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

NSF 融合加速器轨道 D：数据管理程序：编排数据和模型

• 批准号: 2040718
• 负责人: Ian Foster
• 金额: $ 95.46万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040718&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Track; Data

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.

NSF融合加速器支持以团队为基础的多学科努力，这些努力应对国家重要性的挑战，并将在不久的将来为社会带来价值的可交付成果。该项目，NSF Convergence Accelerator – Track D：数据管理程序：协调数据和模型，将设计和实施数据站，这是一个新的体系结构，其中数据和派生的数据产品均被密封，并且任何人都无法直接看到或下载。在数据站体系结构中，将计算带入数据，而不是将数据带给用户，就像传统数据湖泊和仓库中一样。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。尽管有潜在的上行空间，但许多学术界和工业的研究人员都不愿意将数据集中并分享与内部和外部研究人员。今天的组织必须在复杂的监管方面进行浏览并保护知识产权，同时在记录和维护数据时产生大量技术投资。数据站将简化访问敏感数据，协助数据发现和集成，并支持执行任意数据访问和治理策略的执行。该项目将与生物医学，材料科学和企业数据管理领域的合作伙伴合作，以建立功能并证明数据站体系结构的概念。尽管在数据系统中的先前研究基础上，数据站将介绍新颖的数据态度任务胶囊，使用户能够使用户能够指定诸如传统数据Queries和机器学习模型访问的数据驱动的任务，而无需访问用户培训。编程接口为数据站传达了足够的信息，以触发潜在相关数据集的发现；集成并修剪这些数据集以进行计算；并通过执行任务来计算结果。实际上，数据站通过将计算带入数据来反转传统的数据查询建模。任务捕获还包括一个用户定义的度量标准，用于从用户的角度来确定哪些结果有用，以及需要满足哪些信任约束来验证输入数据集的出处。每次创建派生的数据产品时，数据站都会捕获元数据，并提供一组原语，以实施各种数据治理和数据访问策略，以解决数据贡献者用例。只有授权用户才能基于数据站实施的新颖访问模型访问数据，该模型允许允许细粒度但可扩展的访问控制。必须明确授权用户通过从数据贡献者那里获得的令牌访问结果。数据站项目将与材料科学，生物医学和企业方案的潜水伙伴互动，以帮助设计和应用数据站到各种用例中。一项教育计划将吸引高中，本科和研究生研究数据站研究，开发和评估数据站。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，通过评估被认为是珍贵的支持。

项目成果

Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D

• DOI: 10.1002/aaai.12042
• 发表时间: 2022-03-01
• 期刊: AI MAGAZINE
• 影响因子: 0.9
• 作者: Baru, Chaitanya;Pozmantier, Michael;Zhang, Peng
• 通讯作者: Zhang, Peng

Ver: View Discovery in the Wild

• DOI: 10.1109/icde55515.2023.00045
• 发表时间: 2021-06
• 期刊: 2023 IEEE 39th International Conference on Data Engineering (ICDE)
• 作者: Yue Gong;Zhiru Zhu;Sainyam Galhotra;R. Fernandez

Data-Sharing Markets: Model, Protocol, and Algorithms to Incentivize the Formation of Data-Sharing Consortia

数据共享市场：激励数据共享联盟形成的模型、协议和算法

• 发表时间: 2023
• 期刊: Proceedings ACMSIGMOD International Conference on Management of Data
• 作者: Raul Castro Fernandez

Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow

• DOI: 10.14778/3551793.3551861
• 发表时间: 2022-07
• 期刊: ArXiv
• 作者: Siyuan Xia;Zhiru Zhu;Chris Zhu;Jinjin Zhao;K. Chard;Aaron J. Elmore;Ian D. Foster;Michael

Protecting Data Markets from Strategic Buyers

保护数据市场免受战略买家的侵害

• 发表时间: 2023
• 期刊: Proceedings ACMSIGMOD International Conference on Management of Data
• 作者: Raul Castro Fernandez