Interactive Context-Aware Anomaly Detection Guided by User Feedback

Automatic anomaly detection techniques have been extensively used to support decision making in abnormal situations. However, existing approaches are limited in their capacity of effectively identifying anomalies due to the complexity of the real-world environment, the uncertainty of the data input, and the unavailability of ground truth. In this paper, we propose an interactive context-aware anomaly detection algorithm framework that incorporates human judgment in searching for anomalous regions within a large geographic environment. In specific, our framework, 1) estimates a focal region and detect anomalous situations in real time, through which the user can observe and analyze suspicious entities, 2) leverages user feedback to refine results and guide further analysis, and 3) tolerates potential fault feedback provided by the users and resignal dubious anomalous points. Based on the framework, we propose two algorithm implementations, respectively, employ Bayes’ theorem and metric learning. We demonstrate the effectiveness of the proposed framework and corresponding implementations through two controlled user studies and a case study with a domain expert.