Detecting and interpreting common patterns in relational data is crucial for understanding complex topological structures across various domains. These patterns, or network motifs, can often be detected algorithmically. However, visual inspection remains vital for exploring and discovering patterns. This paper focuses on presenting motifs within BioFabric network visualizations-a unique technique that opens opportunities for research on scaling to larger networks, design variations, and layout algorithms to better expose motifs. Our goal is to show how highlighting motifs can assist users in identifying and interpreting patterns in BioFabric visualizations. To this end, we leverage existing motif simplification techniques. We replace edges with glyphs representing fundamental motifs such as staircases, cliques, paths, and connector nodes. The results of our controlled experiment and usage scenarios demonstrate that motif simplification for BioFabric is useful for detecting and interpreting network patterns. Our participants were faster and more confident using the simplified view without sacrificing accuracy. The efficacy of our current motif simplification approach depends on which extant layout algorithm is used. We hope our promising findings on user performance will motivate future research on layout algorithms tailored to maximizing motif presentation. Our supplemental material is available at https://osf.io/f8s3g/?view_only=7e2df9109dfd4e6c85b89ed828320843.

Abstract One‐dimensional orderings of spatial entities have been researched in many contexts, e.g. spatial indexing structures or visualizations for spatiotemporal trend analysis. While plenty of studies have been conducted to evaluate orderings of point‐based data, polygonal shapes, despite their different topological properties, have received less attention. Existing measures to quantify errors in projections or orderings suffer from generic neighborhood definitions and over‐simplification of distances when applied to polygonal data. In this work, we address these shortcomings by introducing measures that adapt to a varying neighborhood size depending on the number of contiguous neighbors and thus, address the limitations of existing measures for polygonal shapes. To guide experts in determining a suitable ordering, we propose a user‐steerable visual analytics prototype capable of locally and globally inspecting ordering errors, investigating the impact of geographic obstacles, and comparing ordering strategies using our measures. We demonstrate the effectiveness of our approach through a use case and conducted an expert study with 8 data scientists as a qualitative evaluation of our approach. Our results show that users are capable of identifying ordering errors, comparing ordering strategies on a global and local scale, as well as assessing the impact of semantically relevant geographic obstacles.

Visualizing relational data is crucial for understanding complex connections between entities in social networks, political affiliations, or biological interactions. Well-known representations like node-link diagrams and adjacency matrices offer valuable insights, but their effectiveness relies on the ability to identify patterns in the underlying topological structure. Reordering strategies and layout algorithms play a vital role in the visualization process since the arrangement of nodes, edges, or cells influences the visibility of these patterns. The BioFabric visualization combines elements of node-link diagrams and adjacency matrices, leveraging the strengths of both, the visual clarity of node-link diagrams and the tabular organization of adjacency matrices. A unique characteristic of BioFabric is the possibility to reorder nodes and edges separately. This raises the question of which combination of layout algorithms best reveals certain patterns. In this paper, we discuss patterns and anti-patterns in BioFabric, such as staircases or escalators, relate them to already established patterns, and propose metrics to evaluate their quality. Based on these quality metrics, we compared combinations of well-established reordering techniques applied to BioFabric with a well-known benchmark data set. Our experiments indicate that the edge order has a stronger influence on revealing patterns than the node layout. The results show that the best combination for revealing staircases is a barycentric node layout, together with an edge order based on node indices and length. Our research contributes a first building block for many promising future research directions, which we also share and discuss. A free copy of this paper and all supplemental materials are available at https://osf.io/9mt8r/?view_only=b7t0dfbe550e3404f83059afdc60184c6.

Treemaps are a powerful tool for representing hierarchical data in a space-efficient manner and are used in various domains, including network security or software development. However, interpreting the topology encoded by nested rectangles can be challenging, particularly compared to tree-structured representations like node-link diagrams or icicle plots. To address this challenge, we introduce TreEducation, a visual education platform designed to improve the visualization literacy skills required for reading treemaps among non-expert users. TreEducation is an online application that combines visualizations, interactions, and gamification elements to facilitate understanding of eight different treemap layout algorithms and enhance students' learning process. We evaluated TreEducation in a classroom setting and a controlled environment. Our results indicate a significant knowledge gain of students training exclusively with TreEducation and the usefulness of competition as a social gamification element included in our competitive quiz.

