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. Responsive matrix cells are local zoomable regions of interest that provide auxiliary data exploration and editing facilities for multivariate graphs. They behave responsively by adapting their visual contents to the cell location, the available display space, and the user task. Responsive matrix cells enable users to reveal details about the graph, compare node and edge attributes, and edit data values directly in a matrix without resorting to external views or tools. We report the general design considerations for responsive matrix cells covering the visual and interactive means necessary to support a seamless data exploration and editing. Responsive matrix cells have been implemented in a web-based prototype based on which we demonstrate the utility of our approach. We describe a walk-through for the use case of analyzing a graph of soccer players and report on insights from a preliminary user feedback session.

Thickness of Intraretinal Layers in Patients with Type 2 Diabetes Mellitus Depending on a Concomitant Diabetic Neuropathy: Results of a Cross-Sectional Study Using Deviation Maps for OCT Data Analysis.

Optical coherence tomography (OCT) supports the detection of thickness changes in intraretinal layers at an early stage of diabetes mellitus. However, the analysis of OCT data in cross-sectional studies is complex and time-consuming. We introduce an enhanced deviation map-based analysis (MA) and demonstrate its effectiveness in detecting early changes in intraretinal layer thickness in adults with type 2 diabetes mellitus (T2DM) compared to common early treatment diabetic retinopathy study (ETDRS) grid-based analysis (GA). To this end, we obtained OCT scans of unilateral eyes from 33 T2DM patients without diabetic retinopathy and 40 healthy controls. The patients were categorized according to concomitant diabetic peripheral neuropathy (DN). The results of MA and GA demonstrated statistically significant differences in retinal thickness between patients and controls. Thinning was most pronounced in total retinal thickness and the thickness of the inner retinal layers in areas of the inner macular ring, selectively extending into areas of the outer macular ring and foveal center. Patients with clinically proven DN showed the strongest thinning of the inner retinal layers. MA showed additional areas of thinning whereas GA tended to underestimate thickness changes, especially in areas with localized thinning. We conclude that MA enables a precise analysis of retinal thickness data and contributes to the understanding of localized changes in intraretinal layers in adults with T2DM.

Deviation Maps for Understanding Thickness Changes of Inner Retinal Layers in Children with Type 1 Diabetes Mellitus.

Purpose: To analyze the use of deviation maps (DevMs) to understand thickness changes of inner retinal layers in optical coherence tomography (OCT) data. To test a new visual analytics (VA) method with reduced complexity of OCT data analysis by comparing the layer thickness of children with type 1 diabetes mellitus (T1DM) to matched controls. Methods: OCT was performed on unilateral eyes of 26 children with T1DM without diabetic retinopathy and 29 healthy children to obtain macular volume scans. Subsequently, segmented inner retinal layers were analyzed using VA. Deviation maps were generated to readily visualize thickness differences between both groups and to investigate thickness changes of individual patients in relation to the control group. Results: In DevMs of the patient group, the total retina (TR) demonstrated localized, irregular areas of thinning (mean ± standard deviation) involving foveal center, inner macula, and inferior-nasal outer macula (-9.31 ± 1.73 µm; p < 0.05). Similarly, retinal nerve fiber layer showed continuous and localized areas of thinning in both inner and outer macula, extending nasally (-5.45 ± 4.31 µm; p < 0.05). In DevMs of individual patients, the TR and inner retinal layers revealed remarkable changes in thickness that were present between patients at both late and early stages of diabetes. Conclusion: The VA method simplifies the in-depth analysis of OCT volume data from different groups and is effective in detecting retinal thickness changes in children with diabetes. It can be easily adopted in a clinical set-up and intuitively used in complex multidisciplinary studies.

[Visual Analysis of Retinal OCT Data]. / Visuelle Analyse von retinalen OCT-Daten.

Optical coherence tomography (OCT) enables noninvasive high-resolution 3D imaging of the human retina, and thus plays a fundamental role in ophthalmology. Via OCT examination, even subtle retinal changes can be captured, which occur in very early stages of different diseases (e.g., glaucoma, diabetes mellitus, or age-related macular degeneration). Yet, analyzing the resulting data is challenging. Conventionally, OCT data are strongly aggregated via automated methods. While this reduces the amount of information to be analyzed, it also makes it difficult, if not impossible, to identify small and localized retinal changes. This might lead to wrong diagnoses, since these methods do not account for patient-specific characteristics. We address this problem by providing new and efficient visual-interactive methods. Particularly, we introduce dedicated visualizations that show different aspects of the data. In addition, we support patient-specific selections of relevant data regions. Selected regions are emphasized, or separately visualized to inspect retinal substructures in detail. By visually comparing the regions to reference data, even very small retinal changes can be detected. We demonstrate the utility of our approach by applying it to data of a study with pediatric patients suffering from diabetes mellitus type 1. Our results show that visual-interactive methods indeed help to analyze subtle retinal changes and, thus, support the diagnosis of diseases in an early stage.

