Data visualization literacy is vital for preparing students for higher education in data-driven fields, yet instructions and teaching materials for high school remain limited. To address this gap, we applied participatory design (PD) and cooperative prototyping (CP) with high school teachers and data visualization experts as equal partners. Over a period of 16 months, we conducted an interview and four iterative workshops to identify teaching challenges and co-create solutions. This process produced three prototype sets, culminating in a 17-step teaching unit on the CoTinker platform for computational thinking. The unit uses a music dataset and a developed visualization tool to engage students in collaboratively creating data visualizations through hands-on learning while introducing visualization concepts, visual storytelling, and data ethics. Our work demonstrates how PD and CP can bridge research and practice, and contributes a replicable framework for integrating data visualization into high school by use of co-designed tools grounded in real-world classrooms.

Progressive visual analytics (PVA) allows analysts to maintain their flow during otherwise long-running computations by producing early, incomplete results that refine over time, for example, by running the computation over smaller partitions of the data. These partitions are created using sampling, whose goal it isto draw samples of the dataset such that the progressive visualization becomes as useful as possible as soon as possible. What makes the visualization useful depends on the analysis task and, accordingly, some task-specific sampling methods have been proposed for PVA to address this need. However, as analysts see more and more of their data during the progression, the analysis task at hand often changes, which means that analysts need to restart the computation to switch the sampling method, causing them to lose their analysis flow. This poses a clear limitation to the proposed benefits of PVA. Hence, we propose a pipeline for PVA-sampling that allows tailoring the data partitioning to analysis scenarios by switching out modules in a way that does not require restarting the analysis. To that end, we characterize the problem of PVA-sampling, formalize the pipeline in terms of data structures, discuss on-the-fly tailoring, and present additional examples demonstrating its usefulness.

GPU-FAST-PROCLUS is a GPU-parallelized algorithm for projected clustering based on the k-medoids approach. It speeds up clustering to allow for real-time interaction - even for datasets of millions of items. Interactivity allows users to quickly determine sensible clustering parameters such as the number of clusters k, provided a suitable visualization is available. Yet, as clustering and visualization are usually decoupled, cluster results are funneled from the GPU back to the CPU, only to be mapped onto appropriate graphics, which are then rendered on the GPU again. This introduces a bottleneck that hinders fluid interaction with clustering. As a solution to this, we propose AVID (Analysis and Visualization In Device). Following the principle "What happens on the GPU, stays on the GPU", AVID removes the round trip to the CPU and keeps clustering results on the GPU to render them on the GPU directly. By doing so, users can interactively tune projected clustering parameters and observe the effects without noticeable delay. In our demo system, we showcase the efficiency of our data management strategies for projected clustering as well as the efficacy of data visualization.

Progressive visual analytics allows users to interact with early, partial results of long-running computations on large datasets. In this context, computational steering is often brought up as a means to prioritize the progressive computation. This is meant to focus computational resources on data subspaces of interest so as to ensure their computation is completed before all others. Yet, current approaches to select a region of the view space and then to prioritize its corresponding data subspace either require a one-to-one mapping between view and data space, or they need to establish and maintain computationally costly index structures to trace complex mappings between view and data space. We present steering-by-example, a novel interactive steering approach for progressive visual analytics, which allows prioritizing data subspaces for the progression by generating a relaxed query from a set of selected data items. Our approach works independently of the particular visualization technique and without additional index structures. First benchmark results show that steering-by-example considerably improves Precision and Recall for prioritizing unprocessed data for a selected view region, clearly outperforming random uniform sampling.

Over the past years, the visualization of large and complex data sets brought up various Visual Analytics (VA) tools in order to solve domain-specific tasks. These VA tools are typically implemented as individual software components in data-flow-oriented models, meaning that data is transferred from one component to the next. While most VA frameworks rely on a monolithic architecture with features for the integration of specialized analysis methods, we consider a loose coupling of independent applications, where autonomous VA tools are used in predefined analysis sequences. To this end, we provide a characterization of the data exchange process among individual VA tools in the form of a taxonomy. This taxonomy can be used as a checklist to identify characteristics and improve the data flow of one's own multi-tool VA setup. For this purpose, we conducted a systematic investigation of the individual aspects of data exchange that are commonly found across different usage scenarios. We apply our taxonomy to three existing multi-tool frameworks, the open-source library ReVize, the toolchain editor AnyProc, and the visualization and monitoring framework Plant@Hand3D.

In visualization, user guidance has become an essential concept to aid users in making informed decisions ranging from what subsets to focus on in the data space to which regions to explore in the view space. To guide users, predominantly visual cues like colors or arrows are used to indicate particular targets or directions. In this paper, we explore the possibility of another sensory channel for guidance cues: vibrotactile feedback. To that end, we explore different properties of the vibrotactile channel (e.g., amplitude and duration) and discuss their potential use as guidance cues. We then report on an experiment (N=14) in which we investigate possible vibrotactile cues in comparison to visual cues and to a combination of visual and vibrotactile cues for a guided selection scenario and a guided navigation scenario. Although none of the vibrotactile cues significantly outperformed the visual cues, our study results shed light on a number of practical issues when using vibration for user guidance - including differences between various types of vibrotactile feedback, as well as diverging performance for different guidance scenarios.

