CC* Integration-Large: Bringing Code to Data: A Collaborative Approach to Democratizing Internet Data Science

机器学习（ML）在网络问题中的成功应用取决于来自现实世界网络的高质量标记数据的可用性。同样重要的是共享这些数据集的能力，尊重数据所有者的隐私问题。不幸的是，缺乏通过今天普遍应用的数据到代码范式共享数据，研究人员缺乏一个系统的框架来处理或受益于第三方收集和整理的数据。因此，今天实践的互联网数据科学不适合以下应用：(i)高质量的数据标记，（ii）对研究工件（如学习模型）的严格评估，以及（iii）报告的研究结果的独立验证/可重复性。该合作项目汇集了来自俄勒冈大学、加州大学圣巴巴拉分校和NIKSUN公司的研究人员，并将分三个重点研究创新的协作数据标签和知识共享框架。首先，该项目将研究一种新的代码到数据的方法，该方法需要共享操作员领域知识的程序化表示，以识别数据中感兴趣的事件。其次，该项目将设计和开发一个新的学习框架，使互联网数据科学的追求成为一个成熟的协作努力。第三，该项目将说明在两所参与大学（UO和UCSB）合作努力的背景下提出的框架的能力，并展示其扩展到任何数量的参与者的能力。由此产生的框架将成为推动互联网数据科学新兴领域协作努力的推动力。除了确定机器学习在网络中应用的一些基本变化之外，研究结果将使工业界和学术界受益，并将确保未来的劳动力接受适当的培训，以充分利用机器学习在网络特定问题上的应用。此外，研究结果将促进运营商采用互联网数据科学工作路线图的发展，并在实际生产网络中部署随后的研究成果。该项目将保留以下网页：https://onrg.gitlab.io/projects/emerge.html.This该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，认为值得支持。

Successful application of machine learning (ML) for networking problems depends on the availability of high-quality labeled data from real-world networks. Equally critical is the ability to share these datasets, respecting the data owners' privacy concerns. Unfortunately, short of sharing the data via today’s commonly-applied data-to-code paradigm, researchers lack a systematic framework for working with or benefiting from data collected and curated by third parties. Consequently, Internet Data Science as practiced today is ill-suited for applications such as (i) high-quality data labeling, (ii) rigorous evaluation of research artifacts such as learning models, and (iii) independent validation/reproducibility of reported research findings.This collaborative project brings together researchers from University of Oregon, University of California-Santa Barbara, and NIKSUN, Inc., and will investigate an innovative collaborative data labeling and knowledge sharing framework in three thrusts. First, the project will investigate a novel code-to-data approach that entails sharing of programmatic representations of operators' domain knowledge to identify events of interest in the data. Second, the project will design and develop a new learning framework to enable the pursuit of Internet Data Science as a full-fledged collaborative effort. Third, the project will illustrate the capabilities of the proposed framework in the context of collaborative efforts between two participating universities (UO and UCSB) and demonstrate its ability to scale to any number of participants.The resulting framework will serve as a driving force for advancing collaborative efforts in the emerging area of Internet Data Science. In addition to identifying some of the fundamental changes to how ML ought to be used in networking, the research findings will benefit both industry and academia and will ensure that tomorrow's workforce has the proper training to fully exploit the application of ML for network-specific problems. Also, the outcomes will catalyze the development of a roadmap for the adoption of Internet Data Science efforts by operators and the deployment of ensuing research artifacts in real-world production networks.This project will maintain the following webpage: https://onrg.gitlab.io/projects/emerge.html.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.