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依赖于来自真实网络的高质量标签数据的可用性。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担忧。不幸的是，由于缺乏通过当今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使用第三方收集和管理的数据或从这些数据中受益。因此，目前实施的互联网数据科学不适合应用如(I)高质量的数据标记，(Ii)对学习模型等研究人工制品的严格评估，以及(Iii)报告研究成果的独立验证/重现性。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，Inc.的研究人员，并将在三个方面研究创新的协作数据标记和知识共享框架。首先，该项目将研究一种新的代码到数据的方法，该方法需要共享运营商领域知识的编程表示法，以确定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，使互联网数据科学成为一项全面的合作努力。第三，该项目将展示拟议框架在两所参与大学(UO和UCSB)之间合作努力的背景下的能力，并展示其扩展到任何数量参与者的能力。由此产生的框架将成为推动新兴互联网数据科学领域合作努力的推动力。除了确定ML在网络中应该如何使用的一些根本变化外，研究结果还将使工业界和学术界受益，并将确保未来的劳动力拥有适当的培训，以充分利用ML的应用来解决特定于网络的问题。该项目将维护以下网页：https://onrg.gitlab.io/projects/emerge.html.This奖反映了国家科学基金会的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.