Abstract Cartographic maps have been shown to provide cognitive benefits when interpreting data in relation to a geographic location. In visualization, the term map‐like describes techniques that incorporate characteristics of cartographic maps in their representation of abstract data. However, the field of map‐like visualization is vast and currently lacks a clear classification of the existing techniques. Moreover, choosing the right technique to support a particular visualization task is further complicated, as techniques are scattered across different domains, with each considering different characteristics as map‐like. In this paper, we give an overview of the literature on map‐like visualization and provide a hierarchical classification of existing techniques along two general perspectives: imitation and schematization of cartographic maps. Each perspective is further divided into four principal categories that group common map‐like techniques along the visual primitives they affect. We further discuss this classification from a task‐centered view and highlight open research questions.

Visual analytics (VA) is typically applied in scenarios where complex data has to be analyzed. Unfortunately, there is a natural correlation between the complexity of the data and the complexity of the tools to study them. An adverse effect of complicated tools is that analytical goals are more difficult to reach. Therefore, it makes sense to consider methods that guide or assist users in the visual analysis process. Several such methods already exist in the literature, yet we are lacking a general model that facilitates in-depth reasoning about guidance. We establish such a model by extending van Wijk's model of visualization with the fundamental components of guidance. Guidance is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis. We describe diverse inputs based on which guidance can be generated and discuss different degrees of guidance and means to incorporate guidance into VA tools. We use existing guidance approaches from the literature to illustrate the various aspects of our model. As a conclusion, we identify research challenges and suggest directions for future studies. With our work we take a necessary step to pave the way to a systematic development of guidance techniques that effectively support users in the context of VA.

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.

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.

In response to the large number of existing tree layouts, generic “meta-layouts” have recently been proposed. These generic approaches utilize layout design spaces to pinpoint a tree drawing with desired characteristics in the wealth of available drawing options and parameters. While design-space-based generic layouts work well for the confined set of implicit space-filling tree layouts, they have so far eluded their extension to explicit node-link diagrams. In order to produce both, implicit and explicit tree layouts, this paper parts with the descriptive nature of the design spaces and instead takes a generative approach based on operators. As these operators can be combined into operator sequences and be used at different stages of the layout process, a small operator set already suffices to yield a large number of different tree layouts. To this end, we present a generic tree layout pipeline and give examples of suitable layout operators to plug into the pipeline. A prototypical implementation of our pipeline and operators is presented, and it is illustrated with space-filling and node-link examples. Furthermore, the paper presents results from a user study evaluating our generative approach as it is realized by the prototype.

Abstract The π -calculus, in particular its stochastic version the stochastic π -calculus, is a common modeling formalism to concisely describe the chemical reactions occurring in biochemical systems. However, it remains largely unexplored how to transform a biochemical model expressed in the stochastic π -calculus back into a set of meaningful reactions. To this end, we present a two step approach of first translating model states to reaction sets and then visualizing sequences of reaction sets, which are obtained from state trajectories, in terms of reaction networks. Our translation from model states to reaction sets is formally defined and shown to be correct, in the sense that it reflects the states and transitions as they are derived from the continuous time Markov chain-semantics of the stochastic π -calculus. Our visualization concept combines high level measures of network complexity with interactive, table-based network visualizations. It directly reflects the structures introduced in the first step and allows modelers to explore the resulting simulation traces by providing both: an overview of a network’s evolution and a detail inspection on demand.

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.

Identification and characterization of cancer subtypes are important areas of research that are based on the integrated analysis of multiple heterogeneous genomics datasets. Since there are no tools supporting this process, much of this work is done using ad-hoc scripts and static plots, which is inefficient and limits visual exploration of the data. To address this, we have developed StratomeX, an integrative visualization tool that allows investigators to explore the relationships of candidate subtypes across multiple genomic data types such as gene expression, DNA methylation, or copy number data. StratomeX represents datasets as columns and subtypes as bricks in these columns. Ribbons between the columns connect bricks to show subtype relationships across datasets. Drill-down features enable detailed exploration. StratomeX provides insights into the functional and clinical implications of candidate subtypes by employing small multiples, which allow investigators to assess the effect of subtypes on molecular pathways or outcomes such as patient survival. As the configuration of viewing parameters in such a multi-dataset, multi-view scenario is complex, we propose a meta visualization and configuration interface for dataset dependencies and data-view relationships. StratomeX is developed in close collaboration with domain experts. We describe case studies that illustrate how investigators used the tool to explore subtypes in large datasets and demonstrate how they efficiently replicated findings from the literature and gained new insights into the 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.

