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

近年来，我们的社会享受到了在线协作和共享的巨大价值，流行的基于云的服务，如Google Docs、Dropbox、GitHub和Over就是明证。由于史无前例的新冠肺炎疫情引发的远程工作新常态，这些好处变得更加有吸引力。在这个奖项中，研究人员想要回答以下问题：有可能开发在线系统来支持基于云的协作数据分析服务吗？这种计算范例允许合作者联合对大量数据进行分析工作。研究人员团队对合作者来自多个学科、具有不同背景的场景特别感兴趣，并且分析以机器学习为中心，因为此类任务正变得越来越常见和重要。虽然数据分析领域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，但由于他们的背景互补和工作日程不同步，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，并开发新的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)允许协作调试外部用户定义的函数，以便不仅利用流行的Python和R数据科学库，而且使用通常用Java和Scala等其他语言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培训和部署；以及4)分析和挖掘从协作者那里收集的数据工作流，以提高用户的工作效率，以制定新的数据分析任务。开发的技术将把许多基于云的协作服务的成功带到使用机器学习技术的可扩展数据分析这一日益重要的领域。这些解决方案将显著降低进入门槛，使特定领域的分析师--而不是受过计算机科学培训的大数据专家--能够收集并高效、有效地和交互地分析不同领域的大量数据。这一奖项反映了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。

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.