OAC Core: Small: Collaborative Research: Data Provenance Infrastructure towards Robust and Reliable Data Sharing and Analytics

互连设备和系统数量的迅速增加为研究人员、业务合作伙伴和医疗保健组织之间带来了前所未有的合作机会，可以扩展发现，而不仅仅是单一研究的结果。例如，不同医疗组织之间的数据共享可以通过对来自多个组织的各种试验的结果进行轮询来加强对个体临床结果的理解，从而能够扩展对治疗方案的分析并加速生物医学研究。从各种来源收集的大量数据给我们带来好处的同时，由于来自各种来源的不同数据的集成，它也带来了确保数据可信度和质量的重要挑战。此外，故障的、配置不正确的或损坏的传感器，以及错误的或损坏的数据处理单元，都可能严重影响数据质量和分析结果。该项目建议开发一个基础设施，为协作数据共享和分析提供健壮的、细粒度的和端到端来源。本研究的结果将直接作为可信数据共享和分析基础设施的基础，通过为各种通信基础设施创建细粒度端到端数据来源框架，为共享数据提供鲁棒性和攻击/故障弹性的细粒度来源。在这个项目中，pi将开发一个端到端数据来源框架，为可信的数据共享和分析基础设施提供健壮和细粒度的数据沿袭。首先，他们将开发一个可扩展和可靠的基础设施，用于收集分布式互联设备的数据来源，该设备可以在分布式设备的各种环境（例如，使用不同硬件和软件平台的设备）中获得简明的来源数据。接下来，他们将设计并实现一个框架，以便为数据共享、处理和分析提供适当的派生和传播细粒度来源记录。当数据被聚合、分析和处理（例如合并、分割、复制、删除、提取或统计分析）时，它将提供溯源跟踪和溯源记录处理服务。pi还计划开发一种系统，可以分析和可视化数据的完整血统，以衡量数据质量，并进行各种根本原因分析，以确定数据质量问题背后的根本原因。该系统将能够处理跨多个设备在很长一段时间内生成的大量数据。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 rapidly increasing number of interconnected devices and systems brings unprecedented collaborative opportunities between researchers, business partners, and healthcare organizations that can extend discoveries beyond those derivable from any single study. For instance, data sharing between various medical organizations can enhance understanding of the results from an individual clinical by polling of results from various trials from multiple organizations, and thus it enables extending the analysis of treatment options and accelerating biomedical research. While a vast amount of data collected from various sources brings us benefits, it imposes, at the same time, an important challenge of ensuring trustworthiness and quality of data due to the integration of disparate data from various sources. Furthermore, faulty, improperly configured, or broken sensors, as well as buggy or compromised data processing units, can severely affect the quality of data and the analyzed results. This project proposes to develop an infrastructure that provides robust, fine-grain, and end-to-end provenance for collaborative data sharing and analytics. The outcome of this research will directly serve as the foundation of trustworthy data sharing and analytic infrastructures by providing robust and attack/fault-resilient fine-grain provenance of the shared data by creating fine-grain end-to-end data provenance framework for diverse communication infrastructures.In this project, the PIs will develop an end-to-end data provenance framework that provides robust and fine-grain data lineage for trustworthy data sharing and analytic infrastructures. First, they will develop a scalable and reliable infrastructure for collecting data provenance for distributed interconnected devices that can derive a concise provenance data in various environments of distributed devices (e.g., devices using diverse hardware and software platforms). Next, they will design and implement a framework to enable proper derivation and propagation of fine-grain provenance records for data sharing, processing, and analytics. It will provide services for provenance tracking as well as provenance record processing when the data are aggregated, analyzed, and processed (e.g., merge, split, duplicate, delete, extract, or statistical analytics). The PIs also plan to develop a system that can analyze and visualize the complete lineage of data to measure the quality of data and conduct various root cause analyses to identify the fundamental reasons behind data quality issues. The system will be capable of handling a large amount of data generated over a long period of time across multiple devices.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.