In recent years, visual analytics has taken an important role in solving many types of complex analytical problems that require deep and specific domain knowledge from users. While the analysis products generated by these ...

Line-chart visualizations of temporal data enable users to identify interesting patterns for the user to inquire about. Using oracles, such as chat AIs, Visual Analytic tools can automatically uncover explicit knowledge ...

Cluster and outlier analysis are two important tasks. Due to their nature these tasks seem to be opposed to each other, i.e., data objects either belong to a cluster structure or a sparsely populated outlier region. In ...

For the automatic segmentation of multivariate time series domain experts at first need to consider a huge space of alternative configurations of algorithms and parameters. We assume that only a small subset of these ...

Approaches in Visual Analytics have so far been developed for a wide array of research areas, mainly with a focus on industrial or business applications. The field of linguistics, however, has only marginally incorporated ...

High-dimensional data poses a significant challenge for analysis, as patterns typically exist only in subsets of dimensions or records. A common approach to reveal patterns, such as meaningful structures or relationships, ...

For understanding the circumstances, causes, and consequences of events that may happen during movement (e.g., harsh brake, sharp turn), it is necessary to analyze event context. The context includes dynamic attributes of ...

Cryptocurrencies are a novel phenomenon in the finance world that is gaining more attention from the general public, banks, investors, and lately also academic research. A characteristic of cryptocurrencies is to be the ...

In this paper we present a simple, yet effective method to map data set values of different distributions to a color map in order to reveal interesting structures. We make use of an ordering and a simple projection technique ...

We present a new method for computing inverse projections from 2D spaces to arbitrary high-dimensional spaces. Given any projection technique, we train a deep neural network to learn a low-to-high dimensional mapping based ...