Eye movements play an important role during social interaction, for instance, by indicating phases of mutual and joint attention. This type of gaze behavior has been researched extensively in pair collaboration but has been far less studied in the investigation of group activities. Further, the analysis of attention is often decoupled from speech, even though they are closely linked in social interactions. We introduce a visualization to facilitate joint analysis of gaze and speech among multiple participants during social activities. Our proposed social interaction graphs are two-layered arc diagrams that visualize pairwise relationships among participants. Our evaluation is based on two datasets of multiplayer tabletop gaming sessions recorded with five players. We present a traditional evaluation using eye-tracking metrics as a baseline and compare it with the additional findings possible with our approach.

In this paper, we propose an interactive visualization tool that supports comparative views on eye movement data by splitting the data into data categories based on the independent, user-selected, and automatically detected variables available in the eye movement data. Moreover, the tool is composed of multiple linked, comparative, and coordinated views to visually depict the data in statistical, point- and AOI-based, as well as spatio-temporal visualizations overlaid on the visual stimuli. It supports algorithmic approaches including clustering, trend detection, and dimensionality reduction to aggregate, group, structure, or filter the data. We illustrate the usefulness of the tool by applying it to eye movement data from a public transport map eye tracking study that is publicly available and show perspectives on each of the three variable categories.

In this paper, we describe a visual analytics tool for monitoring and controlling water quality focusing on the Rhine River. We investigated correlations between several water quality metrics in a static and dynamic way. The results of the analysis are visually represented on a dashboard that allows interactive navigation in the data to support the confirmation, rejection, or refinement of the data-related hypotheses. We integrated easy-to-understand visualization techniques for non-experts in visualization in a multiple coordinated view. We illustrate the usefulness of the approach by applying it to water quality data at a measurement station called Weil am Rhein. We focus on data quality metrics including temperature, pH values, oxygen content, and the electric conductivity while we found a major correlation between temperature and oxygen saturation. Finally, we discuss scalability issues and limitations also as some kind of performance analysis before we conclude the paper with future directions.

We investigate mouse movement as an interaction technique to solve tasks in simple visual interface settings. For this purpose, we recruited 17 participants in a controlled within-subjects study. The design of the study is based on six different task groups. As an independent variable, we tested the impact of typical Gestalt principles visually encoded in user interfaces on the dependent variable mouse movement, in particular on movement path trajectories and task completion times until mouse click. The variations of the independent variable take into account Gestalt principles such as proximity, similarity, common region, and figure ground, testing cases in which the principles are followed and cases in which they are disregarded. We illustrate movement paths as trajectory visualizations in three variations showing the paths themselves over space and time, movement speed, and mouse clicks. The main result of the study suggests that visual interfaces that follow Gestalt principles produce shorter mouse movement paths and task completion times.

Irrational numbers are difficult to compute due to their non-rational nature, i.e. they cannot just be expressed as a ratio consisting of two integer numbers. Various examples of these irrational constants exist, like the circle’s circumference to its diameter $\pi$, Euler’s number e, the Golden ratio $\phi$, or the square roots of 2,3, or 5, and the like. Irrational numbers can be expressed as a sequence of decimal digits just like other real numbers, however, they do not terminate nor do they contain some kind of repeating number pattern. The goal of our work is to efficiently compute and store a very large irrational number by computing as many digits as possible. This test is designed to improve our ability to compute and process extremely large datasets in order to prepare ourselves for future real-world applications based on computational-intensive data analytics concepts, hence high-performance computing is required to solve such data-related problems. To reach these goals we make use of two AMD processors with one terabyte of RAM and 64 cores in total, and an aggregated 510 terabytes of disk space. To run such a computation, we employ 38 hard disk drives running at $7,200 \mathrm{rpm}. 34$ of these disks are used as swap space and 4 disks will hold the final digits. We illustrate our approach by applying it to the irrational number $\pi$ and compute 62.8 trillion decimal places of it. Finally, we discuss scalability issues, limitations, challenges, and future directions of our methodology.

Eye tracking studies are more complicated to design, conduct, and to evaluate than traditional studies solely based on performance measures like error rates and response times. This is typically due to the more complex hardware setup, the calibration procedures, and the spatio-temporal nature of the recorded data that must be analyzed, visualized, or statistically evaluated. As a benefit, eye movement data contains patterns of visual attention over space and time that are not observable in standard error rates, completion times, and qualitative feedback. Students in the field of visualization, human-computer interaction, and user experience represent an interest group that would benefit from the application of eye tracking during their studies and in their future careers. Consequently, instructing them how to design, setup, conduct, and evaluate an eye tracking study is of special interest to current researchers involved in teaching. We describe education in eye tracking in five courses with 79 students from bachelor, master, and PhD levels. We outline our concept and discuss the challenges to raise people with no experience in eye tracking to a level of knowledge that allows them to apply this emerging technology to different scenarios including visual stimuli and related research questions. We discuss our teaching strategy in two course setups (summer school and traditional university lecture), the results of the students' eye tracking studies, and which challenges they and the teachers faced during the course.

In the roles of lecturers we have to present certain topics to students by means of Powerpoint slides, videos, animations, and the like. The content itself but also the presentation speed play crucial roles to make a lecture understandable for the students. Since each student has a different experience level, personal mood, or might be distracted by other scenarios the lecturers cannot keep track of all the individual students in a course but has to present the content more or less independently of the audience. In this paper we introduce work-in-progress that focuses on monitoring the students’ behavior in a course including the understanding of the presentation in terms of presentation speed. To reach this goal we make use of mobile eye tracking devices that permanently track the eye movements of the students paying visual attention to the lecturer’s slides. In the current state of the developed system we record two options, whether an individual student could follow and understand the slides or not while the summed up students’ feedback is reflected and displayed to the teacher in real-time, providing some kind of overview and avoiding asking questions permanently about the understanding of the slides’ contents. Finally, we will discuss challenges and limitations of our gaze-based monitoring system.