Abstract The visual analysis of multivariate network data is a common yet difficult task in many domains. The major challenge is to visualize the network's topology and additional attributes for entities and their connections. Although node‐link diagrams and adjacency matrices are widespread, they have inherent limitations. Node‐link diagrams struggle to scale effectively, while adjacency matrices can fail to represent network topologies clearly. In this paper, we delve into the design space of BioFabric, which aligns entities along rows and relationships along columns, providing a way to encapsulate multiple attributes for both. We explore how we can leverage the unique opportunities offered by BioFabric's design space to visualize multivariate network data — focusing on three main categories: juxtaposed visualizations, embedded on‐node and on‐edge encoding, and transformed node and edge encoding. We complement our exploration with a quantitative assessment comparing BioFabric to adjacency matrices. We postulate that the expansive design possibilities introduced in BioFabric network visualization have the potential for the visualization of multivariate data, and we advocate for further evaluation of the associated design space. Our supplemental material is available on osf.io.

We frequently utilize face emojis to express emotions in digital communication. But how wholly and precisely do such pictographs sample the emotional spectrum, and are there gaps to be closed? Our research establishes emoji intensity scales for seven basic emotions: happiness, anger, disgust, sadness, shock, annoyance, and love. In our survey (N = 1195), participants worldwide assigned emotions and intensities to 68 face emojis. According to our results, certain feelings, such as happiness or shock, are visualized by manifold emojis covering a broad spectrum of intensities. Other feelings, such as anger, have limited and only very intense representative visualizations. We further emphasize that the cultural background influences emojis’ perception: for instance, linear-active cultures (e.g., UK, Germany) rate the intensity of such visualizations higher than multi-active (e.g., Brazil, Russia) or reactive cultures (e.g., Indonesia, Singapore). To summarize, our manuscript promotes future research on more expressive, culture-aware emoji design.

Abstract After a long period of scepticism, more and more publications describe basic research but also practical approaches to how abstract data can be presented in immersive environments for effective and efficient data understanding. Central aspects of this important research question in immersive analytics research are concerned with the use of 3D for visualization, the embedding in the immersive space, the combination with spatial data, suitable interaction paradigms and the evaluation of use cases. We provide a characterization that facilitates the comparison and categorization of published works and present a survey of publications that gives an overview of the state of the art, current trends, and gaps and challenges in current research.

Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visual exploration of hierarchical genome structures in molecular biology. However, creating such multiscale visualizations remains challenging due to the plethora of existing work and the expression ambiguity in visualization research. Up to today, there has been little work to compare and categorize multiscale visualizations to understand their design practices. In this article, we present a structured literature analysis to provide an overview of common design practices in multiscale visualization research. We systematically reviewed and categorized 122 published journal or conference articles between 1995 and 2020. We organized the reviewed articles in a taxonomy that reveals common design factors. Researchers and practitioners can use our taxonomy to explore existing work to create new multiscale navigation and visualization techniques. Based on the reviewed articles, we examine research trends and highlight open research challenges.

Abstract While there are many visualization techniques for exploring numeric data, only a few work with categorical data. One prominent example is Parallel Sets, showing data frequencies instead of data points ‐ analogous to parallel coordinates for numerical data. As nominal data does not have an intrinsic order, the design of Parallel Sets is sensitive to visual clutter due to overlaps, crossings, and subdivision of ribbons hindering readability and pattern detection. In this paper, we propose a set of quality metrics, called ParSetgnostics (Parallel Sets diagnostics), which aim to improve Parallel Sets by reducing clutter. These quality metrics quantify important properties of Parallel Sets such as overlap, orthogonality, ribbon width variance, and mutual information to optimize the category and dimension ordering. By conducting a systematic correlation analysis between the individual metrics, we ensure their distinctiveness. Further, we evaluate the clutter reduction effect of ParSetgnostics by reconstructing six datasets from previous publications using Parallel Sets measuring and comparing their respective properties. Our results show that ParSetgostics facilitates multi‐dimensional analysis of categorical data by automatically providing optimized Parallel Set designs with a clutter reduction of up to 81% compared to the originally proposed Parallel Sets visualizations.

