BD Hubs: Collaborative Proposal: Midwest: Midwest Big Data Hub: Building Communities to Harness the Data Revolution

BD 中心：协作提案：中西部：中西部大数据中心：建设社区以利用数据革命

• 批准号: 1916252
• 负责人: Jeffrey Peterson
• 金额: $ 34万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916252&HistoricalAwards=false
• 关键词: BD; Hubs; Collaborative; Proposal; Midwest

This project builds on a prior Midwest Big Data Hub effort. In 2015 stakeholders in the Midwest region of the United States formed a consortium of partners and working groups called the Midwest Big Data Hub (MBDH). MBDH aimed to help member organizations working in Big Data coordinate current activities and launch new collaborative projects. The project included stakeholders in the twelve states of the Midwest Census region (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin) and six leading universities that support hundreds of researchers, technologists, and students. This hub provides a basis for collaboration and outreach that increases the potential for benefitting society. The current award is a collaboration among five academic sites (Indiana University, Iowa State University, UIUC/NCSA, the University of Michigan, the University of North Dakota, and the University of Minnesota - Twin Cities). The project focuses on priority areas that are important to the region and can also be influential on the national stage. - The five thematic areas of focus, and the institutional partner leading that thematic area, are: Digital Agriculture (led by Iowa State); Smart, Connected, and Resilient Communities (Indiana University); Water Quality (University of Minnesota); Advanced Materials and Manufacturing (UIUC); and Health and Biomedicine (University of Michigan). - Three cross-cutting areas that are emphasized across the project are: data science education and workforce development; cyberinfrastructure, data access and use; and communication and community development.The priority areas have regional relevance and also have the prospect for integration into societal contexts at the national level. The overall goal is to enable the use of existing and emerging cyberinfrastructure and best practices to improve access to and use of data. The project plans to reach out to the Midwest community at large and to connect people, resources, and organizations. Ties to Big Data Hubs in three other regions provide a means to advance knowledge across these fields at the national level.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目建立在之前的中西部大数据中心工作的基础上。2015年，美国中西部地区的利益相关者组成了一个由合作伙伴和工作组组成的联盟，称为中西部大数据中心（MBDH）。 MBDH旨在帮助从事大数据工作的成员组织协调当前的活动并启动新的合作项目。 该项目包括中西部人口普查区域的12个州（伊利诺伊州、印第安纳州、爱荷华州、堪萨斯、密歇根州、明尼苏达州、密苏里州、内布拉斯加州、北达科他州、俄亥俄州、南达科他州和威斯康星州）的利益相关者以及支持数百名学生、研究人员和技术人员的六所领先大学。 该中心为协作和外联提供了基础，增加了造福社会的潜力。目前的奖项是五个学术网站（印第安纳州大学，爱荷华州州立大学，UIUC/NCSA，密歇根大学，北达科他州大学和明尼苏达大学双城）之间的合作。 该项目的重点是对该区域很重要的优先领域，也可以在国家舞台上产生影响。 - 五个重点主题领域以及领导该主题领域的机构合作伙伴是：数字农业（由爱荷华州领导）;智能，互联和弹性社区（印第安纳州大学）;水质（明尼苏达大学）;先进材料和制造（UIUC）;以及健康和生物医学（密歇根大学）。 - 整个项目强调的三个交叉领域是：数据科学教育和劳动力发展;网络基础设施、数据访问和使用;以及通信和社区发展，这些优先领域具有区域相关性，也具有融入国家一级社会背景的前景。总体目标是利用现有和新出现的网络基础设施和最佳做法，改进数据的获取和使用。 该项目计划深入到整个中西部社区，并将人员、资源和组织联系起来。该奖项反映了NSF的法定使命，通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Unified Framework for Robust and Efficient Hotspot Detection in Smart Cities

智慧城市中稳健高效的热点检测的统一框架

• DOI: 10.1145/3379562
• 发表时间: 2020
• 期刊: ACM/IMS Transactions on Data Science
• 作者: Xie, Yiqun;Shekhar, Shashi
• 通讯作者: Shekhar, Shashi

Significant DBSCAN+: Statistically Robust Density-based Clustering

• DOI: 10.1145/3474842
• 发表时间: 2021-10
• 期刊: ACM Transactions on Intelligent Systems and Technology (TIST)
• 作者: Yiqun Xie;X. Jia;S. Shekhar;Han Bao;Xun Zhou

Technical perspective: Progress in spatial computing for flood prediction

技术视角：洪水预测空间计算的进展

• DOI: 10.1145/3410410
• 发表时间: 2020
• 期刊: Communications of the ACM
• 影响因子: 22.7
• 作者: Shekhar, Shashi

Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach

• DOI: 10.1145/3466688
• 发表时间: 2021-11-01
• 期刊: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
• 影响因子: 5
• 作者: Gupta, Jayant;Molnar, Carl;Shekhar, Shashi
• 通讯作者: Shekhar, Shashi

Spatial Ensemble Learning for Heterogeneous Geographic Data with Class Ambiguity

• DOI: 10.1145/3337798
• 发表时间: 2019-08-01
• 期刊: ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
• 影响因子: 5
• 作者: Jiang, Zhe;Sainju, Arpan Man;Knight, Joseph
• 通讯作者: Knight, Joseph