Phase 1 IUCRC NC State University: Center for Accelerated Real Time Analytics (CARTA)

第一阶段 IUCRC 北卡罗来纳州立大学：加速实时分析中心 (CARTA)

• 批准号: 1747555
• 负责人: Rada Chirkova
• 金额: $ 74.76万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1747555&HistoricalAwards=false
• 关键词: Phase; IUCRC; NC; State; University

Nearly every research field and industry sector is struggling with extracting useful information from massive and dynamic data in a timely way. Developing tools and technologies in this realm of real-time and accelerated analytics contributes to promoting the progress of science and to advancing the national prosperity and welfare. Success in this realm hinges on balancing fundamental research, technological know-how, and commercial market intelligence. To address this challenge, this project joins industry members with academic centers to conduct multidisciplinary science and research towards extracting value from massive and moving data and enabling better decision making of complex, dynamic data.The Center of Accelerated Real Time Analytics (CARTA) project explores the ways in which relatively-high-risk fundamental developments can be leveraged to help organizations that have longer-term, more complex analytic needs. The focus of CARTA is on horizontal foundational technologies that would create an infrastructure capable of powering applications of national significance. In this context, at North Carolina State University (NCSU), the CARTA/NCSU site will enable new application domains through innovative methods in structuring and transforming data for accelerated and real-time analytics. Having these technologies and tools will be key in achieving the goals of the overall CARTA center. The broader impact of the work of the CARTA center will be in addressing the future advanced, real-time analytics needs of the industry and society. The techniques developed by CARTA can be applied across industry sectors, including national security, healthcare, manufacturing, energy, and business intelligence. The fundamental research done at CARTA will be translated into technology developments, delivering practical solutions to hard problems. The ultimate success of this paradigm shift by the analytics industry will rest on the ability of CARTA universities to prepare experts to take advantage of the science and technologies to solve a variety of real-life applications. CARTA research may involve sensitive academic and industrial data along with public domain data. This data and resulting research outputs will be maintained using appropriate best practices for each type of data for a period of three years after the closing of CARTA. A central repository, suitably tagged for appropriate referencing and documentation, will be set up at https://carta.umbc.edu for maintaining the acquired and generated data from Center projects. Project results will be made available in response to approved user requests.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.

几乎每个研究领域和行业都在努力从海量和动态的数据中及时提取有用的信息。在实时和加速分析领域开发工具和技术有助于促进科学进步，促进国家繁荣和福祉。这一领域的成功取决于平衡基础研究、技术诀窍和商业市场情报。为了应对这一挑战，该项目将行业成员与学术中心联合起来，进行多学科的科学和研究，以从海量和移动的数据中提取价值，并使复杂、动态数据能够更好地做出决策。加速实时分析中心(CARTA)项目探索如何利用相对高风险的基础开发来帮助具有较长期、更复杂分析需求的组织。《宪章》的重点是横向基础技术，这些技术将创建一个能够为具有国家意义的应用提供动力的基础设施。在这方面，在北卡罗来纳州立大学(NCSU)，CARTA/NCSU网站将通过创新方法构建和转换数据，以实现加速和实时分析，从而实现新的应用领域。拥有这些技术和工具将是实现整个CARTA中心目标的关键。CARTA中心工作的更广泛影响将是满足行业和社会未来先进的实时分析需求。CARTA开发的技术可以应用于各个行业，包括国家安全、医疗保健、制造、能源和商业智能。在CARTA所做的基础研究将转化为技术开发，为难题提供实用的解决方案。分析行业这种范式转变的最终成功将取决于CARTA大学培养专家的能力，以利用科学和技术来解决各种现实生活中的应用。CARTA研究可能涉及敏感的学术和工业数据以及公共领域数据。在《宪章》结束后的三年内，这些数据和由此产生的研究成果将使用每类数据的适当最佳做法加以保存。Https://carta.umbc.edu将设立一个适当标记的中央储存库，以供适当的参考和记录，以维护从中心项目获得和生成的数据。项目结果将根据批准的用户请求提供。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Atlantic Hurricane Activity Prediction: A Machine Learning Approach

• DOI: 10.3390/atmos12040455
• 发表时间: 2021-04
• 期刊: Atmosphere
• 影响因子: 2.9
• 作者: Tanmay Asthana;H. Krim;Xia Sun;Siddharth Roheda;Lianqi Xie

Towards Realistic and ReproducibleWeb Crawl Measurements

• DOI: 10.1145/3442381.3450050
• 发表时间: 2021-04
• 期刊: Proceedings of the Web Conference 2021
• 作者: Jordan Jueckstock;Shaown Sarker;Peter Snyder;Aidan Beggs;P. Papadopoulos;Matteo Varvello;B. Livshits;A. Kapravelos

CrawlPhish: Large-scale Analysis of Client-side Cloaking Techniques in Phishing

• DOI: 10.1109/sp40001.2021.00021
• 发表时间: 2021-05
• 期刊: 2021 IEEE Symposium on Security and Privacy (SP)
• 作者: Penghui Zhang;Adam Oest;Haehyun Cho;Zhibo Sun;RC Johnson;Brad Wardman;Shaown Sarker;A. Kapravelos;Tiffany Bao;Ruoyu Wang;Yan Shoshitaishvili;Adam Doupé;Gail-Joon Ahn

Browserprint: An Analysis of the Impact of Browser Features on Fingerprintability and Web Privacy

Browserprint：浏览器功能对指纹识别和 Web 隐私的影响分析

• 发表时间: 2021
• 期刊: International conference on Information Security and Privacy Proceedings
• 作者: • Akhavani, Seyed Ali

A Machine Learning Based Ensemble Forecasting Optimization Algorithm for Preseason Prediction of Atlantic Hurricane Activity

• DOI: 10.3390/atmos12040522
• 发表时间: 2021-04
• 期刊: Atmosphere
• 影响因子: 2.9
• 作者: Xia Sun;Lianqi Xie;S. Shah;Xipeng Shen