We introduce FitYou, an interactive dashboard for health data visualizations from wearable devices. It supports the exploration of health trends and health attribute correlations. Wearable devices are small enough to wear on the body and that can be equipped with sensors to track various physical and physiological attributes like heart rate, steps taken, and sleep quality, to just mention a few. By exploiting those attribute values we can visually monitor health-related issues, for example during sports activities like training, a sports competition, or a rehabilitation phase. Activity levels are hard to detect without such support but can help to improve someone’s overall well-being. We illustrate the usefulness of FitYou by means of some application examples. Finally, we discuss the challenges and limitations of the dashboard by focusing on algorithmic, visual, and perceptual issues.

Abstract Event sequence visualization aids analysts in many domains to better understand and infer new insights from event data. Analysing behaviour before or after a certain event of interest is a common task in many scenarios. In this paper, we introduce, formally define, and position double trees as a domain‐agnostic tree visualization approach for this task. The visualization shows the sequences that led to the event of interest as a tree on the left, and those that followed on the right. Moreover, our approach enables users to create selections based on event attributes to interactively compare the events and sequences along colour‐coded categories. We integrate the double tree and category‐based comparison into a user interface for event sequence analysis. In three application examples, we show a diverse set of scenarios, covering short and long time spans, non‐spatial and spatial events, human and artificial actors, to demonstrate the general applicability of the approach.

Educational systems are based on the teaching inputs from teachers and the learning outputs from the pupils/students. Moreover, also the surrounding environment as well as the social activities and networks of the involved people have an impact on the success of such a system. However, in most of them, standard teaching equipment is used while it is difficult to gain some knowledge and insights about the fine-grained spatio-temporal activities in the classroom. On the other hand, understanding why an educational system is not that successful as it was expected, can hardly be explored by just generating statistics about the learning outputs based on several student-related metrics. In this paper we discuss the benefits of using eye tracking as a powerful technology to track people’s visual attention to explore the value of an educational system, however, we also take a look at the drawbacks that come in the form of data recording, storing, processing, and finally, data analysis and visualization to rapidly gain insights in such large datasets apart from many more. We argue that if eye tracking is applied in a clever way, an educational system might draw valuable conclusions to improve it from several perspectives, be it under the light of online/remote or classroom teaching or also from the perspectives of teachers and pupils/students.

Eye movement data can be used for a variety of research in marketing, advertisement, and other design-related industries to gain interesting insights into customer preferences. However, interpreting such data can be a challenging task due to its spatio-temporal complexity. In this paper we describe a web-based tool that has been developed to provide various visualizations for interpreting eye movement data of static stimuli. The tool provides several techniques to visualize and analyze eye movement data. These visualizations are interactive and linked in a coordinated way to help gain more insights. Overall, this paper illustrates the features and functionality offered by the tool by using data recorded from transport map readers in a previously conducted experiment as use case. Furthermore, the paper discusses limitations of the tool and possible future developments.

Creating information visualization tools is a challenging task for which expertise in many research fields is required to get a suitable result for the tasks at hand, for example, to interactively explore data sets for data patterns. To reach a good outcome, it is important to learn how to combine the different concepts from all the related disciplines and to understand what the no-goes are to avoid possible design flaws that hinder a data explorer at solving the tasks at hand. In particular, the newcomers in information visualization have to be taught to follow the rules in visualization, human-computer interaction, but even more the well-known and established design criteria to avoid the same mistakes as others did before. In this paper we describe a teaching concept to guide bachelor students with nearly no experience in information visualization to design their own visual interfaces focusing on data visualization problems. We illustrate the results from the course in several application examples showcasing the visual designs that students came up with.

Earthquakes are one of the most destructive natural forces on Earth. Helping uncover and understand the hidden patterns behind the occurrence of Earthquakes is vital for preparation and emergency response. Unfortunately, it is difficult to do so as those patterns probably depend on too many variables to be easily perceived with common visualization techniques. In this paper we expand on a technique designed for eye movement data and apply it to spatio-temporal earthquake events. This visualization combines the spatial and temporal dimension into a line-based splatted diagram that provides an overview and serves as a starting point for further data explorations in space and time.

Geospatial planning drives the population-resource-environment system and guarantees sustainable development. In Switzerland, the development of vacancies can be observed in various municipalities. Hence, no optimal geospatial planning is taking place. In this paper, a visual representation in combination with cluster analysis is presented to give the planners support in geospatial shaping. These methods allow to identify commonalities in municipalities and reveal potential options for a geospatial development. For this purpose, public data for eight municipalities are gathered from the Swiss Federal Statistical Office and Open Street Map. Our results give clear commonalities for each municipality and allow to determine the places which have more development resources.

The number π has motivated mathematicians and math enthusiasts for millennia. John Venn was one of the first to use visualizations with π. In this paper, we use visualizations to reveal patterns in the first 62.8 trillion digits of π. We computed the distribution of natural numbers and identified the longest sequences of the Fibonacci sequence, the longest sequences of the natural numbers and identified the longest series of the same digit. We observed that no regular distribution of the natural numbers appears.

Humans are able to associate colors with certain objects. However, if the color is missing like in grayscale images it is questionable if such associations can be built the same way. To investigate this aspect we conducted an eye tracking study. We found out that the visual attention behavior for colored objects is different from grayscale objects.

In machine learning, the presumably best model is selected from a variety of model candidates generated by testing different model types, hyperparameters, or feature subsets. The advent of deep learning has made model selection even more challenging due to the huge parameter search space. Relying on a single metric to select the best model does not consider class imbalances or the different costs of misclassifications. We argue that incorporating human knowledge to interactively analyse the per-class errors and class confusions over all model candidates enables a more efficient training process and yields better models for given applications. This paper proposes the model-agnostic approach ConfusionVis which allows to comparatively evaluate and select multi-class classifiers based on their confusion matrices. This contributes to making the models’ results understandable, while treating the models as black boxes. Therefore, we propose a novel method to measure and visualise distances between confusion matrices and an interactive query interface to incorporate all composition levels of class errors. The approach is evaluated in a user study and the applicability is shown by a case study where marine biologists investigate the conservation efforts of baleen whales by classifying whale species in acoustic recordings. ConfusionVis is available online: https://www.ml-and-vis.org/confusionvis.

