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.

We present Marvis, a conceptual framework that combines mobile devices and head-mounted Augmented Reality (AR) for visual data analysis. We propose novel concepts and techniques addressing visualization-specific challenges. By showing additional 2D and 3D information around and above displays, we extend their limited screen space. AR views between displays as well as linking and brushing are also supported, making relationships between separated visualizations plausible. We introduce the design process and rationale for our techniques. To validate Marvis’ concepts and show their versatility and widespread applicability, we describe six implemented example use cases. Finally, we discuss insights from expert hands-on reviews. As a result, we contribute to a better understanding of how the combination of one or more mobile devices with AR can benefit visual data analysis. By exploring this new type of visualization environment, we hope to provide a foundation and inspiration for future mobile data visualizations.

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. We also developed a fully functional application with more than 45 coordinated views visualizing a real-world, multivariate data set of crime activities, which we used in a comprehensive qualitative user study investigating how pairs of users behave. Most importantly, we found that flexible movement is essential and-depending on user goals-is connected to collaboration, perception, and interaction. Therefore, we argue that for future systems interaction from the distance is required and needs good support. We show that our consistent design for both direct touch at the large display and distant interaction using mobile phones enables the seamless exploration of large-scale MCV at wall-sized displays. Our MCV application builds on design aspects such as simplicity, flexibility, and visual consistency and, therefore, supports realistic workflows. We believe that in the future, many visual data analysis scenarios will benefit from wall-sized displays presenting numerous coordinated visualizations, for which our findings provide a valuable foundation.

We demonstrate the prototype of the conceptual VisTiles framework. VisTiles allows exploring multivariate data sets by using multiple coordinated views that are distributed across a set of mobile devices. This setup allows users to benefit from dynamic and user-defined interface arrangements and to easily initiate co-located data exploration sessions. The current web-based prototype runs on commodity devices and is able to determine the spatial device arrangement by either a cross-device pinch gesture or an external tracking system. Multiple data sets are provided that can be explored by different visualizations (e.g., scatterplots, parallel coordinate plots, stream graphs). With this demonstration, we showcase the general concepts of VisTiles and discuss ideas for enhancements as well the potential for application cases beyond data analysis.

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. The major components of the framework are: (i) dynamic and flexible layouts for CMV focusing on the distribution of views and (ii) an interaction concept for smart adaptations and combinations of visualizations utilizing explicit side-by-side arrangements of devices. As a result, users can benefit from the possibility to combine devices and organize them in meaningful spatial layouts. Furthermore, we present a web-based prototype implementation as a specific instance of our concepts. This implementation provides a practical application case enabling users to explore a multivariate data collection. We also illustrate the design process including feedback from a preliminary user study, which informed the design of both the concepts and the final prototype.

Three-dimensional visualizations employing traditional input and output technologies have well-known limitations. Immersive technologies, natural interaction techniques, and recent developments in data physicalization may help to overcome these issues. In this context, we are specifically interested in the usage of spatial interaction with mobile devices for improved 3D visualizations. To contribute to a better understanding of this interaction style, we implemented example visualizations on a spatially-tracked tablet and investigated their usage and potential. In this paper, we report on a qualitative study comparing spatial interaction with inplace 3D visualizations to classic touch interaction regarding typical visualization tasks: navigation of unknown datasets, comparison of individual data objects, and the understanding and memorization of structures in the data. We identify several distinct usage patterns and derive recommendations for using spatial interaction in 3D data visualization.

We introduce Stackables: tangibles designed to support faceted information seeking in a variety of contexts. We are faced, more than ever, with tasks that require us to find, access, and act on information by ourselves or together with others. Current interfaces for browsing and search in large data spaces, however, largely focus on the support of either individual or collaborative activities. Stackables were designed to bridge this gap and be useful in meetings, for sharing results from individual search activities, and for realistic datasets including multiple facets with large value ranges. Each Stackable tangible represents search parameters that can be shared amongst collaborators, modified during an information seeking process, and stored and transferred. We describe Stackables, their flexible and expressive combination to formulate queries, and the underlying interaction concept in detail. An evaluation provides initial evidence of their usability in targeted and exploratory information seeking tasks.

Precise alignment of graphical objects and the creation of accurate layouts are crucial activities in many applications, such as graphics design tools, presentation software or graph editors. Surface computing is very promising for these application domains but not fully explored yet. In this paper we contribute two tools which support layout tasks on interactive displays: interactive grids and multi-touch alignment guides. Both tools allow the precise positioning of graphical objects in a flexible and fluent way by multi-touch input. Direct bimanual interaction and physical metaphors are applied to arrange objects along straight lines and curves. A formative user evaluation showed promising results with regard to a productive and easy use of the tools.