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Phase 1 IUCRC Rutgers-New Brunswick: Center for Accelerated Real Time Analytics (CARTA)

Phase 1 IUCRC Rutgers-New Brunswick: Center for Accelerated Real Time Analytics (CARTA)
第一阶段 IUCRC 罗格斯-新不伦瑞克:加速实时分析中心 (CARTA)
批准号:
1747778
负责人:
Dimitris Metaxas
金额:
$75.0万
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
项目状态:
已结题
起止时间:
2018-06-01 至 2024-05-31

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中文摘要
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英文摘要
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 Rutgers-New Brunswick (RU-NB), the CARTA/RU-NB site will enable new application domains through innovative machine learning, statistical, modeling methods and technologies 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. Access to all models and project results will be stored on line and made available for downloading in near real time to respond 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.
期刊论文(35)
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会议论文
DOI: 10.1109/tvcg.2019.2938961
发表时间: 2021-01-01
期刊: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
影响因子: 5.2
作者: [Berseth, Glen, Haworth, Brandon, Faloutsos, Petros]
通讯作者: Faloutsos, Petros
Generation of crowd arrival and destination locations/times in complex transit facilities
生成复杂交通设施中的人群到达和目的地位置/时间
DOI: 10.1007/s00371-019-01761-z
发表时间: 2019
期刊: The Visual Computer
影响因子: --
作者: [Ricks, Brian, Dobson, Andrew, Krontiris, Athanasios, Bekris, Kostas, Kapadia, Mubbasir, Roberts, Fred]
通讯作者: Roberts, Fred
DOI: 10.1145/3581641.3584045
发表时间: 2023-03
期刊: Proceedings of the 28th International Conference on Intelligent User Interfaces
影响因子: --
作者: [Che-Jui Chang;Samuel S. Sohn;Sen Zhang;R. Jayashankar;Muhammad Usman;M. Kapadia]
通讯作者: Che-Jui Chang;Samuel S. Sohn;Sen Zhang;R. Jayashankar;Muhammad Usman;M. Kapadia
DOI: 10.1109/cvpr42600.2020.00656
发表时间: 2020-04
期刊: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子: --
作者: [Long Zhao;Xi Peng;Yuxiao Chen;M. Kapadia;Dimitris N. Metaxas]
通讯作者: Long Zhao;Xi Peng;Yuxiao Chen;M. Kapadia;Dimitris N. Metaxas
32
    Center: IUCRC Phase II Rutgers University: Center for Accelerated and Real Time Analytics (CARTA)
    • 批准号:
      2310966
    • 项目类别:
      Continuing Grant
    • 资助金额:
      $50.0万
    • 财政年份:
      2023
    • 负责人:
      Dimitris Metaxas
    • 依托单位:
    Collaborative Research: HCC: Medium: Linguistically-Driven Sign Recognition from Continuous Signing for American Sign Language (ASL)
    • 批准号:
      2212301
    • 项目类别:
      Standard Grant
    • 资助金额:
      $62.9万
    • 财政年份:
      2022
    • 负责人:
      Dimitris Metaxas
    • 依托单位:
    NSF Convergence Accelerator Track H: AI-based Tools to Enhance Access and Opportunities for the Deaf
    • 批准号:
      2235405
    • 项目类别:
      Standard Grant
    • 资助金额:
      $75.0万
    • 财政年份:
      2022
    • 负责人:
      Dimitris Metaxas
    • 依托单位:
    NSF Convergence Accelerator Track D: Data & AI Methods for Modeling Facial Expressions in Language with Applications to Privacy for the Deaf, ASL Education & Linguistic Res
    • 批准号:
      2040638
    • 项目类别:
      Standard Grant
    • 资助金额:
      $96.0万
    • 财政年份:
      2020
    • 负责人:
      Dimitris Metaxas
    • 依托单位:
    海外基金