On-Tube Attribute Visualization for Multivariate Trajectory Data.

Stylized tubes are an established visualization primitive for line data as encountered in many scientific fields, ranging from characteristic lines in flow fields, fiber tracks reconstructed from diffusion tensor imaging, to trajectories of moving objects as they arise from cyber-physical systems in many engineering disciplines. Typical challenges include large data set sizes demanding for efficient rendering techniques as well as a large number of attributes that cannot be mapped simultaneously to the basic visual attributes provided by a tube-based visualization. In this work, we tackle both challenges with a new on-tube visualization approach.

BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations.

We developed and validated a rating scale to assess the aesthetic pleasure (or beauty) of a visual data representation: the BeauVis scale. With our work we offer researchers and practitioners a simple instrument to compare the visual appearance of different visualizations, unrelated to data or context of use. Our rating scale can, for example, be used to accompany results from controlled experiments or be used as informative data points during in-depth qualitative studies.

Visual Analysis of Hyperproperties for Understanding Model Checking Results.

Model checkers provide algorithms for proving that a mathematical model of a system satisfies a given specification. In case of a violation, a counterexample that shows the erroneous behavior is returned. Understanding these counterexamples is challenging, especially for hyperproperty specifications, i.

Responsive Matrix Cells: A Focus+Context Approach for Exploring and Editing Multivariate Graphs.

Matrix visualizations are a useful tool to provide a general overview of a graph's structure. For multivariate graphs, a remaining challenge is to cope with the attributes that are associated with nodes and edges. Addressing this challenge, we propose responsive matrix cells as a focus+context approach for embedding additional interactive views into a matrix.

Personal Augmented Reality for Information Visualization on Large Interactive Displays.

In this work we propose the combination of large interactive displays with personal head-mounted Augmented Reality (AR) for information visualization to facilitate data exploration and analysis. Even though large displays provide more display space, they are challenging with regard to perception, effective multi-user support, and managing data density and complexity. To address these issues and illustrate our proposed setup, we contribute an extensive design space comprising first, the spatial alignment of display, visualizations, and objects in AR space.

Augmented Reality Graph Visualizations.

Three-dimensional node-link diagrams are an important class of visualization for immersive analysis. Yet, there is little knowledge on how to visualize edges to support efficient analysis. We present an exploration of the design space for edge styles and discuss the results of a user study comparing six different edge variants.

Multiple Coordinated Views at Large Displays for Multiple Users: Empirical Findings on User Behavior, Movements, and Distances.

Interactive wall-sized displays benefit data visualization. Due to their sheer display size, they make it possible to show large amounts of data in multiple coordinated views (MCV) and facilitate collaborative data analysis. In this work, we propose a set of important design considerations and contribute a fundamental input vocabulary and interaction mapping for MCV functionality.

VISTILES: Coordinating and Combining Co-located Mobile Devices for Visual Data Exploration.

We present VISTILES, a conceptual framework that uses a set of mobile devices to distribute and coordinate visualization views for the exploration of multivariate data. In contrast to desktop-based interfaces for information visualization, mobile devices offer the potential to provide a dynamic and user-defined interface supporting co-located collaborative data exploration with different individual workflows. As part of our framework, we contribute concepts that enable users to interact with coordinated & multiple views (CMV) that are distributed across several mobile devices.

Interactive Near-Field Illumination for Photorealistic Augmented Reality with Varying Materials on Mobile Devices.

At present, photorealistic augmentation is not yet possible since the computational power of mobile devices is insufficient. Even streaming solutions from stationary PCs cause a latency that affects user interactions considerably. Therefore, we introduce a differential rendering method that allows for a consistent illumination of the inserted virtual objects on mobile devices, avoiding delays.