New Paper at IEEE Big Data 2019
Guo et al. introduce new methods for visual anomaly detection.
A paper titled Visual Anomaly Detection in Event Sequence Data was introduced at IEEE Big Data 2019 in Los Angeles, Calafornia in December 2019. Lead author Shunan Guo and collaborators—including the VACLab’s David Gotz—describe an unsupervised anomaly detection algorithm based on Variational AutoEncoders (VAE). The paper also introduces a visualization system, EventThread3, designed to support interactive exploration of the analysis results. More details can be found in the paper which is available both through IEEE and as a open preprint on arXiv.
See the publication page for more details.