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

快速增长的互联设备和系统在研究人员、业务合作伙伴和医疗保健组织之间带来了前所未有的协作机会，可以将发现扩展到任何单一研究得出的发现之外。例如，不同医疗组织之间的数据共享可以通过轮询来自多个组织的各种试验的结果来增强对单个临床结果的理解，从而使其能够扩展治疗选择的分析并加速生物医学研究。虽然从不同来源收集的海量数据给我们带来了好处，但同时，由于整合了来自不同来源的不同数据，它也给确保数据的可信性和质量带来了重大挑战。此外，有故障、配置不当或损坏的传感器，以及出现故障或受损的数据处理单元，都会严重影响数据质量和分析结果。该项目建议开发一种基础设施，为协作数据共享和分析提供可靠、细粒度和端到端的来源。这项研究的成果将直接作为可信数据共享和分析基础设施的基础，通过为不同的通信基础设施创建细粒度的端到端数据起源框架，提供健壮的和具有攻击/故障弹性的共享数据的细粒度起源。在这个项目中，PI将开发一个端到端的数据起源框架，为可信的数据共享和分析基础设施提供稳健和细粒度的数据谱系。首先，他们将开发一个可扩展和可靠的基础设施，用于为分布式互联设备收集数据来源，该基础设施可以在分布式设备(例如，使用不同硬件和软件平台的设备)的各种环境中得出简明的来源数据。接下来，他们将设计和实现一个框架，以支持为数据共享、处理和分析适当地派生和传播细粒度的起源记录。当数据被汇总、分析和处理(例如，合并、拆分、复制、删除、提取或统计分析)时，它将为来源跟踪以及来源记录处理提供服务。投资促进局还计划开发一个系统，该系统可以分析和显示完整的数据谱系，以衡量数据的质量，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。该系统将能够处理在多个设备上长时间产生的大量数据。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.