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack of comprehensibility, posing a challenge to understand how each model affects the classification outputs and from where the errors come. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce an interactive workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. The involvement of the user is key to our approach. Therefore, we elaborate on the role of the human and connect our approach to theoretical frameworks on human-centered machine learning. We showcase the usefulness of our approach and the integration of the user via binary and multiclass classification problems. Based on ensembles automatically selected by a standard ensemble selection algorithm, the user can manipulate models and alternative combinations.

Heatmaps are a popular visualization technique that encode 2D density distributions using color or brightness. Experimental studies have shown though that both of these visual variables are inaccurate when reading and comparing numeric data values. A potential remedy might be to use 3D heatmaps by introducing height as a third dimension to encode the data. Encoding abstract data in 3D, however, poses many problems, too. To better understand this tradeoff, we conducted an empirical study (N=48) to evaluate the user performance of 2D and 3D heatmaps for comparative analysis tasks. We test our conditions on a conventional 2D screen, but also in a virtual reality environment to allow for real stereoscopic vision. Our main results show that 3D heatmaps are superior in terms of error rate when reading and comparing single data items. However, for overview tasks, the well-established 2D heatmap performs better.

Movement-compensating interactions like teleportation are commonly deployed techniques in virtual reality environments. Although practical, they tend to cause disorientation while navigating. Previous studies show the effectiveness of orientation-supporting tools, such as trails, in reducing such disorientation and reveal different strengths and weaknesses of individual tools. However, to date, there is a lack of a systematic comparison of those tools when teleportation is used as a movement-compensating technique, in particular under consideration of different tasks. In this paper, we compare the effects of three orientation-supporting tools, namely minimap, trail, and heatmap. We conducted a quantitative user study with 48 participants to investigate the accuracy and efficiency when executing four exploration and search tasks. As dependent variables, task performance, completion time, space coverage, amount of revisiting, retracing time, and memorability were measured. Overall, our results indicate that orientation-supporting tools improve task completion times and revisiting behavior. The trail and heatmap tools were particularly useful for speed-focused tasks, minimal revisiting, and space coverage. The minimap increased memorability and especially supported retracing tasks. These results suggest that virtual reality systems should provide orientation aid tailored to the specific tasks of the users.

Abstract The ability to perceive patterns in parallel coordinates plots (PCPs) is heavily influenced by the ordering of the dimensions. While the community has proposed over 30 automatic ordering strategies, we still lack empirical guidance for choosing an appropriate strategy for a given task. In this paper, we first propose a classification of tasks and patterns and analyze which PCP reordering strategies help in detecting them. Based on our classification, we then conduct an empirical user study with 31 participants to evaluate reordering strategies for cluster identification tasks. We particularly measure time, identification quality, and the users’ confidence for two different strategies using both synthetic and real‐world datasets. Our results show that, somewhat unexpectedly, participants tend to focus on dissimilar rather than similar dimension pairs when detecting clusters, and are more confident in their answers. This is especially true when increasing the amount of clutter in the data. As a result of these findings, we propose a new reordering strategy based on the dissimilarity of neighboring dimension pairs.

Abstract Domain experts for collective animal behavior analyze relationships between single animal movers and groups of animals over time and space to detect emergent group properties. A common way to interpret this type of data is to visualize it as a spatio‐temporal network. Collective behavior data sets are often large, and may hence result in dense and highly connected node‐link diagrams, resulting in issues of node‐overlap and edge clutter. In this design study, in an iterative design process, we developed glyphs as a design for seamlessly encoding relationships and movement characteristics of a single mover or clusters of movers. Based on these glyph designs, we developed a visual exploration prototype, MotionGlyphs, that supports domain experts in interactively filtering, clustering, and animating spatio‐temporal networks for collective animal behavior analysis. By means of an expert evaluation, we show how MotionGlyphs supports important tasks and analysis goals of our domain experts, and we give evidence of the usefulness for analyzing spatio‐temporal networks of collective animal behavior.

