Martin Luboschik

Particle-based labeling: Fast point-feature labeling without obscuring other visual features

In many information visualization techniques, labels are an essential part to communicate the visualized data. To preserve the expressiveness of the visual representation, a placed label should neither occlude other labels nor visual representatives (e.g., icons, lines) that communicate crucial information. Optimal, non-overlapping labeling is an NP-hard problem. Thus, only a few approaches achieve a fast non-overlapping labeling in highly interactive scenarios like information visualization. These approaches generally target the point-feature label placement (PFLP) problem, solving only label-label conflicts. This paper presents a new, fast, solid and flexible 2D labeling approach for the PFLP problem that additionally respects other visual elements and the visual extent of labeled features. The results (number of placed labels, processing time) of our particle-based method compare favorably to those of existing techniques. Although the esthetic quality of non-real-time approaches may not be achieved with our method, it complies with practical demands and thus supports the interactive exploration of information spaces. In contrast to the known adjacent techniques, the flexibility of our technique enables labeling of dense point clouds by the use of non-occluding distant labels. Our approach is independent of the underlying visualization technique, which enables us to demonstrate the application of our labeling method within different information visualization scenarios.

A new weaving technique for handling overlapping regions

The use of transparencies is a common strategy in visual representations to guarantee the visibility of different overlapping graphical objects, especially, if no visibility-deciding order is given (e.g., importance, depth). Alpha-blending, however, could generate new colors that are not specified by the given color scale and overlapping shapes may become difficult to be separated visually and the selection of specific elements would be difficult. In this paper, we present a new approach for representing overlapping regions: Instead of blending different colors, our weaving technique separates the original colors and shapes are easier to differentiate. Due to a deterministic weaving order, all overlapping objects are visible. We apply our approach to scatter plot visualizations to enhance the communication of overlapping clusters.

Special Section on Uncertainty and Parameter Space Analysis in Visualization: Supporting the integrated visual analysis of input parameters and simulation trajectories

The visualization of simulation trajectories is a well-established approach to analyze simulated processes. Likewise, the visualization of the parameter space that configures a simulation is a well-known method to get an overview of possible parameter combinations. This paper follows the premise that both of these approaches are actually two sides of the same coin; since the input parameters influence the simulation outcome, it is desirable to visualize and explore both in a combined manner. The main challenge posed by such an integrated visualization is the combinatorial explosion of possible parameter combinations. It leads to insurmountably high simulation runtimes and screen space requirements for their visualization. The Visual Analytics approach presented in this paper targets this issue by providing a visualization of a coarsely sampled subspace of the parameter space and its corresponding simulation outcome. In this visual representation, the analyst can identify regions for further drill-down and thus finer subsampling. We aid this identification by providing visual cues based on heterogeneity metrics. These indicate in which regions of the parameter space deviating behavior occurs at a more fine-grained scale and thus warrants further investigation and possible re-computation. We demonstrate our approach in the domain of systems biology by a visual analysis of a rule-based model of the canonical Wnt signaling pathway that plays a major role in embryonic development. In this case, the aim of the domain experts was to systematically explore the parameter space to determine those parameter configurations that match experimental data sufficiently well.

An Approximate Execution of Rule-Based Multi-level Models

In cell biology, models increasingly capture dynamics at different organizational levels. Therefore, new modeling languages are developed, e.g., like ML-Rules, that allow a compact and concise description of these models. However, the more complex models become the more important is an efficient execution of these models. i¾?-leaping algorithms can speed up the execution of biochemical reaction models significantly by introducing acceptable inaccurate results. Whereas those approximate algorithms appear particularly promising to be applied to hierarchically structured models, the dynamic nested structures cause specific challenges. We present a i¾?-leaping algorithm for ML-Rules which tackles these specific challenges and evaluate the efficiency and accuracy of this adapted i¾?-leaping based on a recently developed visual analysis technique.

