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.

Event sequence data such as electronic health records, a person's academic records, or car service records, are ordered series of events which have occurred over a period of time. Analyzing collections of event sequences can reveal common or semantically important sequential patterns. For example, event sequence analysis might reveal frequently used care plans for treating a disease, typical publishing patterns of professors, and the patterns of service that result in a well-maintained car. It is challenging, however, to visually explore large numbers of event sequences, or sequences with large numbers of event types. Existing methods focus on extracting explicitly matching patterns of events using statistical analysis to create stages of event progression over time. However, these methods fail to capture latent clusters of similar but not identical evolutions of event sequences. In this paper, we introduce a novel visualization system named EventThread which clusters event sequences into threads based on tensor analysis and visualizes the latent stage categories and evolution patterns by interactively grouping the threads by similarity into time-specific clusters. We demonstrate the effectiveness of EventThread through usage scenarios in three different application domains and via interviews with an expert user.