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

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，正如流行的基于云的服务所证明的那样，如Google Chrome，Dropbox，GitHub和Overleaf。由于前所未有的新型冠状病毒肺炎（COVID-19，即2019冠状病毒病）大流行导致远程工作的新常态，这些福利变得更具吸引力。在这个奖项中，研究人员希望回答以下问题：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？这种计算模式允许协作者联合对大量数据进行分析。 研究人员团队特别感兴趣的场景是，合作者来自不同背景的多个学科，并且分析是以机器学习为中心的，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主题并充分利用他们的专业知识和技能，但由于他们的互补背景和异步工作时间表，他们也面临着挑战。 因此，这种合作既有学科间的障碍，也有学科内的障碍。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。 研究人员团队确定了四个独特的研究课题：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作调试，不仅可以利用Python和R中流行的数据科学库，而且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来实现高性能; 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和部署;以及4）分析和挖掘从协作者收集的数据工作流，以提高用户的生产力，从而制定新的数据分析任务。开发的技术将为许多基于云的协作服务带来成功，以利用机器学习技术进行日益重要的可扩展数据分析。这些解决方案将大大降低进入门槛，使特定领域的分析师-而不是计算机科学培训的大数据专家-能够收集并高效，有效，该奖项反映了NSF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进行评估，被认为值得支持的搜索.

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.