Recent developments in technology encourage the use of head-mounted displays (HMDs) as a medium to explore visualizations in virtual realities (VRs). VR environments (VREs) enable new, more immersive visualization design spaces compared to traditional computer screens. Previous studies in different domains, such as medicine, psychology, and geology, report a positive effect of immersion, e.g., on learning performance or phobia treatment effectiveness. Our work presented in this paper assesses the applicability of those findings to a common task from the information visualization (InfoVis) domain. We conducted a quantitative user study to investigate the impact of immersion on cluster identification tasks in scatterplot visualizations. The main experiment was carried out with 18 participants in a within-subjects setting using four different visualizations, (1) a 2D scatterplot matrix on a screen, (2) a 3D scatterplot on a screen, (3) a 3D scatterplot miniature in a VRE and (4) a fully immersive 3D scatterplot in a VRE. The four visualization design spaces vary in their level of immersion, as shown in a supplementary study. The results of our main study indicate that task performance differs between the investigated visualization design spaces in terms of accuracy, efficiency, memorability, sense of orientation, and user preference. In particular, the 2D visualization on the screen performed worse compared to the 3D visualizations with regard to the measured variables. The study shows that an increased level of immersion can be a substantial benefit in the context of 3D data and cluster detection.

Immersive technologies such as augmented reality devices are opening up a new design space for the visual analysis of data. This paper studies the potential of an augmented reality environment for the purpose of collaborative analysis of multidimensional, abstract data. We present ART, a collaborative analysis tool to visualize multidimensional data in augmented reality using an interactive, 3D parallel coordinates visualization. The visualization is anchored to a touch-sensitive tabletop, benefiting from well-established interaction techniques. The results of group-based, expert walkthroughs show that ART can facilitate immersion in the data, a fluid analysis process, and collaboration. Based on the results, we provide a set of guidelines and discuss future research areas to foster the development of immersive technologies as tools for the collaborative analysis of multidimensional data.

Abstract The visualization community has developed to date many intuitions and understandings of how to judge thequalityof views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi‐ and high‐dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.

Many visualizations have proven to be effective in supporting various software related tasks. Although multiple media can be used to display a visualization, the standard computer screen is used the most. We hypothesize that the medium has a role in their effectiveness. We investigate our hypotheses by conducting a controlled user experiment. In the experiment we focus on the 3D city visualization technique used for software comprehension tasks. We deploy 3D city visualizations across a standard computer screen (SCS), an immersive 3D environment (I3D), and a physical 3D printed model (P3D). We asked twenty-seven participants (whom we divided in three groups for each medium) to visualize software systems of various sizes, solve a set of uniform comprehension tasks, and complete a questionnaire. We measured the effectiveness of visualizations in terms of performance, recollection, and user experience. We found that even though developers using P3D required the least time to identify outliers, they perceived the least difficulty when visualizing systems based on SCS. Moreover, developers using I3D obtained the highest recollection.

Abstract Named entity recognition (NER) denotes the task to detect entities and their corresponding classes, such as person or location, in unstructured text data. For most applications, state of the art NER software is producing reasonable results. However, as a consequence of the methodological limitations and the well‐known pitfalls when analyzing natural language data, the NER results are likely to contain ambiguities. In this paper, we present an interactive NER ambiguity resolution technique, which enables users to create (post‐processing) rules for named entity recognition data based on the content and entity context of the analyzed documents. We specifically address the problem that in use‐cases where ambiguities are problematic, such as the attribution of fictional characters with traits, it is often unfeasible to train models on custom data to improve state of the art NER software. We derive an iterative process model for improving NER results, show an interactive NER ambiguity resolution prototype, illustrate our approach with contemporary literature, and discuss our work and future research.

We systematically reviewed 64 user-study papers on data glyphs to help researchers and practitioners gain an informed understanding of tradeoffs in the glyph design space. The glyphs we consider are individual representations of multi-dimensional data points, often meant to be shown in small-multiple settings. Over the past 60 years many different glyph designs were proposed and many of these designs have been subjected to perceptual or comparative evaluations. Yet, a systematic overview of the types of glyphs and design variations tested, the tasks under which they were analyzed, or even the study goals and results does not yet exist. In this paper we provide such an overview by systematically sampling and tabulating the literature on data glyph studies, listing their designs, questions, data, and tasks. In addition we present a concise overview of the types of glyphs and their design characteristics analyzed by researchers in the past, and a synthesis of the study results. Based on our meta analysis of all results we further contribute a set of design implications and a discussion on open research directions.

