Collaborative Research: Learning to Use Essential Tools and Resources for Data Science with a Cloud-Based Virtual Environment

协作研究：学习在基于云的虚拟环境中使用数据科学的基本工具和资源

• 批准号: 1726532
• 负责人: Hui Zhang
• 金额: $ 23万
• 依托单位: University of Louisville Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1726532&HistoricalAwards=false
• 关键词: Collaborative; Research; Learning; Use; Essential

Business, government, and science researchers are producing massive amounts of complex data. The availability of these huge datasets fuels a need for both data-driven analytics and a 21st-century workforce that can use data analytics to answer questions and solve problems. This collaborative project will develop a cloud-based virtual platform to train undergraduate students how to use software tools essential to data science. The platform will make state-of-the-art computing resources, including both powerful data analysis tools and parallel hardware systems, more accessible to students and faculty, even if they are at institutions without locally available high-power computing systems. The project aims to help students develop critical workforce skills in data science. The project will also provide professional development opportunities to help faculty use data-analysis tools in their courses and research.The goal of this project is to develop a cloud-based infrastructure in the form of a virtual science platform with related training modules. First, it will leverage an existing framework for building web applications to provide broad access to open source, high performance computing resources at the collaborating universities and through the NSF Extreme Science and Engineering Discovery Environment. The cloud-based platform will support both training of students and collaboration among students. Second, the project will produce a data science curriculum targeted to undergraduate students. The curriculum will also be suitable for graduate students, post-doctoral researchers, and information technology professionals interested in data science. The project will deliver a full set of interactive documents and video tutorials on using and configuring the platform. The educational activities will use graphical, interactive, simulation-based, and experiential learning components to teach data science concepts and computing skills, accessed through the cloud-based platform. Through the platform, students will have the opportunity to learn how to use powerful data science resources, enabling their potential to transform data-rich computer science and engineering problems into practical solutions. Third, the project will deliver professional development for faculty at multiple institutions, to help them learn how to use data science in their classrooms and their own research. This project addresses national interests by making state-of-the-art computing resources more accessible to students, supporting their development of critical workforce skills.

商业、政府和科学研究人员正在产生大量复杂的数据。这些庞大数据集的可用性激发了对数据驱动分析和能够使用数据分析来回答问题和解决问题的 21 世纪劳动力的需求。该合作项目将开发一个基于云的虚拟平台，以培训本科生如何使用数据科学必需的软件工具。该平台将使学生和教师更容易获得最先进的计算资源，包括强大的数据分析工具和并行硬件系统，即使他们所在的机构没有本地可用的高功率计算系统。 该项目旨在帮助学生培养数据科学方面的关键劳动力技能。该项目还将提供专业发展机会，帮助教师在课程和研究中使用数据分析工具。该项目的目标是以虚拟科学平台和相关培训模块的形式开发基于云的基础设施。首先，它将利用现有的构建 Web 应用程序的框架，通过 NSF 极限科学与工程发现环境，在合作大学提供对开源、高性能计算资源的广泛访问。基于云的平台将支持学生的培训和学生之间的协作。其次，该项目将制作针对本科生的数据科学课程。该课程也适合对数据科学感兴趣的研究生、博士后研究人员和信息技术专业人士。该项目将提供有关使用和配置平台的全套交互式文档和视频教程。教育活动将使用图形、交互式、基于模拟和体验式学习组件来教授数据科学概念和计算技能，并通过基于云的平台访问。通过该平台，学生将有机会学习如何使用强大的数据科学资源，发挥他们的潜力，将数据丰富的计算机科学和工程问题转化为实际的解决方案。第三，该项目将为多个机构的教师提供专业发展，帮助他们学习如何在课堂和自己的研究中使用数据科学。该项目通过使学生更容易获得最先进的计算资源来满足国家利益，支持他们发展关键的劳动力技能。

项目成果

Parallel Framework for Data-Intensive Computing with XSEDE

• DOI: 10.1145/3332186.3338097
• 发表时间: 2019-07
• 期刊: Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)
• 作者: R. Subramanian;Hui Zhang

Visualizing and Slicing Topological Surfaces in Four Dimensions

四维拓扑表面的可视化和切片

• DOI: 10.2352/j.imagingsci.technol.2021.65.6.060410
• 发表时间: 2021
• 期刊: The Journal of imaging science and technology
• 作者: Liu, Huan;Zhang, Hui
• 通讯作者: Zhang, Hui

Performance Analysis of Divide-and-Conquer strategies for Large scale Simulations in R

R 中大规模仿真分而治之策略的性能分析

• DOI: 10.1109/bigdata.2018.8622068
• 发表时间: 2018
• 期刊: 2018 IEEE International Conference on Big Data (Big Data
• 作者: Subramanian, Ranjini;Zhang, Hui
• 通讯作者: Zhang, Hui

Parallel R Computing on the Web

Web 上的并行 R 计算

• DOI: 10.1109/bigdata47090.2019.9006311
• 发表时间: 2019
• 期刊: 2019 IEEE International Conference on Big Data (Big Data
• 作者: Subramanian, Ranjini;Zhang, Hui
• 通讯作者: Zhang, Hui

Automatic code parallelization for data-intensive computing in multicore systems

多核系统中数据密集型计算的自动代码并行化

• DOI: 10.1088/1742-6596/1411/1/012014
• 发表时间: 2019
• 期刊: Journal of Physics: Conference Series
• 作者: Subramanian, Ranjini;Zhang, Hui
• 通讯作者: Zhang, Hui