We investigate hybrid user interfaces (HUIs), aiming to establish a cohesive understanding and to adopt consistent terminology for this nascent research area. HUIs combine heterogeneous devices in complementary roles, leveraging the distinct benefits of each. Our work focuses on cross-device interaction between 2D devices and mixed reality environments, which are particularly compelling, leveraging the familiarity of traditional 2D platforms while providing spatial awareness and immersion. Although prior work has prominently explored such HUIs in the context of mixed reality, we still lack a cohesive understanding of the unique design possibilities and challenges of such combinations, resulting in a fragmented research landscape. We conducted a systematic survey and present a taxonomy of HUIs that combine conventional display technology and mixed reality environments. Based on this, we discuss past and current challenges, the evolution of definitions, and prospective opportunities to tie together the past 30 years of research with our vision of future HUIs.

In recent years, data-driven stories have found a firm footing in journalism, business, and education. They leverage visualization and storytelling to convey information to broader audiences. Likewise, immersive technologies, like augmented and virtual reality devices, provide excellent potential for exploring and explaining data, thus inviting research on how data-driven storytelling transfers to immersive environments. To gain a better understanding of this exciting novel research area, we conducted a scoping review on the emerging notion of immersive data-driven storytelling, extended by surveying immersive data journalism and by analyzing immersive games, selected based on community reviews and tags. We present our methodology for the survey and discuss prominent themes that coalesce the knowledge we extracted from the literature, journalism, and games. These themes include, among others, the spatial embodiment of narration, the incorporation of the users and their context into narratives, and the balance between guiding the user versus promoting serendipity. Our discussion of these themes reveals research opportunities and challenges that will inform the design of immersive data-driven stories in the future.

Abstract State-of-the-art Probabilistic Model Checking (PMC) offers multiple engines for the quantitative analysis of Markov Decision Processes (MDPs), including rewards modeling cost or utility values. Despite the huge amount of internally computed information, support for debugging and facilities that enhance the understandability of PMC models and results are very limited. As a first step to improve on that, we present the basic principles of PMC-VIS, a tool that supports the exploration of large MDPs together with the computed PMC results per MDP-state through interactive visualization. By combining visualization techniques, such as node-link diagrams and parallel coordinates, with quantitative analysis capabilities, PMC-VIS supports users in gaining insights into the probabilistic behavior of MDPs and PMC results and enables different ways to explore the behaviour of schedulers of multiple target properties. The usefulness of PMC-VIS is demonstrated through three different application scenarios.

Abstract OWL is a powerful language to formalize terminologies in an ontology. Its main strength lies in its foundation on description logics, allowing systems to automatically deduce implicit information through logical reasoning. However, since ontologies are often complex, understanding the outcome of the reasoning process is not always straightforward. Unlike already existing tools for exploring ontologies, our visualization tool Evonne is tailored towards explaining logical consequences. In addition, it supports the debugging of unwanted consequences and allows for an interactive comparison of the impact of removing statements from the ontology. Our visual approach combines (1) specialized views for the explanation of logical consequences and the structure of the ontology, (2) employing multiple layout modes for iteratively exploring explanations, (3) detailed explanations of specific reasoning steps, (4) cross‐view highlighting and colour coding of the visualization components, (5) features for dealing with visual complexity and (6) comparison and exploration of possible fixes to the ontology. We evaluated Evonne in a qualitative study with 16 experts in logics, and their positive feedback confirms the value of our concepts for explaining reasoning and debugging ontologies.

Recent developments on solvers for abstract argumentation frameworks (AFs) made them capable to compute extensions for many semantics efficiently. However, for many input instances these solution spaces can become very large and incomprehensible. So far, for the further exploration and investigation of the AF solution space the user needs to use post-processing methods or handcrafted tools. To compare and explore the solution spaces of two selected semantics, we propose an approach that visually supports the user, via a combination of dimensionality reduction of argumentation extensions and a projection of extensions to sets of accepted or rejected arguments. We introduce the novel web-based visualization tool NEXAS that allows for an interactive exploration of the solution space together with a statistical analysis of the acceptance of individual arguments for the selected semantics, as well as provides an interactive correlation matrix for the acceptance of arguments. We validate the tool with a walk-through along three use cases.

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.e., specifications that relate multiple executions of a system to each other. We aim to facilitate the visual analysis of such counterexamples through our HyperVis tool, which provides interactive visualizations of the given model, specification, and counterexample. Within an iterative and interdisciplinary design process, we developed visualization solutions that can effectively communicate the core aspects of the model checking result. Specifically, we introduce graphical representations of binary values for improving pattern recognition, color encoding for better indicating related aspects, visually enhanced textual descriptions, as well as extensive cross-view highlighting mechanisms. Further, through an underlying causal analysis of the counterexample, we are also able to identify values that contributed to the violation and use this knowledge for both improved encoding and highlighting. Finally, the analyst can modify both the specification of the hyperproperty and the system directly within HyperVis and initiate the model checking of the new version. In combination, these features notably support the analyst in understanding the error leading to the counterexample as well as iterating the provided system and specification. We ran multiple case studies with HyperVis and tested it with domain experts in qualitative feedback sessions. The participants' positive feedback confirms the considerable improvement over the manual, text-based status quo and the value of the tool for explaining hyperproperties.