Event sequence data record series of discrete events in the time order of occurrence. They
are commonly observed in a variety of applications ranging from electronic health records to …

Research on intelligent vehicles has been popular in the past decade. To fill the gap
between automatic approaches and man-machine control systems, it is indispensable to …

There is a need for solutions which assist users to understand long time-series data by
observing its changes over time, finding repeated patterns, detecting outliers, and effectively …

We propose InSituNet, a deep learning based surrogate model to support parameter space
exploration for ensemble simulations that are visualized in situ. In situ visualization …

One major cause of performance degradation in predictive models is that the test samples
are not well covered by the training data. Such not well-represented samples are called OoD …

In this paper, we develop a visual analysis method for interactively improving the quality of
labeled data, which is essential to the success of supervised and semi-supervised learning …

Detecting anomalies in time-varying multivariate data is crucial in various industries for the
predictive maintenance of equipment. Numerous machine learning (ML) algorithms have …

Over the past years, an increasing number of publications in information visualization,
especially within the field of visual analytics, have mentioned the term “embedding” when …

Distributed tracing tools have emerged in recent years to enable operators of modern
internet applications to troubleshoot cross-component problems in deployed applications …

Detecting and analyzing potential anomalous performances in cloud computing systems is
essential for avoiding losses to customers and ensuring the efficient operation of the …