Eye movements are closely related to the cognitive processes and often act as a window to the brain and mind. To facilitate a window to the brain using eye movements, we propose EyeFIX, an interactive visual analytics interface. Our interface currently focuses on processing gaze movements to generate the two most prominent events, fixations, and saccades which are used in a large part of the eye movement literature. Our interface has multiple interactive widgets that allow the users to choose the algorithm and tune its hyper parameters that control how fixations and saccades are generated. It has four major visualizations: (1) A Gaze Plot to showcase the gaze movement of each participant, (2) a Fixation Plot that helps to identify regions of the stimulus which are of interest to a particular participant, (3) a Heat Map to study the denser regions of fixation and regions which the participant frequently visited, and (4) a Timeline Visualization to assist in having a closer look at temporal regions of interest. We design a set of brushing and linking operators that allow users to interactively control all the visualizations to dig deeper in both, the spatial and temporal domains. We demonstrate the applicability of our interface with datasets obtained by human subjects viewing naturalistic stimuli while performing various viewing tasks, including visual exploration, visual search, and prolonged visual fixation.

In this paper we describe a visualization tool for representing the dynamics of graph algorithms. Toward this end, we designed a web-based framework which illustrates the dynamics as time-to-space mappings of dynamic graphs. Such static diagrams of dynamic data have the benefit of being able to display longer time spans in one view, hence supporting the observer with comparison tasks. The tool can show details about how an algorithm traverses a graph step-by-step in a static and animated fashion, for graph algorithm exploration as well as educational purposes. The animation together with the time-to-space mapping forms an overview-and-detail approach. By using flight carrier data from the U.S. Department of Transportation we show the usefulness of our interactive visualization for conveying graph algorithm dynamics.

Data on education contains various attributes, modeling the pupils’ or students’ personal properties or educational outcomes like reading or writing performances, mathematical skills, or linguistic competences. In this paper we describe a visual analysis tool that takes into account a multitude of educational data sources, filters those into the most relevant ones, and derives attributes-of-interest which form a high-dimensional dataset. For this data we apply t-SNE as one possible projection method to visualize and uncover similarities and dissimilarities among the high-dimensional attributes in a lower-dimensional space like in a scatter plot. We also support interaction techniques and further visual variables to encode additional data attributes in the form of color codings, sizes, and shapes. We illustrate the usefulness of our approach by applying it to educational data sources focusing on pupils and students in Switzerland while also visually communicating the found insights with educational science experts. Finally, we discuss limitations and scalability issues.

This paper describes the space-reclaiming icicle plots, hierarchy visualizations based on the visual metaphor of icicles. As a novelty, our approach tries to reclaim empty space in all hierarchy levels. This reclaiming results in an improved visibility of the hierarchy elements especially those in deeper levels. We implemented an algorithm that is capable of producing more space-reclaiming icicle plot variants. Several visual parameters can be tweaked to change the visual appearance and readability of the plots: among others, a space-reclaiming parameter, an empty space shrinking parameter, and a gap size. To illustrate the usefulness of the novel visualization technique we applied it, among others, to an NCBI taxonomy dataset consisting of more than 300,000 elements and with maximum depth 42. Moreover, we explore the parameter and design space by applying several values for the visual parameters. We also conducted a controlled user study with 17 participants and received qualitative feedback from 112 students from a visualization course.

Making students aware of eye tracking technologies can have a great benefit on the entire application field since they may build the next generation of eye tracking researchers. On the one hand students learn the usefulness and benefits of this technique for different scientific purposes like user evaluation to find design flaws or visual attention strategies, gaze-assisted interaction to enhance and augment traditional interaction techniques, or as a means to improve virtual reality experiences. However, on the other hand, the large amount of recorded data means a challenge for data analytics in order to find rules, patterns, but also anomalies in the data, finally leading to insights and knowledge to understand or predict eye movement patterns which can have synergy effects for both disciplines - eye tracking and visual analytics. In this paper we will describe the challenges of teaching eye tracking combined with visual analytics in a computer and data science bachelor course with 42 students in an active learning scenario following four teaching stages. Some of the student project results are shown to demonstrate learning outcomes with respect to eye tracking data analysis and visual analytics techniques.

In this paper we present a procedural city modeling approach which combines real-world street data and the multi-nuclei theory to generate believable cities. Our approach performs a partitioning of a city into urban zones based on Perlin noise and different parameters that are adjustable by the user. Our method is efficient enough to be used for augmented reality applications to be run on devices with limited processing capabilities such as smartphones. The approach can be used to build applications for professional urban planners and entertainment purposes. We illustrate the usefulness of our approach by applying it to data of three cities. Moreover, we provide implementation details and discuss challenges and limitations of the technique in the light of several application scenarios.

Soccer is one of the most popular sports in the world, played by thousands of professionals and amateurs every week. Consequently, it is no surprise that it generates an enormous amount of data. In today's data-driven world it is essential to find an optimal, self-explanatory, way to present the data in a way to be able to derive visual patterns that relate to the underlying data patterns. In this paper, we describe an interactive visualization for analyzing soccer data and identifying patterns, correlations, and insights. We illustrate the usefulness of our approach, especially targeted towards non-visualization experts, by applying it to World Cup data and by discussing potential use cases.

Visualizing graphs is a challenging task due to the various properties of the underlying relational data. For sparse and small graphs the perceptually most efficient way are node-link diagrams whereas for dense graphs with attached data, adjacency matrices might be the better choice. Since graphs can contain both properties, being globally sparse and locally dense, a combination of several visualizations is beneficial. In this paper we describe a visually and algorithmically scalable approach to provide views and perspectives about graphs as interactively linked node-link as well as adjacency matrix visualizations. The novelty of the technique is that insights like clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the others. Moreover, the importance of nodes and node groups can be detected, computed, and visualized by taking into account several layout and reordering properties in combination as well as different edge properties for the same set of nodes. We illustrate the usefulness of our tool by applying it to graph datasets like co-authorships, co-citations, and a CPAN distribution.

