III: Medium: Collaborative Research: Collaborative Machine-Learning-Centric Data Analytics at Scale

III:媒介:协作研究:以机器学习为中心的大规模协作数据分析

基本信息

  • 批准号:
    2106859
  • 负责人:
  • 金额:
    $ 45.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2021
  • 资助国家:
    美国
  • 起止时间:
    2021-10-01 至 2024-09-30
  • 项目状态:
    已结题

项目摘要

In recent years our society has enjoyed the huge value of online collaboration and sharing, as evidenced by popular cloud-based services such as Google Docs, Dropbox, GitHub, and Overleaf. These benefits become even more attractive due to the new norm of working remotely caused by the unprecedented Covid-19 pandemic. In this award the investigators want to answer the following question: is it possible to develop online systems to support cloud-based services for collaborative data analytics? This computing paradigm allows collaborators to jointly conduct an analysis job on a large amount of data. The investigator team is particularly interested in scenarios where collaborators are from multiple disciplines with different backgrounds, and the analytics is machine learning centric, since such tasks are becoming increasingly common and important. While collaborators in data analytics want to focus on their research topics and fully utilize their expertise and skills, they are also facing challenges due to their complementary backgrounds and asynchronous working schedules. As a consequence, the collaboration has both inter-disciplinary obstacles and intra-disciplinary obstacles. The goal of this award is to study these challenges and develop new techniques to support such novel online services to support collaborative data analytics. The investigator team identifies four unique research topics: 1) Allowing collaborators to debug the training process of a machine learning model by pausing and resuming the process or setting conditional breakpoints, as these tasks tend to be computationally intensive; 2) Enabling collaborative debugging of external user-defined functions in order to not only harness the popular data science libraries in Python and R, but also achieve a high performance using a parallel data-processing engine often written in other languages such as Java and Scala; 3) Supporting collaborative instance labeling and machine learning training and deployment between domain scientists and machine learning experts; and 4) Analyzing and mining the collected data workflows from collaborators to improve the user productivity to formulate new data analytics tasks. The developed techniques will bring the success of many cloud-based collaboration services to the increasingly important space of scalable data analytics using machine learning techniques. The solutions will significantly lower the barriers to entry in terms of enabling domain-specific analysts -- as opposed to computer-science-trained Big Data experts -- to gather and to efficiently, effectively, and interactively analyze large quantities of data in different domains.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.
近年来,我们的社会已经享受到了在线协作和共享的巨大价值,谷歌Docs、Dropbox、GitHub和Overleaf等流行的基于云的服务证明了这一点。由于前所未有的Covid-19大流行造成了远程工作的新常态,这些好处变得更具吸引力。在这个奖项中,研究人员想要回答以下问题:是否有可能开发在线系统来支持基于云的协作数据分析服务?这种计算范式允许协作者共同对大量数据进行分析工作。研究者团队对合作者来自不同学科背景的场景特别感兴趣,并且分析以机器学习为中心,因为这些任务变得越来越普遍和重要。虽然数据分析的合作者希望专注于他们的研究课题,充分利用他们的专业知识和技能,但由于他们的背景互补和异步的工作时间表,他们也面临着挑战。因此,合作既有跨学科障碍,也有学科内障碍。该奖项的目标是研究这些挑战,并开发新技术来支持这种新颖的在线服务,以支持协作数据分析。研究者团队确定了四个独特的研究主题:1)允许合作者通过暂停和恢复过程或设置条件断点来调试机器学习模型的训练过程,因为这些任务往往是计算密集型的;2)支持外部用户定义函数的协同调试,不仅可以利用Python和R中流行的数据科学库,还可以使用通常用其他语言(如Java和Scala)编写的并行数据处理引擎实现高性能;3)支持领域科学家和机器学习专家之间的协作实例标记和机器学习训练与部署;4)分析和挖掘合作者收集的数据工作流,以提高用户的工作效率,制定新的数据分析任务。开发的技术将为使用机器学习技术的可扩展数据分析日益重要的领域带来许多基于云的协作服务的成功。这些解决方案将显著降低进入门槛,使特定领域的分析师(而不是经过计算机科学培训的大数据专家)能够高效、有效地收集和交互分析不同领域的大量数据。该奖项反映了美国国家科学基金会的法定使命,并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

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Where Does Your News Come From? Predicting Information Pathways in Social Media
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Wei Wang其他文献