An Enhanced Visualization Process Model for Incremental Visualization.

With today's technical possibilities, a stable visualization scenario can no longer be assumed as a matter of course, as underlying data and targeted display setup are much more in flux than in traditional scenarios. Incremental visualization approaches are a means to address this challenge, as they permit the user to interact with, steer, and change the visualization at intermediate time points and not just after it has been completed. In this paper, we put forward a model for incremental visualizations that is based on the established Data State Reference Model, but extends it in ways to also represent partitioned data and visualization operators to facilitate intermediate visualization updates. In combination, partitioned data and operators can be used independently and in combination to strike tailored compromises between output quality, shown data quantity, and responsiveness-i.e., frame rates. We showcase the new expressive power of this model by discussing the opportunities and challenges of incremental visualization in general and its usage in a real world scenario in particular.

A modular degree-of-interest specification for the visual analysis of large dynamic networks.

Large dynamic networks are targets of analysis in many fields. Tracking temporal changes at scale in these networks is challenging due in part to the fact that small changes can be missed or drowned-out by the rest of the network. For static networks, current approaches allow the identification of specific network elements within their context. However, in the case of dynamic networks, the user is left alone with finding salient local network elements and tracking them over time. In this work, we introduce a modular DoI specification to flexibly define what salient changes are and to assign them a measure of their importance in a time-varying setting. The specification takes into account neighborhood structure information, numerical attributes of nodes/edges, and their temporal evolution. A tailored visualization of the DoI specification complements our approach. Alongside a traditional node-link view of the dynamic network, it serves as an interface for the interactive definition of a DoI function. By using it to successively refine and investigate the captured details, it supports the analysis of dynamic networks from an initial view until pinpointing a user's analysis goal. We report on applying our approach to scientific coauthorship networks and give concrete results for the DBLP data set.

Supporting the visual analysis of dynamic networks by clustering associated temporal attributes.

The visual analysis of dynamic networks is a challenging task. In this paper, we introduce a new approach supporting the discovery of substructures sharing a similar trend over time by combining computation, visualization and interaction. With existing techniques, their discovery would be a tedious endeavor because of the number of nodes, edges as well as time points to be compared. First, on the basis of the supergraph, we therefore group nodes and edges according to their associated attributes that are changing over time. Second, the supergraph is visualized to provide an overview of the groups of nodes and edges with similar behavior over time in terms of their associated attributes. Third, we provide specific interactions to explore and refine the temporal clustering, allowing the user to further steer the analysis of the dynamic network. We demonstrate our approach by the visual analysis of a large wireless mesh network.

A design space of visualization tasks.

Knowledge about visualization tasks plays an important role in choosing or building suitable visual representations to pursue them. Yet, tasks are a multi-faceted concept and it is thus not surprising that the many existing task taxonomies and models all describe different aspects of tasks, depending on what these task descriptions aim to capture. This results in a clear need to bring these different aspects together under the common hood of a general design space of visualization tasks, which we propose in this paper. Our design space consists of five design dimensions that characterize the main aspects of tasks and that have so far been distributed across different task descriptions. We exemplify its concrete use by applying our design space in the domain of climate impact research. To this end, we propose interfaces to our design space for different user roles (developers, authors, and end users) that allow users of different levels of expertise to work with it.

Model-driven design for the visual analysis of heterogeneous data.

As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.

In situ exploration of large dynamic networks.

The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization--a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown--right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.

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.

Visual methods for analyzing time-oriented data.

Providing appropriate methods to facilitate the analysis of time-oriented data is a key issue in many application domains. In this paper, we focus on the unique role of the parameter time in the context of visually driven data analysis. We will discuss three major aspects - visualization, analysis, and the user. It will be illustrated that it is necessary to consider the characteristics of time when generating visual representations. For that purpose we take a look at different types of time and present visual examples. Integrating visual and analytical methods has become an increasingly important issue. Therefore, we present our experiences in temporal data abstraction, principal component analysis, and clustering of larger volumes of time-oriented data. The third main aspect we discuss is supporting user-centered visual analysis. We describe event-based visualization as a promising means to adapt the visualization pipeline to needs and tasks of users.