Eye movement data is high-dimensional, and therefore hard to visualize. In this paper we focus on a dataset of scanpaths: Eye movements performed by subjects and tracked during a task which is based on path-finding. We describe comparisons of different approaches of dimensionality reduction applied to eye movement data, including t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), principal component analysis (PCA), and metric multidimensional scaling (MDS). We describe a tool created to analyze and compare these different methods, and perform a case study in which we explore an eye movement dataset.

We describe an interactive application for transforming an image into a Voronoi diagram. We combine a variety of methods for generating a spatial point cloud from an input image. In addition, several methods for pruning the spatial point cloud are introduced. These pruning methods can significantly reduce the computation time needed for the transformation. A Voronoi diagram can be constructed from the pruned spatial point cloud using either a naive approach or a Delaunay triangulation. Moreover, an order-k Voronoi diagram can be constructed using this naive approach. We introduce many configuration parameters and we integrate interactivity in the Voronoi diagram by giving users the ability to manually add and remove centroids. To make the application accessible to everyone, we provide a web-based solution by using the Vue.js framework based on the JavaScript programming language. This solution supports the transformation from an image to a Voronoi diagram in a browser, and hence, has the advantage of not being restricted to a certain kind of environment. We illustrate the usefulness of our application by applying it to several images.

Public transport maps are typically designed in a way to support route finding tasks for passengers while they also provide an overview about stations, metro lines, and city-specific attractions. Most of those maps are designed as a static representation, maybe placed in a metro station or printed in a travel guide. In this paper we describe a dynamic, interactive public transport map visualization enhanced by additional views for the dynamic passenger data on different levels of temporal granularity. Moreover, we also allow extra statistical information in form of density plots, calendar-based visualizations, and line graphs. All this information is linked to the contextual metro map to give a viewer insights into the relations between time points and typical routes taken by the passengers. We illustrate the usefulness of our interactive visualization by applying it to the railway system of Hamburg in Germany while also taking into account the extra passenger data. As another indication for the usefulness of the interactively enhanced metro maps we conducted a user experiment with 20 participants.

In this paper we describe an approach for visualizing the textual information archived in the DBLP and the static and dynamic relations contained in it. Those relations are existing between authors and co-authors, between keywords, but also between authors and keywords. Visually representing them provides a way to quickly get an overview about emerging or disappearing topics as well as researchers and researcher groups. To reach our goal we apply node-link diagrams, word clouds, heatmaps, and area plots to the preprocessed and transformed DBLP data. All visualizations are equipped with interaction techniques and are built by using the functionality of the Bokeh library in Python, which enables the users to run the eDBLP in a web browser and to explore the dataset in an interactive and intuitive way. Finally, we discuss limitations and scalability issues of our approach.

In this paper we present a web-based interactive tool for visualizing hierarchical data. Our main purpose is to facilitate the visualization of datasets. We achieve this by offering VizWick in a browser environment, with no requirement of additional software. We provide the option to view the same dataset from multiple coordinated perspectives, thus providing the possibility to gain more analytical insight than if the dataset was visualized in a single view. We focus on several hierarchy visualization techniques which can be either in 2D, 3D, or a virtual reality environment. The choice of programming language is JavaScript, with the aid of PixiJS and Three.js libraries. We demonstrate the usefulness of our tool by applying it to the NCBI taxonomy, a hierarchically structured dataset which contains over 300,000 elements.

This paper describes an interactive web-based tool for visualizing hierarchical data including the recently developed concept of space-reclaiming icicle plots and several more traditional hierarchy visualizations. The tool provides ways to upload, share, explore, and compare hierarchical data using a multitude of different linked hierarchy visualizations. The current version supports up to 8 hierarchy visualizations, with the space-reclaiming icicle plots among them. Several of the visualizations can be shown in linked views while typical hierarchy parameters and visual variables can be changed on user demand. The interactive tool makes use of OpenGL and Angular, an industry standard JavaScript platform, and runs in a web browser. We illustrate the usefulness of the visualization tool by applying it to the NCBI taxonomy that consists of more than 300,000 hierarchically organized species while filtering for the tetrapoda subhierarchy. Finally, we explain implementation details and discuss limitations and scalability issues of the linked visualization techniques.

In this paper an overview-based interactive visualization for temporally long dynamic graph sequences is described. To reach this goal, each graph can be mapped to a certain value based on a given property. Among others, a property can be number of vertices, number of edges, average degree, density, number of self-loops, degree (maximum and total), or edge weight (minimum, maximum, and total). To achieve an overview over time, an aggregation strategy based on either the mean, minimum, or maximum of two values is applied. This temporal value aggregation generates a triangular shape with an overview of the entire graph sequence as the peak. The color coding can be adjusted, forming visual patterns that can be rapidly explored for certain data features over time, supporting comparison tasks between the properties. The usefulness of the approach is illustrated by means of applying it to dynamic graphs generated from US domestic flight data.

Analyzing and visualizing eye movement data can provide useful insights into the connectivities and linkings of points and areas of interest (POIs and AOIs). Those typically time-varying relations can give hints about applied visual scanning strategies by either individual or many eye tracked people. However, the challenging issue with this kind of data is its spatio-temporal nature requiring a good visual encoding in order to first, achieve a scalable overview-based diagram, and second, to derive static or dynamic patterns that might correspond to certain comparable visual scanning strategies. To reliably identify the dynamic strategies we describe a visualization technique that generates a more linear representation of the spatio-temporal scan paths. This is achieved by applying different visual encodings of the spatial dimensions that typically build a limitation for an eye movement data visualization causing visual clutter effects, overdraw, and occlusions while the temporal dimension is depicted as a linear time axis. The presented interactive visualization concept is composed of three linked views depicting spatial, metrics-related, as well as distance-based aspects over time.