A High-Performance Isolated High-Frequency Converter With Optimal Switch Impedance
具有最佳开关阻抗的高性能隔离式高频转换器
Cambrian magmatic flare-up, central Tibet: Magma mixing in proto-Tethyan arc along north Gondwanan margin
西藏中部寒武纪岩浆爆发:沿冈瓦南边缘北缘的原特提斯弧中岩浆混合
  • DOI:
    10.1130/b35859.1
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    4.9
  • 作者:
    Peiyuan Hu;Qingguo Zhai;Peter A. Cawood;Guochun Zhao;Jun Wang;Yue Tang;Zhicai Zhu;Wei Wang;Hao Wu
  • 通讯作者:
    Hao Wu
Spatial resolution comparison of AC-SECM with SECM and their characterization of self-healing performance of hexamethylene diisocyanate trimer microcapsule coatings
AC-SECM与SECM的空间分辨率比较及其对六亚甲基二异氰酸酯三聚体微胶囊涂层自修复性能的表征
  • DOI:
    10.1039/c5ta00529a
  • 发表时间:
    2015-02
  • 期刊:
  • 影响因子:
    11.9
  • 作者:
    Wei Wang;Likun Xu;Huyuan Sun;Xiangbo Li;Shouhuan Zhao;Weining Zhang
  • 通讯作者:
    Weining Zhang
Application of machine learning algorithms in lane-changing model for intelligent vehicles exiting to off-ramp
机器学习算法在智能车辆驶出匝道换道模型中的应用
  • DOI:
    10.1080/23249935.2020.1746861
  • 发表时间:
    2020-04
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Changyin Dong;Hao Wang;Ye Li;Xiaomeng Shi;Daiheng Ni;Wei Wang
  • 通讯作者:
    Wei Wang
Financial development and wage income: Evidence from the global football market
金融发展与工资收入:来自全球足球市场的证据
  • DOI:
    10.1016/j.jbankfin.2023.106813
  • 发表时间:
    2023-02
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Wei Wang;Haoxi Yang;Xi Wang
  • 通讯作者:
    Xi Wang

Wei Wang的其他文献

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{{ truncateString('Wei Wang', 18)}}的其他基金

CAREER: Harnessing the Interplay of Morphology, Viscoelasticity, and Surface-Active Agents to Modulate Soft Wetting
职业:利用形态、粘弹性和表面活性剂的相互作用来调节软润湿
  • 批准号:
    2336504
  • 财政年份:
    2024
  • 资助金额:
    $ 45.5万
  • 项目类别:
    Continuing Grant
An Educational Tool for Teaching and Learning Concurrent Computer Programming Techniques
用于教授和学习并行计算机编程技术的教育工具
  • 批准号:
    2215359
  • 财政年份:
    2022
  • 资助金额:
    $ 45.5万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF: Small: Exploiting Performance Correlations for Accurate and Low-cost Performance Testing for Serverless Computing
协作研究:SHF:小型:利用性能相关性对无服务器计算进行准确且低成本的性能测试
  • 批准号:
    2155096
  • 财政年份:
    2022
  • 资助金额:
    $ 45.5万
  • 项目类别:
    Standard Grant
Collaborative Research: EAGER: Enhancing Security and Privacy of Augmented Reality Mobile Applications through Software Behavior Analysis
合作研究:EAGER:通过软件行为分析增强增强现实移动应用程序的安全性和隐私性
  • 批准号:
    2221843
  • 财政年份:
    2022
  • 资助金额:
    $ 45.5万
  • 项目类别:
    Standard Grant
PIPP Phase I: An End-to-End Pandemic Early Warning System by Harnessing Open-source Intelligence
PIPP 第一阶段:利用开源情报的端到端流行病预警系统
  • 批准号:
    2200274
  • 财政年份:
    2022
  • 资助金额:
    $ 45.5万
  • 项目类别:
    Standard Grant
Enhancing Programming and Machine Learning Education for Students with Visual Impairments through the Use of Compilers, AI and Cloud Technologies
通过使用编译器、人工智能和云技术加强对视力障碍学生的编程和机器学习教育
  • 批准号:
    2202632
  • 财政年份:
    2022
  • 资助金额:
    $ 45.5万
  • 项目类别:
    Standard Grant
Collaborative Research: A Bioinspired Approach towards Sustainable Membranes for Resilient Brine Treatment
合作研究:用于弹性盐水处理的可持续膜的仿生方法
  • 批准号:
    2226501
  • 财政年份:
    2022
  • 资助金额:
    $ 45.5万
  • 项目类别:
    Standard Grant
RAPID: Dynamic Graph Neural Networks for Modeling and Monitoring COVID-19 Pandemic
RAPID:用于建模和监测 COVID-19 大流行的动态图神经网络
  • 批准号:
    2031187
  • 财政年份:
    2020
  • 资助金额:
    $ 45.5万
  • 项目类别:
    Standard Grant
Collaborative Research; RUI: Non-Orthogonal Multiple Access Pricing for Wireless Multimedia Communications
合作研究;
  • 批准号:
    2010284
  • 财政年份:
    2020
  • 资助金额:
    $ 45.5万
  • 项目类别:
    Standard Grant
SusChEM: Direct functionalization of aldehydes enabled by aminocatalysis
SusChEM:通过氨基催化实现醛的直接官能化
  • 批准号:
    1903983
  • 财政年份:
    2019
  • 资助金额:
    $ 45.5万
  • 项目类别:
    Continuing Grant

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