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.

Large volumes of real-world data often exhibit inhomogeneities: vertically in the form of correlated or independent dimensions and horizontally in the form of clustered or scattered data items. In essence, these inhomogeneities form the patterns in the data that researchers are trying to find and understand. Sophisticated statistical methods are available to reveal these patterns, however, the visualization of their outcomes is mostly still performed in a one-view-fits-all manner. In contrast, our novel visualization approach, VisBricks, acknowledges the inhomogeneity of the data and the need for different visualizations that suit the individual characteristics of the different data subsets. The overall visualization of the entire data set is patched together from smaller visualizations, there is one VisBrick for each cluster in each group of interdependent dimensions. Whereas the total impression of all VisBricks together gives a comprehensive high-level overview of the different groups of data, each VisBrick independently shows the details of the group of data it represents. State-of-the-art brushing and visual linking between all VisBricks furthermore allows the comparison of the groupings and the distribution of data items among them. In this paper, we introduce the VisBricks visualization concept, discuss its design rationale and implementation, and demonstrate its usefulness by applying it to a use case from the field of biomedicine.

Apart from explicit node-link representations, implicit visualizations and especially the Treemap as their frontrunner have acquired a solid position among the available techniques to visualize hierarchies. Their advantage is a highly space-efficient graphical representation that does not require explicit drawing of edges. In this paper, we survey the design space for this class of visualization techniques. We establish the design space along the four axes of dimensionality, edge representation, node representation, and layout by examining existing implicit hierarchy visualization techniques. The survey is completed by casting some light into regions of the design space that have not yet been explored. Our design space is not a mere theoretical construct, but a practically usable tool for rapid visualization development. To that end, we discuss a software implementation of the introduced design space.

Space-filling layout techniques for tree representations are frequently used when the available screen space is small or the data set is large. In this paper, we propose an efficient approach to space-filling tree representations that uses mechanisms from the point-based rendering paradigm. We present helpful interaction techniques and visual cues that tie in with our layout. Additionally, we relate this new layout approach to common layout mechanisms and evaluate the new layout along the lines of a numerical evaluation using the measures of the Ink-Paper Ratio and overplotted%, and in a preliminary user study. The flexibility of the general approach is illustrated by several enhancements of the basic layout, as well as its usage within the context of two software frameworks from different application fields.

When visualizing data, spatial and temporal references of these data often have to be considered in addition to the actual data attributes. Nowadays, structural information is becoming more and more important. Hierarchies, for instance, are frequently applied to make large and complex data manageable. Hence, a visual depiction of hierarchical structures in space and time is required. Although there are several techniques addressing specific aspects of spatio-temporal visualization, approaches that cope with space, time, data, and structure are rare. With this article we take a step to fill this gap. By combining various well-established concepts, we achieve a reasonably complete visualization of all of the aforementioned aspects, where our focus is on the hierarchical structures. We embed hierarchies directly into the regions of a map display using variants of the point-based layout. Layering and animation are applied to visualize temporal aspects. Based on the analysis goals, users can switch between representations that emphasize data attributes or hierarchical structures. Interaction techniques support users in navigating the data and their visualization. We demonstrate the usefulness of our approach by adapting it to implement a visualization for spatiotemporal human health data.

Space-filling layout techniques for tree representations are frequently used when the available screen space is small or the data set is large. In this paper, we propose a new approach to space-filling tree representations, which uses mechanisms from the point-based rendering paradigm. Additionally, helpful interaction techniques that tie in with our layout are presented. We will relate our new technique to established space-filling techniques along the lines of a newly developed classification and also evaluate it numerically using the measures of theink-paper-ratioandoverplotted%.

The rise in the use of social network sites allows us to collect large amounts of user reported data on social structures and analysis of this data could provide useful insights for many of the social sciences. This analysis is typically the domain of Social Network Analysis, and visualization of these structures often proves invaluable in understanding them. However, currently available visual analysis tools are not very well suited to handle the massive scale of this network data, and often resolve to displaying small ego networks or heavily abstracted networks. In this paper, we present Honeycomb, a visualization tool that is able to deal with much larger scale data (with millions of connections), which we illustrate by using a large scale corporate social networking site as an example. Additionally, we introduce a new probability based network metric to guide users to potentially interesting or anomalous patterns and discuss lessons learned during design and implementation. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The visualization of graphs has proven to be very useful for exploring structures in different application domains. However, in certain fields of computer science, graph visualization is understood and focused quite differently. While "graph drawing" focuses on optimized layouts for node-link-representations of networks, "information visualization" prefers to work on hierarchies focusing on very large structures, different views and interactivity. This paper gives a systematic view of the problem of graph visualization by combining both approaches. We introduce a general view of different visualization methods as well as describe occurring problems and discuss basic constraints. These are used to propose a visualization framework for graphs, whose development motivated this paper