Cappiello Cinzia, Meroni Giovanni, Pernici Barbara, Plebani Pierluigi, Salnitri Mattia, Vitali Monica, Trojaniello Diana, Catallo Ilio, Sanna Alberto
Dip. Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy.
Center for Advanced Technology for Health and Wellbeing, IRCCS San Raffaele Hospital, Milan, Italy.
Front Robot AI. 2020 Sep 15;7:96. doi: 10.3389/frobt.2020.00096. eCollection 2020.
Pervasive sensing is increasing our ability to monitor the status of patients not only when they are hospitalized but also during home recovery. As a result, lots of data are collected and are available for multiple purposes. If operations can take advantage of timely and detailed data, the huge amount of data collected can also be useful for analytics. However, these data may be unusable for two reasons: data quality and performance problems. First, if the quality of the collected values is low, the processing activities could produce insignificant results. Second, if the system does not guarantee adequate performance, the results may not be delivered at the right time. The goal of this document is to propose a data utility model that considers the impact of the quality of the data sources (e.g., collected data, biographical data, and clinical history) on the expected results and allows for improvement of the performance through utility-driven data management in a Fog environment. Regarding data quality, our approach aims to consider it as a context-dependent problem: a given dataset can be considered useful for one application and inadequate for another application. For this reason, we suggest a context-dependent quality assessment considering dimensions such as accuracy, completeness, consistency, and timeliness, and we argue that different applications have different quality requirements to consider. The management of data in Fog computing also requires particular attention to quality of service requirements. For this reason, we include QoS aspects in the data utility model, such as availability, response time, and latency. Based on the proposed data utility model, we present an approach based on a goal model capable of identifying when one or more dimensions of quality of service or data quality are violated and of suggesting which is the best action to be taken to address this violation. The proposed approach is evaluated with a real and appropriately anonymized dataset, obtained as part of the experimental procedure of a research project in which a device with a set of sensors (inertial, temperature, humidity, and light sensors) is used to collect motion and environmental data associated with the daily physical activities of healthy young volunteers.
普适传感不仅增强了我们在患者住院期间，还在其居家康复期间监测患者状态的能力。因此，收集了大量数据，可用于多种目的。如果运营能够利用及时且详细的数据，那么所收集的海量数据对于分析也可能是有用的。然而，这些数据可能由于两个原因而无法使用：数据质量和性能问题。首先，如果所收集值的质量较低，处理活动可能产生无足轻重的结果。其次，如果系统不能保证足够的性能，结果可能无法在正确的时间交付。本文档的目标是提出一种数据效用模型，该模型考虑数据源（例如收集的数据、传记数据和临床病史）的质量对预期结果的影响，并允许通过雾环境中基于效用的数据管理来提高性能。关于数据质量，我们的方法旨在将其视为一个依赖上下文的问题：给定的数据集对于一个应用可能被认为是有用的，而对于另一个应用则是不足的。因此，我们建议进行依赖上下文的质量评估，考虑准确性、完整性、一致性和及时性等维度，并且我们认为不同的应用有不同的质量要求需要考虑。雾计算中的数据管理还需要特别关注服务质量要求。因此，我们在数据效用模型中纳入了QoS方面，例如可用性、响应时间和延迟。基于所提出的数据效用模型，我们提出一种基于目标模型的方法，该方法能够识别服务质量或数据质量的一个或多个维度何时被违反，并能建议采取哪种最佳行动来解决这种违反情况。所提出的方法使用一个真实且经过适当匿名处理的数据集进行评估，该数据集是一个研究项目实验过程的一部分，在该项目中，使用一个带有一组传感器（惯性、温度、湿度和光传感器）的设备来收集与健康年轻志愿者日常身体活动相关的运动和环境数据。