Smart views in smart environments

Smart environments integrate a multitude of different device ensembles and aim to facilitate proactive assistance in multidisplay scenarios. However, the integration of existing software, especially visualization systems, to take advantage of these novel capabilities is still a challenging task. In this paper we present a smart view management concept for an integration that combines and displays views of different systems in smart meeting rooms. Considering these varying requirements arising in such environments we provide a smart viewing management taking e.g. the dynamic user positions, view directions and even the semantics of views to be shown into account.

Adaptive Labeling for Interactive Mobile Information Systems

Textual annotations are important elements in all but the simplest visual interfaces. In order to integrate textual annotations smoothly into the dynamic graphical content of interactive information systems, fast yet high-quality label layout algorithms are required. With the ongoing pervasion of mobile applications these requirements are shifted from workstations to comparatively low-performance mobile devices. Fortunately, ubiquitous network access is also on the advance, so that mobile applications can employ remote layout services on external workstations. This paper presents two novel label layout algorithms for relevance-driven dynamic visualizations in interactive information systems. They are employed to generate adaptive visualizations in a mobile maintenance support scenario

Heterogeneity-based guidance for exploring multiscale data in systems biology

In systems biology, analyzing simulation trajectories at multiple scales is a common approach when subtle, detailed behavior and fundamental, overall behavior of a modeled system are to be investigated at the same time. A variety of multiscale visualization techniques provide solutions to handle and depict data at different scales. Yet the mere existence of multiple scales does not necessarily imply the existence of additional information on each of them: Data on a more fine-grained scale may not always yield new details, but instead reflect the already known data from more coarse-grained scales - just at a higher resolution. Nevertheless, to be sure of this, all scales have to be explored. We address this issue by guiding the exploration of simulation trajectories according to information about the deviation of the data between subsequent scales. For this purpose, we apply different dissimilarity measures to the simulation data at subsequent scales to automatically discern heterogeneous regions that exhibit deviating behavior on more fine-grained scales. We mark these regions and display them alongside the actual data in a multiscale visualization. By doing so, our approach provides valuable visual cues on whether it is worthwhile to drill-down further into the multi-scale data and if so, where additional information can be expected. Our approach is demonstrated by an exploratory walk-through of stochastic simulation results of a biochemical reaction network.

Illustrative halos in information visualization

In many interactive scenarios, the fast recognition and localization of crucial information is very important to effectively perform a task. However, in information visualization the visualization of permanently growing large data volumes often leads to a simultaneously growing amount of presented graphical primitives. Besides the fundamental problem of limited screen space, the effective localization of single or multiple items of interest by a user becomes more and more difficult. Therefore, different approaches have been developed to emphasize those items -- mainly by manipulating the items size, by suppressing the whole context or by adding supplemental visual elements (e.g., contours, arrows). This paper introduces the well known illustrative technique of haloing to information visualization to address the localization problem. Applying halos emphasizes items without a manipulation of size or an introduction of additional visual elements and reduces the context suppression to a locally defined region. This paper also presents the results of a first user-study to get an impression of the usefulness of halos for a faster recognition.

Interactive visual exploration of simulator accuracy: a case study for Stochastic Simulation Algorithms

Visual Analytics offers various interesting methods to explore high dimensional data interactively. In this paper we investigate how it can be applied to support experimenters and developers of simulation software conducting simulation studies. In particular the usage and development of approximate simulation algorithms poses several practical problems, e.g., estimating the impact of algorithm parameters on accuracy or detecting faulty implementations. To address some of those problems, we present an approach that allows to relate configurations and accuracy visually and exploratory. The approach is evaluated by a brief case study, focusing on the accuracy of Stochastic Simulation Algorithms.

On Spatial Perception Issues In Augmented Reality Based Immersive Analytics

Beyond other domains, the field of immersive analytics makes use of Augmented Reality techniques to successfully support users in analyzing data. When displaying ubiquitous data integrated into the everyday life, spatial immersion issues like depth perception, data localization and object relations become relevant. Although there is a variety of techniques to deal with those, they are difficult to apply if the examined data or the reference space are large and abstract. In this work, we discuss observed problems in such immersive analytics systems and the applicability of current countermeasures to identify needs for action.