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

• 批准号: 2106859
• 负责人: Wei Wang
• 金额: $ 45.5万
• 依托单位: University of California-Los Angeles
• 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
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106859&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Machine

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，我们的社会享有在线协作和共享的巨大价值，这是由Google Docs，Dropbox，Github和Overleaf等流行的基于云的服务所证明的。由于前所未有的COVID-19，由于新工作的新规范，这些好处变得更加有吸引力。在此奖项中，调查人员想回答以下问题：是否可以开发在线系统来支持基于云的协作数据分析的服务？该计算范式使合作者可以在大量数据上共同执行分析工作。 研究者团队对合作者来自具有不同背景的多个学科的场景特别感兴趣，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。尽管数据分析的合作者希望专注于他们的研究主题并充分利用其专业知识和技能，但由于其互补背景和异步工作时间表，他们也面临着挑战。 结果，该协作既有跨学科的障碍，也存在科明障碍。该奖项的目的是研究这些挑战并开发新技术，以支持这种新颖的在线服务以支持协作数据分析。 研究人员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定有条件的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范围的； 2）启用外部用户定义功能的协作调试，不仅可以利用Python和R中的流行数据科学库，而且还使用经常用其他语言（例如Java和Scala）编写的并行数据处理引擎来实现高性能； 3）支持域科学家与机器学习专家之间的协作实例标签和机器学习培训和部署； 4）分析和挖掘从合作者那里分析收集的数据工作流程，以提高用户生产率以制定新的数据分析任务。开发的技术将使许多基于云的协作服务的成功进入使用机器学习技术越来越重要的可扩展数据分析的空间。这些解决方案将大大降低进入特定领域的分析师（而不是由计算机科学培训的大数据专家）收集，有效，有效，互动分析不同领域中的大量数据。该奖项的奖项反映了NSF的法规和经过评估的支持者，该奖项反映了该奖项的知识群体和众所周知的基础。

项目成果

Scalable Graph Representation Learning via Locality-Sensitive Hashing

• DOI: 10.1145/3511808.3557689
• 发表时间: 2022-01-01
• 期刊: PROCEEDINGS OF THE 31ST ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, CIKM 2022
• 作者: Chen, Xiusi;Jiang, Jyun-Yu;Wang, Wei
• 通讯作者: Wang, Wei

Where Does Your News Come From? Predicting Information Pathways in Social Media

• DOI: 10.1145/3539618.3592087
• 发表时间: 2023-07
• 期刊: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
• 作者: Alexander K. Taylor;Nuan Wen;Po-Nien Kung;Jiaao Chen;Violet Peng;W. Wang