Eye movements recorded for many study participants are difficult to interpret, in particular when the task is to identify similar scanning strategies over space, time, and participants. In this paper we describe an approach in which we first compare scanpaths, not only based on Jaccard (JD) and bounding box (BB) similarities, but also on more complex approaches like longest common subsequence (LCS), Frechet distance (FD), dynamic time warping (DTW), and edit distance (ED). The results of these algorithms generate a weighted comparison matrix while each entry encodes the pairwise participant scanpath comparison strength. To better identify participant groups of similar eye movement behavior we reorder this matrix by hierarchical clustering, optimal-leaf ordering, dimensionality reduction, or a spectral approach. The matrix visualization is linked to the original stimulus overplotted with visual attention maps and gaze plots on which typical interactions like temporal, spatial, or participant-based filtering can be applied.

Yarbus' claim to decode the observer's task from eye movements has received mixed reactions. In this paper, we have supported the hypothesis that it is possible to decode the task. We conducted an exploratory analysis on the dataset by projecting features and data points into a scatter plot to visualize the nuance properties for each task. Following this analysis, we eliminated highly correlated features before training an SVM and Ada Boosting classifier to predict the tasks from this filtered eye movements data. We achieve an accuracy of 95.4% on this task classification problem and hence, support the hypothesis that task classification is possible from a user's eye movement data.

In this paper we describe an interactive and web-based visual analytics tool combining linked visualization techniques and algorithmic approaches for exploring the hierarchical visual scanning behavior of a group of people when solving tasks in a static stimulus. This has the benefit that the recorded eye movement data can be observed in a more structured way with the goal to find patterns in the common scanning behavior of a group of eye tracked people. To reach this goal we first preprocess and aggregate the scanpaths based on formerly defined areas of interest (AOIs) which generates a weighted directed graph. We visually represent the resulting AOI graph as a modified hierarchical graph layout. This can be used to filter and navigate in the eye movement data shown in a separate view overplotted on the stimulus for preserving the mental map and for providing an intuitive view on the semantics of the original stimulus. Several interaction techniques and complementary views with visualizations are implemented. Moreover, due to the web-based nature of the tool, users can upload, share, and explore data with others. To illustrate the usefulness of our concept we apply it to real-world eye movement data from a formerly conducted eye tracking experiment.

Eye tracking studies have been conducted to understand the visual attention in different scenarios like, for example, how people read text, which graphical elements in a visualization are frequently attended, how they drive a car, or how they behave during a shopping task. All of these scenarios - either static or dynamic - show a visual stimulus in which the spectators are not able to change the visual content they see. This is different if interaction is allowed like in (graphical) user interfaces (UIs), integrated development environments (IDEs), dynamic web pages (with different user-defined states), or interactive displays in general as in human-computer interaction, which gives a viewer the opportunity to actively change the stimulus content. Typically, for the analysis and visualization of time-varying visual attention paid to a web page, there is a big difference for the analytics and visualization approaches - algorithmically as well as visually - if the presented web page stimulus is static or dynamic, i.e. time-varying, or dynamic in the sense that user interaction is allowed. In this paper we discuss the challenges for visual analysis concepts in order to analyze the recorded data, in particular, with the goal to improve interactive stimuli, i.e., the layout of a web page, but also the interaction concept. We describe a data model which leads to interaction graphs, a possible way to analyze and visualize this kind of eye movement data.

In this paper, we describe the design of an interactive visualization tool for the comparison of eye movement data with a special focus on the outliers. In order to make the tool usable and accessible to anyone with a data science background, we provide a web-based solution by using the Dash library based on the Python programming language and the Python library Plotly. Interactive visualization is very well supported by Dash, which makes the visualization tool easy to use. We support multiple ways of comparing user scanpaths like bounding boxes and Jaccard indices to identify similarities. Moreover, we support matrix reordering to clearly separate the outliers in the scanpaths. We further support the data analyst by complementary views such as gaze plots and visual attention maps.

In this paper we describe an interactive web-based visual analysis tool for Formula one races. It first provides an overview about all races on a yearly basis in a calendar-like representation. From this starting point, races can be selected and visually inspected in detail. We support a dynamic race position diagram as well as a more detailed lap times line plot for showing the drivers' lap times in comparison. Many interaction techniques are supported like selections, filtering, highlighting, color coding, or details-on-demand. We illustrate the usefulness of our visualization tool by applying it to a Formula one dataset while we describe the different dynamic visual racing patterns for a number of selected races and drivers.

The growing number of students can be a challenge for teaching visualization lectures, supervision, evaluation, and grading. Moreover, designing a visualization course, matching the different experiences and skills of the students is one major goal in order to find a common solvable task for all of the students. However, the given task is important to follow a common goal, to collaborate in small project groups, but also to further experience, learn, or extend programming skills. In this paper we describe an approach to manage a large number of 272 students in a design-based active learning course who were relatively unexperienced first year bachelor students with a wide range of programming skills. We explain different subsequent stages to successfully handle the upcoming problems and describe how many and to which extent supervisors are involved in the development of the project. The project task description is given in a way that it has a minimal number of requirements but can be extended in many directions while most of the decisions are up to the students like programming languages, visualization approaches, or interaction techniques. Finally, we discuss the benefits and drawbacks of our teaching strategy.

In this paper, we discuss and evaluate the advantages and disadvantages of several techniques to visualize and analyze eye movement data tracked and recorded from public transport map viewers in a formerly conducted eye tracking experiment. Such techniques include heat maps and gaze stripes. To overcome the disadvantages and improve the effectiveness of those techniques, we present a viable solution that makes use of existing techniques such as heat maps and gaze stripes, as well as attention clouds which are inspired by the general concept of word clouds. We also develop a web application with interactive attention clouds, named the EyeCloud, to put theory into practice. The main objective of this paper is to help public transport map designers and producers gain feedback and insights on how the current design of the map can be further improved, by leveraging on the visualization tool. In addition, this visualization tool, the EyeCloud, can be easily extended to many other purposes with various types of data. It could be possibly applied to entertainment industries, for instance, to track the attention of the film audiences in order to improve the advertisements.

