TsOutlier: Explaining Outliers with Uniform Profiles over IoT Data

Abstract

IoT data with timestamps are often found with outliers, such as GPS trajectories or sensor readings. While existing systems mostly focus on detecting temporal outliers without explanations, a decision maker may be more interested in the cause of the outlier appearance such that subsequent actions would be taken, e.g., cleaning unreliable readings or repairing broken devices. Such outlier detection and explanation are expected to be performed in either offline (batch) or online modes (over streaming IoT data with timestamps). In this work, we present TsOutlier, a new prototype system for detecting outliers with explanations over IoT data. The framework defines uniform profiles to explain the outliers detected by various algorithms, including the outliers with variant time intervals. Both batch and streaming processing are supported in a uniform framework. In particular, by varying the block size, it provides a tradeoff between computing the accurate results and approximating with efficient incremental computation. In this paper, we present several case studies of applying TsOutlier in industry, e.g., how this framework works in detecting outliers over the operation data of Shanghai Subway, and how to get reasonable explanations for the detected outliers in tracking excavators.