CIF21 DIBBs: EI: mProv: Provence-Based Data Analytics Cyberinfrastructure for High-frequency Mobile Sensor Data

该项目解决了一个快速增长的机会：研究界使用高频移动的传感器数据的能力。 移动的传感器（嵌入在手机、车辆、可穿戴设备和环境中）持续捕获非常详细的数据，并有可能解决一系列科学和工程领域的问题。 这项工作的重点是一个特定的情况下-健康数据-建立在美国国立卫生研究院（NIH）赞助的项目中开发的几个功能，用于收集和分析通过移动的传感器和应用程序收集的健康数据。 提高噪音极大的分布式数据的有用性可以服务于许多社区，并且这些组件可以扩展到人类健康领域之外。 移动的传感器带来了一系列独特的数据挑战：数据数量和质量波动，不确定性可能很高。 在这种嘈杂的数据上建立出处是一个挑战，并且在获得人类受试者的数据方面存在限制。 该项目解决了与移动的传感器数据相关的几个独特的挑战。 可变性是通过提供详细的注释与元数据（如出处和质量），并通过提供设施的上下文特定的推理元数据。 系统捕获来源元数据沿着流中的数据，并将该信息随同派生数据从一个阶段传播到下一个阶段。 这创建了网络基础设施，使得可以“重放”具有不同配置的移动终端数据，以比较基准两种算法或诊断错误输出。 该项目建立在NIH资助的移动的传感器数据到知识（MD 2K）卓越中心的能力和成功的基础上，该中心提供了一个开源的网络基础设施，能够收集，策划，分析，可视化和解释高频移动的传感器数据。 利用他人收集的移动的传感器数据进行研究仍然具有挑战性;该项目开发了一个配套的开源出处网络基础设施，促进了移动的传感器数据本身的共享。 结果包括元数据标准，接口和运行时支持注释数据流与源（传感器，位置，采样率，连续或偶发），输出的语义（数量，概率，类），出处（功能，决策规则）和验证（特异性，灵敏度，基准使用）。 该基础设施可容纳各种数据类型，并支持第三方研究人员进行数据发现、分析、可视化、集成和验证。该项目提高了更广泛的科学和工程界使用移动的传感系统和元数据的能力，并在卫生和健康方面产生了直接、切实的社会效益。 该奖项由高级网络基础设施部门联合支持，由NSF计算机信息科学工程理事会（计算机和网络系统部门，信息和智能系统部门）。

This project addresses a rapidly growing opportunity: the ability of the research community to use high-frequency mobile sensor data. Mobile sensors (embedded in phones, vehicles, wearables, and the environment) continuously capture data in great detail, and have the potential to address problems in a range of scientific and engineering domains. This effort focuses upon a specific case -- health data -- that builds upon several capabilities developed in National Institutes of Health (NIH) sponsored projects for assembling and analyzing health data collected through mobile sensors and apps. Improvements to the usefulness of extremely noisy, distributed data can serve many communities, and the components are extensible outside the human health domain. Mobile sensors present a distinct set of data challenges: the data quantity and quality fluctuate, and uncertainty can be high. Establishing provenance on such noisy data is a challenge, and there are limitations on access to data from human subjects. This project addresses several of the distinctive challenges associated with mobile sensor data. Variability is addressed by providing detailed annotation with metadata (such as provenance and quality), and by providing facilities for context-specific reasoning about the metadata. The system captures provenance metadata along with data in a stream, and propagates this information alongside derived data from one stage to the next. This creates cyberinfrastructure that makes it possible to 'replay' mobile device data with different configurations, to comparatively benchmark two algorithms or to diagnose erroneous output. The project builds upon the capabilities and success of the NIH-funded Center of Excellence in Mobile Sensor Data to Knowledge (MD2K), which provides an open-source cyberinfrastructure enabling the collection, curation, analysis, visualization, and interpretation of high-frequency mobile sensor data. Conducting research with mobile sensor data collected by others continues to be challenging; this project develops a companion open-source provenance cyberinfrastructure, facilitating the sharing of the mobile sensor data itself. Results include metadata standards, interfaces, and runtime support for annotating data streams with the source (sensor, location, sampling rate, continuous or episodic), semantics of output (number, probability, class), provenance (features, rules for decision), and validation (specificity, sensitivity, benchmark used). The infrastructure accommodates a wide variety of data types and enables data discovery, analytics, visualization, integration, and validation by third party researchers. The project improves the ability of the wider scientific and engineering community to use mobile sensing systems and metadata, and it also has immediate, tangible societal benefits in health and wellness. This award by the Advanced Cyberinfrastructure Division is jointly supported by the NSF Directorate for Computer & Information Science & Engineering (Division of Computer and Network Systems, and Division of Information and Intelligent Systems).