Visual Anomaly Detection in Event Sequence Data
This addresses the challenge of interpreting anomalies in sequential and temporal data for analysts, though it is incremental as it builds on existing VAE methods.
The paper tackles anomaly detection in event sequence data by proposing an unsupervised VAE-based algorithm to identify abnormal events based on occurrence probability violations, and introduces a visualization system, EventThread3, for interactive exploration, with quantitative evaluation and a case study demonstrating effectiveness.
Anomaly detection is a common analytical task that aims to identify rare cases that differ from the typical cases that make up the majority of a dataset. When applied to the analysis of event sequence data, the task of anomaly detection can be complex because the sequential and temporal nature of such data results in diverse definitions and flexible forms of anomalies. This, in turn, increases the difficulty in interpreting detected anomalies. In this paper, we propose an unsupervised anomaly detection algorithm based on Variational AutoEncoders (VAE) to estimate underlying normal progressions for each given sequence represented as occurrence probabilities of events along the sequence progression. Events in violation of their occurrence probability are identified as abnormal. We also introduce a visualization system, EventThread3, to support interactive exploration and interpretations of anomalies within the context of normal sequence progressions in the dataset through comprehensive one-to-many sequence comparison. Finally, we quantitatively evaluate the performance of our anomaly detection algorithm and demonstrate the effectiveness of our system through a case study.