论文标题
基于集合方案的数据插补模型
A Data Imputation Model based on an Ensemble Scheme
论文作者
论文摘要
Edge Computing（EC）提供了一种基础架构，该基础架构充当云和物联网（IoT）之间的中介。目的是减少我们依靠云时所享有的延迟。物联网设备与他们的环境进行交互，以通过EC收集数据将它们传递到云。可以在EC上提供各种服务，以立即管理收集的数据。一项重要的任务是管理缺失值。在本文中，我们提出了一种基于合奏的数据插补方法，该方法考虑了收集的数据和报告设备的时空方面。我们建议依靠类似于报告丢失数据并增强其数据插图过程的设备的IoT设备组。我们不断推理报告的流的相关性，并有效地结合了可用的数据。我们的目的是“汇总”本地替代品与小组的“意见”的观点。我们采用众所周知的相似性技术和统计建模方法来提供最终结果。我们提供了模型的描述，并通过大量采用各种实验场景进行评估。
Edge Computing (EC) offers an infrastructure that acts as the mediator between the Cloud and the Internet of Things (IoT). The goal is to reduce the latency that we enjoy when relying on Cloud. IoT devices interact with their environment to collect data relaying them towards the Cloud through the EC. Various services can be provided at the EC for the immediate management of the collected data. One significant task is the management of missing values. In this paper, we propose an ensemble based approach for data imputation that takes into consideration the spatio-temporal aspect of the collected data and the reporting devices. We propose to rely on the group of IoT devices that resemble to the device reporting missing data and enhance its data imputation process. We continuously reason on the correlation of the reported streams and efficiently combine the available data. Our aim is to `aggregate' the local view on the appropriate replacement with the `opinion' of the group. We adopt widely known similarity techniques and a statistical modelling methodology to deliver the final outcome. We provide the description of our model and evaluate it through a high number of simulations adopting various experimental scenarios.