Eye movement data can be regarded as a set of scan paths, each corresponding to one of the visual scanning strategies of a certain study participant. Finding common subsequences in those scan paths is a challenging task since they are typically not equally temporally long, do not consist of the same number of fixations, or do not lead along similar stimulus regions. In this paper we describe a technique based on pairwise and multiple sequence alignment to support a data analyst to see the most important patterns in the data. To reach this goal the scan paths are first transformed into a sequence of characters based on metrics as well as spatial and temporal aggregations. The result of the algorithmic data transformation is used as input for an interactive consensus matrix visualization. We illustrate the usefulness of the concepts by applying it to formerly recorded eye movement data investigating route finding tasks in public transport maps.

We present an approach for interactively analyzing large dynamic graphs consisting of several thousand time steps with a particular focus on temporal aspects. we employ a static representation of the time-varying graph based on the concept of space-time cubes, i.e., we create a volumetric representation of the graph by stacking the adjacency matrices of each of its time steps. To achieve an efficient analysis of complex data, we discuss three classes of analytics methods of particular importance in this context: data views, aggregation and filtering, and comparison. For these classes, we present a GPU-based implementation of respective analysis methods that enable the interactive analysis of large graphs. We demonstrate the utility as well as the scalability of our approach by presenting application examples for analyzing different time-varying data sets.

Analyzing and visualizing call relations can provide useful insights into the connectivities and linkings of certain parts of a software system. This can in particular be a good strategy to find software system parts that are interlinked a lot while others typically occur as more or less stand-alone components not called by many others. The challenging problem with call relation data comes from the dynamics of the data, i.e., a call graph can be changing either during the development of a software system or during the execution of the software. The second case mostly leads to long graph sequences changing on a fine-granular temporal scale requiring a suitable overview-based dynamic graph visualization technique. Moreover, identifying certain temporal patterns in the graph evolution can help to detect certain phases of either the evolution of a software system or phases during the execution that can show which components are connected while someone interacts with the runnable software for example. This can particularly be based on graph, layout, or attribute properties, all providing different perspectives on the dynamics of the graph data. We illustrate the usefulness of our visualization technique by applying it to the open source software project JHotDraw. The call graphs are recorded during runtime while typical user interactions are applied.

In this paper we describe the visual notifier which is a timeline-based visualization that provides an overview about incoming notifications while it also supports an easier way to manage such notifications from several environments. Moreover, it is also possible to link them by certain subject types, author names or groups, or textual content. Such a visualization is in particular useful if there are too many notifications occurring in a short time period to answer and react on immediately, or to keep track of. The interactive visualization is implemented in Java and is easily extendable by additional functionality and interaction techniques. We illustrate theusefulness bytesting itforseveralemailaddresses andmessages from Facebook, Twitter, Ebay, Linkedin, and Researchgate.

Lots of movies are produced every year, too many to watch all of them and in particular, to get an overview about the evolution of typical movie genres and actors playing in them. Moreover, it is a challenging problem to detect correlations among the movies and the actors in those movies, in particular, if we are interested in time-varying data patterns like trends, countertrends, or anomalies and outliers. Those correlations are specifically interesting if they can be inspected on different levels of granularity, e.g., temporal, but also hierarchical in form of country- or continent-based correlations. In this paper we describe the IMDb Explorer, a web-based visualization tool that consists of two major views denoted by the movie cosmos and the career lines. Both views are linked and interactively manipulable while a list of user-defined metrics are explorable. We illustrate the usefulness of the visualization tool by applying it to the entire movie database provided by IMDb.

In this paper we take a look into typical interaction scenarios when it comes to visualizing hierarchical data while we particularly focus on one specific candidate that is the visual depiction by generalized Pythagoras trees. Those are visualizations that benefit from their aesthetically appealing appearance since they come close to a visual model of trees growing in nature. Traditionally, those generalized Pythagoras trees are visualized on typical computer screens, clearly indicating the hierarchy, but negatively, the finer substructures are oftentimes not visible anymore due to the display space restriction and the low resolution. Hence, in this paper we describe the generalized Pythagoras trees displayed on largescale high-resolution displays and illustrate the challenges and perspectives when we have to interact with the visual representation by using the technique individually, but also in collaboration. To explore possible interactions we experimented with tablet computers combined with an optical tracking system. We evaluate the effectiveness and aesthetics by means of several modifiable visual parameters and by interacting with the visualization.

Partially drawn links are a possibility to reduce visual clutter in node-link visualizations of relational data caused by link crossings. Although partial links have some benefits concerning task performance, they exhibit issues regarding target node ambiguities. In this paper, we provide the results of a user study that investigates the performance in terms of task accuracy when judging target nodes to which partial links are pointing. We vary the link lengths and link directions as independent variables and measure the task accuracy as dependent variable while the exposure duration for each stimulus is fixed. The major result of our user study is that people tend to make more target node judgment errors with shorter link lengths. Moreover, the direction of the partial links also has an impact on the accuracy. With these results, we are able to choose appropriate parameter settings for graphs drawn with partial links. This can be regarded as a novel graph drawing criterion for improving the readability of graphs represented with partial links.

Dynamic graphs are typically represented in a time-to-space mapping with the goal to preserve the mental map in order to reduce cognitive efforts for comparison tasks. Such a mapping from time to space has the general drawback that space limitations are sooner reached than in corresponding time-to-time mappings to which graph animation belongs. Consequently, to get an overview about the dynamics in a graph sequence, space-efficient and compact visual encodings are used to show as many graphs in the sequence as possible. Temporal graph aggregation is hence a clever data transformation strategy, but negatively, it does not provide an overview about individual graphs nor does it show graph subsequences on finer time granularities. In this paper we describe a visualization technique that can visualize dynamic graphs in a time-to-space mapping and additionally, allows the graph analyst to interactively explore the dynamic graph data on different temporal granularities. Moreover, if the dynamic graph data is rather dense, it can be filtered by selecting density intervals. We illustrate the usefulness of our visualization tool by applying it to a dynamic graph dataset that simulates time-continuously changing graphs.

