Vekaria Komal, Calyam Prasad, Sivarathri Sai Swathi, Wang Songjie, Zhang Yuanxun, Pandey Ashish, Chen Cong, Xu Dong, Joshi Trupti, Nair Satish
Department of Electrical Engineering and Computer Science, University of Missouri-Columbia, Missouri, USA.
Department of Health Management and Informatics, University of Missouri-Columbia, Missouri, USA.
Concurr Comput. 2021 Oct 10;33(19). doi: 10.1002/cpe.6080. Epub 2020 Nov 11.
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).
神经科学和生物信息学等学科的科学家越来越依赖科学网关来处理海量数据的实验，以及从多个角度进行分析和可视化。尽管当前的科学网关提供了对特定学科的计算资源、数据集和工具的便捷访问，但科学家们通常仍需通过缓慢而繁琐的人工操作来进行知识发现，以完成他们的研究/教育任务。推荐系统可以提供专家指导，并帮助他们浏览和发现相关的出版物、工具、数据集，甚至可以自动配置适合特定科学任务的云资源。为了实现将推荐系统集成到科学网关中的潜力，以提高研究效率，我们提出了一种新颖的“准时推荐”（OnTimeRecommend）推荐系统。OnTimeRecommend由几个作为微服务实现的集成推荐模块组成，可以以推荐即服务的形式添加到科学网关中。一个名为Vidura Advisor的聊天机器人插件辅助了科学网关中推荐模块的使用指导。为了验证我们的OnTimeRecommend，我们在两个示例科学网关中进行了集成，并展示了其在特定领域知识发现中对新手和专家用户的益处，一个是神经科学领域的（CyNeuro），另一个是生物信息学领域的（KBCommons）。