We conducted three experiments to investigate the effects of contours on the detection of data similarity with star glyph variations. A star glyph is a small, compact, data graphic that represents a multi-dimensional data point. Star glyphs are often used in small-multiple settings, to represent data points in tables, on maps, or as overlays on other types of data graphics. In these settings, an important task is the visual comparison of the data points encoded in the star glyph, for example to find other similar data points or outliers. We hypothesized that for data comparisons, the overall shape of a star glyph--enhanced through contour lines--would aid the viewer in making accurate similarity judgments. To test this hypothesis, we conducted three experiments. In our first experiment, we explored how the use of contours influenced how visualization experts and trained novices chose glyphs with similar data values. Our results showed that glyphs without contours make the detection of data similarity easier. Given these results, we conducted a second study to understand intuitive notions of similarity. Star glyphs without contours most intuitively supported the detection of data similarity. In a third experiment, we tested the effect of star glyph reference structures (i.e., tickmarks and gridlines) on the detection of similarity. Surprisingly, our results show that adding reference structures does improve the correctness of similarity judgments for star glyphs with contours, but not for the standard star glyph. As a result of these experiments, we conclude that the simple star glyph without contours performs best under several criteria, reinforcing its practice and popularity in the literature. Contours seem to enhance the detection of other types of similarity, e. g., shape similarity and are distracting when data similarity has to be judged. Based on these findings we provide design considerations regarding the use of contours and reference structures on star glyphs.

We present the results of a controlled experiment to investigate the performance of different temporal glyph designs in a small multiple setting. Analyzing many time series at once is a common yet difficult task in many domains, for example in network monitoring. Several visualization techniques have, thus, been proposed in the literature. Among these, iconic displays or glyphs are an appropriate choice because of their expressiveness and effective use of screen space. Through a controlled experiment, we compare the performance of four glyphs that use different combinations of visual variables to encode two properties of temporal data: a) the position of a data point in time and b) the quantitative value of this data point. Our results show that depending on tasks and data density, the chosen glyphs performed differently. Line Glyphs are generally a good choice for peak and trend detection tasks but radial encodings are more effective for reading values at specific temporal locations. From our qualitative analysis we also contribute implications for designing temporal glyphs for small multiple settings.

Routing in the Internet is vulnerable to attacks due to the insecure design of the border gateway protocol (BGP). One possible exploitation of this insecure design is the hijacking of IP blocks. Such hijacked IP blocks can then be used to conduct malicious activities from seemingly legitimate IP addresses. In this study we actively trace and monitor the routes to spam sources over several consecutive days after having received a spam message from such a source. However, the real challenge is to distinguish between legitimate routing changes and those ones that are related to systematic misuse in so-called spam campaigns. To combine the strengths of human judgement and computational efficiency, we thus present a novel visual analytics tool named Vistracer in this paper. This tool represents analysis results of our anomaly detection algorithms on large traceroute data sets with the help of several scalable representations to support the analyst to explore, identify and analyze suspicious events and their relations to malicious activities. In particular, pixel-based visualization techniques, novel glyph-based summary representations and a combination of temporal glyphs in a graph representation are used to give an overview of route changes to specific destinations over time. To evaluate our tool, real-world case studies demonstrate the usage of Vistracer in practice on large-scale data sets.

The growing amounts of hosts that are placed into the networks represent an enormous challenge to most network administrators who have to monitor these hosts conscientiously. While automatically monitoring the network for slow or failing components has become common practice, defining an acceptable state of the system is only possible to a very limited extent and thus exploratory analysis tasks by real human analysts complement the analysis process. However, this is a problem of scale since it is infeasible to manually inspect thousands of hosts without proper visual support for the tasks of gaining an overview, focusing and retrieving details on demand. In this paper we present a design study to enable visual support for monitoring large IP spaces. In particular, the presented system features 1) a scalable glyph representation in the style of a clock for giving an overview of the activity over time of thousands of hosts in the network, 2) subnet and port views for focusing the analysis to a particular subset of the data and 3) detailed pixel matrix visualizations for interpreting concrete traffic patterns. Furthermore, the tool's feedback loop, which is implemented through interaction capabilities, allows for retrieving new details, refocusing and enhancing of the overview.