We investigate the problem of analyzing word frequencies in multiple text sources with the aim to give an overview of word-based similarities in several texts as a starting point for further analysis. To reach this goal, we designed a visual analytics approach composed of typical stages and processes, combining algorithmic analysis, visualization techniques, the human users with their perceptual abilities, as well as interaction methods for both the data analysis and the visualization component. By our algorithmic analysis, we first generate a multivariate dataset where words build the cases and the individual text sources the attributes. Real-valued relevances express the significances of each word in each of the text sources. From the visualization perspective, we describe how this multivariate dataset can be visualized to generate, confirm, rebuild, refine, or reject hypotheses with the goal to derive meaning, knowledge, and insights from several text sources. We discuss benefits and drawbacks of the visualization approaches when analyzing word relevances in multiple texts.

The visual analysis of eye movement data is a challenging task since the data has an inherent spatio-temporal nature and is typically recorded by measuring the eye movements of various study participants and sometimes, over long time periods. Finding similar strategies among the participants while applying a visual scanning strategy is one of the most important but also most difficult tasks. In this paper we describe the eye movement plots with which we are able to visually encode the eye movement fixation sequences of several study participants as a stacked line-based representation. The two-dimensional fixation points are mapped to a time-dependent sequence of lines while edge splatting is applied to visually augment the otherwise cluttered diagrams. With this visualization concept, time-varying eye movement patterns become visible, comparable, and generate an overview representation for a set of eye movements allowing comparison tasks. We illustrate the usefulness of the technique by means of applying it to formerly recorded eye tracking data by investigating route finding tasks in public transport systems. Moreover, we describe challenges, limitations, and scalability issues of the approach.

Painting galleries typically provide a wealth of data composed of several data types. Those multivariate data are too complex for laymen like museum visitors to first, get an overview about all paintings and to look for specific categories. Finally, the goal is to guide the visitor to a specific painting that he wishes to have a more closer look on. In this paper we describe an interactive visualization tool that first provides such an overview and lets people experiment with the more than 41,000 paintings collected in the web gallery of art. To generate such an interactive tool, our technique is composed of different steps like data handling, algorithmic transformations, visualizations, interactions, and the human user working with the tool with the goal to detect insights in the provided data. We illustrate the usefulness of the visualization tool by applying it to such characteristic data and show how one can get from an overview about all paintings to specific paintings.

Visualizing time series data with a spatial context is a problem that appears more and more often, since small and lightweight GPS devices allow us to enrich the time series data with position information. One example is the visualization of the energy output of power plants. We present a web-based application that aims to provide information about the energy production of a specified region, along with location information about the power plants. The application is intended to be used as a solid data basis for political discussions, nudging, and story telling about the German energy transition to renewables, called "Energiewende". It was therefore designed to be intuitive, easy to use, and provide information for a broad spectrum of users that do not need any domain-specific knowledge. Users are able to select different categories of power plants and look up their positions on an overview map. Glyphs indicate their exact positions and a selection mechanism allows users to compare the power output on different time scales using stacked area charts or ThemeRivers. As an evaluation of the application, we have collected web access statistics and conducted an online survey with respect to the intuitiveness, usability, and informativeness.

Software systems can grow large, consisting of thousands of hierarchically organized elements like directories, subdirectories, files, and functions. Moreover, those hierarchy elements can carry additional information worth investigating for a software developer. Getting an overview of both the hierarchy and the attached static or dynamic data can become a tedious task if it is not supported by a visually scalable visualization technique. In this paper, we use a hierarchy visualization based on the visual metaphor of indentation to generate an overview of the software system hierarchy and easily attach additional attributes. The extra information is aligned with the hierarchy elements and, hence, supports visual comparisons of the attachments on different levels of hierarchical granularity. Through interaction, we provide additional views on the data, e.g., by filtering, hierarchy transformations, or details-on-demand. We illustrate the usefulness of our hierarchy visualization technique by means of an application example exploring data from the open-source software project jEdit. We investigated the readability of the hierarchy visualization with a user experiment, comparing indentation to node-link diagrams for varying sizes of a hierarchy.

Abstract Dynamic graph visualization focuses on the challenge of representing the evolution of relationships between entities in readable, scalable and effective diagrams. This work surveys the growing number of approaches in this discipline. We derive a hierarchical taxonomy of techniques by systematically categorizing and tagging publications. While static graph visualizations are often divided into node‐link and matrix representations, we identify the representation of time as the major distinguishing feature for dynamic graph visualizations: either graphs are represented as animated diagrams or as static charts based on a timeline. Evaluations of animated approaches focus on dynamic stability for preserving the viewer's mental map or, in general, compare animated diagrams to timeline‐based ones. A bibliographic analysis provides insights into the organization and development of the field and its community. Finally, we identify and discuss challenges for future research. We also provide feedback from experts, collected with a questionnaire, which gives a broad perspective of these challenges and the current state of the field.

Abstract This survey provides an introduction into eye tracking visualization with an overview of existing techniques. Eye tracking is important for evaluating user behaviour. Analysing eye tracking data is typically done quantitatively, applying statistical methods. However, in recent years, researchers have been increasingly using qualitative and exploratory analysis methods based on visualization techniques. For this state‐of‐the‐art report, we investigated about 110 research papers presenting visualization techniques for eye tracking data. We classified these visualization techniques and identified two main categories: point‐based methods and methods based on areas of interest. Additionally, we conducted an expert review asking leading eye tracking experts how they apply visualization techniques in their analysis of eye tracking data. Based on the experts' feedback, we identified challenges that have to be tackled in the future so that visualizations will become even more widely applied in eye tracking research.

Abstract The visualization of dynamic graphs demands visually encoding at least three major data dimensions: vertices, edges, and time steps. Many of the state‐of‐the‐art techniques can show an overview of vertices and edges but lack a data‐scalable visual representation of the time aspect. In this paper, we address the problem of displaying dynamic graphs with a thousand or more time steps. Our proposed interleaved parallel edge splatting technique uses a time‐to‐space mapping and shows the complete dynamic graph in a static visualization. It provides an overview of all data dimensions, allowing for visually detecting time‐varying data patterns; hence, it serves as a starting point for further data exploration. By applying clustering and ordering techniques on the vertices, edge splatting on the links, and a dense time‐to‐space mapping, our approach becomes visually scalable in all three dynamic graph data dimensions. We illustrate the usefulness of our technique by applying it to call graphs and US domestic flight data with several hundred vertices, several thousand edges, and more than a thousand time steps.

