prasad Calyam1, Sai Swathi Sivarathri1, Songjie Wang1, Yuanxun Zhang1, Ashish Pandey1, Cong Chen1, Dong Xu1, Trupti Joshi2, Satish Nair1.
Abstract
Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity, we present a novel "OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons).Entities:
Keywords: chatbot guided user interface; knowledge discovery; microservices; recommender system; science gateway
Year: 2020 PMID: 35495546 PMCID: PMC9040042 DOI: 10.1002/cpe.6080
Source DB: PubMed Journal: Concurr Comput ISSN: 1532-0626 Impact factor: 1.831