NSF Convergence Accelerator Track D: The Data Hypervisor: Orchestrating Data and Models

NSF融合加速器支持以使用为灵感，以团队为基础，多学科的努力，解决国家重要性的挑战，并将在不久的将来产生对社会有价值的可交付成果。这个项目，NSF融合加速器-轨道D：数据管理程序：编排数据和模型，将设计和实现数据站——一个新的架构，其中数据和衍生数据产品都是密封的，任何人都不能直接看到或下载。在数据站体系结构中，计算是针对数据进行的，而不是像传统的数据湖和数据仓库那样将数据提供给用户。共享数据和模型对从医学成像到自然语言理解的科学问题产生了变革性的影响。尽管有潜在的好处，学术界和工业界的许多研究人员都不愿意将数据集中并共享给内部和外部的研究人员。今天的组织必须驾驭复杂的法规考虑和保护知识产权，同时在记录和维护数据方面进行重大的技术投资。该数据站将简化对敏感数据的访问，协助数据发现和集成，并促进任意数据访问和治理政策的实施。该项目将与生物医学、材料科学和企业数据管理方面的合作伙伴合作，以建立能力并证明数据站架构的概念。在先前数据系统研究的基础上，data Station将引入新的数据不感知任务胶囊，使用户能够指定数据驱动的任务，如传统的数据查询和机器学习模型训练，而无需用户直接访问数据本身。编程接口为数据站传递足够的信息，以触发发现潜在的相关数据集；对这些数据集进行整合和修剪以进行计算；并通过执行任务来计算结果。实际上，数据站通过对数据进行计算来颠覆传统的数据查询建模。任务胶囊还包括一个用户定义的度量，用于从用户的角度确定哪些结果是有用的，以及需要满足哪些信任约束来验证输入数据集的来源。每次创建派生数据产品时，Data Station都会捕获元数据，并提供一组原语，以实现处理数据贡献者用例所需的各种数据治理和数据访问策略。只有经过授权的用户才能基于data Station实现的一种新颖的访问令牌模型访问数据，该模型允许细粒度但可扩展的访问控制。必须显式地授权用户通过从数据贡献者获得的令牌访问结果。该数据站项目将与材料科学、生物医学和企业领域的各种合作伙伴合作，以帮助设计和应用数据站的各种用例。一项教育计划将让高中生、本科生和研究生参与研究、开发和评估这个数据站。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. This project, NSF Convergence Accelerator–Track D: The Data Hypervisor: Orchestrating Data and Models, will design and implement the Data Station—a new architecture where both data and derived data products are sealed and cannot be directly seen or downloaded by anyone. In the Data Station architecture, computation is brought to the data, rather than data being brought to users, as is common in traditional data lakes and warehouses. Sharing data and models has had a transformative impact on scientific problems from medical imaging to natural language understanding. Despite the potential upside, many researchers in both academia and industry are reluctant to centralize and share data to both internal and external researchers. Organizations today have to navigate complex regulatory considerations and protect intellectual property while incurring a significant technical investment in documenting and maintaining data. The Data Station will ease access to sensitive data, assist with data discovery and integration, and facilitate enforcement of arbitrary data access and governance policies. The project will work with partners in biomedicine, materials science, and enterprise data management to establish the capabilities and prove the concepts of the Data Station architecture.While building upon prior research in data systems, Data Station will introduce novel data-unaware task capsules that enable users to specify data-driven tasks such as traditional data queries and machine learning model training without the user requiring direct access to the data itself. The programming interfaces convey sufficient information for the Data Station to trigger the discovery of potentially relevant datasets; integrate and prune those datasets for computation; and compute the results by executing the task. In effect, Data Station inverts the traditional data querying modeling by bringing computations to the data. Task capsules also include a user-defined metric for determining what results are useful from the user’s perspective as well as which trust constraints need to be met to validate the provenance of input datasets. The Data Station captures metadata every time a derived data product is created and provides a set of primitives to implement various data governance and data access policies necessary to address data contributor use cases. Only authorized users are able to access the data based on a novel access-token model implemented by Data Station that permits fine-grained yet scalable access control. Users must explicitly be authorized to access results via tokens obtained from data contributors. The Data Station project will engage a diverse set of partners in materials science, biomedicine, and enterprise scenarios to help design and apply the Data Station to various use cases. An education program will engage high school, undergraduate, and graduate students in researching, developing, and evaluating the Data Station.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.