Sensor data streams occur widely in various real-time applications in the context of the Internet of Things (IoT). However, sensor data streams feature missing values due to factors such as sensor failures, communication errors, or depleted batteries. Missing values can compromise the quality of real-time analytics tasks and downstream applications. Existing imputation methods either make strong assumptions about streams or have low efficiency. In this study, we aim to accurately and efficiently impute missing values in data streams that satisfy only general characteristics in order to benefit real-time applications more widely. First, we propose a message propagation imputation network (MPIN) that is able to recover the missing values of data instances in a time window. We give a theoretical analysis of why MPIN is effective. Second, we present a continuous imputation framework that consists of data update and model update mechanisms to enable MPIN to perform continuous imputation both effectively and efficiently. Extensive experiments on multiple real datasets show that MPIN can outperform the existing data imputers by wide margins and that the continuous imputation framework is efficient and accurate.

翻译：传感器数据流广泛应用于物联网环境下的各类实时应用中。然而，由于传感器故障、通信错误或电池耗尽等因素，传感器数据流常出现缺失值。这些缺失值会降低实时分析任务及下游应用的数据质量。现有填补方法要么对数据流特性做出较强假设，要么效率较低。本研究旨在仅利用数据流的通用特征，高效准确地填补缺失值，从而更广泛地服务于实时应用。首先，我们提出一种消息传播填补网络，可恢复时间窗口内数据实例的缺失值，并对其有效性进行了理论分析。其次，我们构建了一个包含数据更新与模型更新机制的连续填补框架，使消息传播填补网络能够高效持续地执行填补任务。在多个真实数据集上的大量实验表明，消息传播填补网络显著优于现有数据填补方法，且该连续填补框架兼具高效性与准确性。