Visual search can be time-consuming, especially if the scene contains a large number of possibly relevant objects. An instance of this problem is present when using geographic or schematic maps with many different elements representing cities, streets, sights, and the like. Unless the map is well-known to the reader, the full map or at least large parts of it must be scanned to find the elements of interest. In this paper, we present a controlled eye-tracking study (30 participants) to compare four variants of map annotation with labels: within-image annotations, grid reference annotation, directional annotation, and miniature annotation. Within-image annotation places labels directly within the map without any further search support. Grid reference annotation corresponds to the traditional approach known from atlases. Directional annotation utilizes a label in combination with an arrow pointing in the direction of the label within the map. Miniature annotation shows a miniature grid to guide the reader to the area of the map in which the label is located. The study results show that within-image annotation is outperformed by all other annotation approaches. Best task completion times are achieved with miniature annotation. The analysis of eye-movement data reveals that participants applied significantly different visual task solution strategies for the different visual annotations.

We facilitate the comparative visual analysis of eye tracking data from multiple participants with a visualization that represents the temporal changes of viewing behavior. Common approaches to visually analyze eye tracking data either occlude or ignore the underlying visual stimulus, impairing the interpretation of displayed measures. We introduce fixation-image charts: a new technique to display the temporal changes of fixations in the context of the stimulus without visual overlap between participants. Fixation durations, the distance and direction of saccades between consecutive fixations, as well as the stimulus context can be interpreted in one visual representation. Our technique is not limited to static stimuli, but can be applied to dynamic stimuli as well. Using fixation metrics and the visual similarity of stimulus regions, we complement our visualization technique with an interactive filter concept that allows for the identification of interesting fixation sequences without the time-consuming annotation of areas of interest. We demonstrate how our technique can be applied to different types of stimuli to perform a range of analysis tasks. Furthermore, we discuss advantages and shortcomings derived from a preliminary user study.

In this short paper, we present a lightweight application for the interactive annotation of eye tracking data for both static and dynamic stimuli. The main functionality is the annotation of fixations that takes into account the scanpath and stimulus. Our visual interface allows the annotator to work through a sequence of fixations, while it shows the context of the scanpath in the form of previous and subsequent fixations. The context of the stimulus is included as visual overlay. Our application supports the automatic initial labeling according to areas of interest (AOIs), but is not dependent on AOIs. The software is easily configurable, supports user-defined annotation schemes, and fits in existing workflows of eye tracking experiments and the evaluation thereof by providing import and export functionalities for data files.

Static or dynamic graphs are typically visualized by either node-link diagrams, adjacency matrices, adjacency lists, orhybrids thereof. In particular, for the case of a changing graphstructure a viewer wishes to be able to visually compare thegraphs in a sequence. Doing such a comparison task rapidlyand reliably can give support to visually analyze the dynamicgraph for certain dynamic patterns. In this paper we describe a novel dynamic graph visualization that is based on the concept of smooth density fields generated by first splatting the links of a given graph in a certain layout. To further visually enhance the time-varying graph structures we add user-adaptable isolines to the resulting dynamic graph representation. The computed visual encoding of the dynamic graph is aesthetically appealing due to its smooth curves and can additionally be used to do comparisons in a long graph sequence, i.e., from an information visualization perspective it serves as an overview representation supporting to start more detailed analyses processes. To demonstrate theusefulness of the technique we explore real-world dynamic graph data by taking into account visual parameters like node-link layouts, smoothing iterations, number of isolines, and different color codings.

Plenty of songs are composed, written, and releasedevery year being recorded in a variety of databases. Thosedo not only store the audio, but also additional data like theartists' names, the years of release, the lengths of the songs, or number of visits, comments, and remarks of visitors andthe like. However, another important data is the lyrics, i.e., thetextual content which can give insights about the topic, genre, or intention of the musicians. Getting an overview about thetextual content of a song, i.e., the lyrics can become a tediouschallenge since listening to the songs or reading the texts is a timeconsuming task. To support users of song databases we propose a visualization tool that is able to generate word clouds from lyrics. Interaction techniques are incorporated in the tool to give more detailed information about the occurrence of words in a song that finally help to find insights about the genre or to just compare the content very rapidly, for example. Our visualization tool is implemented as a web-based interface, that stores requests, updates the local tool database based on those requests, and finally, provides an interactive visualization for the user.

Visualizing dynamic call graphs is typically done by providing a visual representation that shows the sequence of static graphs placed next to each other. Such a time-to-space mapping is useful to visually compare the graphs and to see the changing graph structure, but, negatively, a view on changes between all pairs of graphs in the sequence is not provided and detecting similarities of longer call graph subsequences is not well supported. In this paper we describe the dynamic call graph matrix that shows similarities and differences between graph pairs in a graph sequence by applying certain well-known set operations as well as fine-grained and coarse-grained graph views supporting visual scalability. We illustrate the usefulness of our technique by applying it to dynamic call graph data from the open source software project JUnit that contains several inherent dynamic call graph features worth investigating and hard to find by a side-by-side dynamic graph visualization alone.

Compound graphs consist of two separate components. On the one hand a graph structure describes which elements are related to each other and to what extent, i.e., inherent edge weights and directions may exist, which we refer to as adjacency edges. On the other hand the graph elements are not only related by adjacencies, but they are also hierarchically organized which might be considered another kind of relationship among the graph vertices. Those relations are further referred to as inclusion edges. There are various application domains in which such a data structure occurs and with which a data analyst has to deal, either analytically on the basis of algorithms or visually, i.e., more on the basis of diagrams and visual languages. In this paper we introduce a visualization tool that is able to provide linked views on both aspects, i.e., the graph relations and the hierarchical organization. We illustrate the usefulness of our tool in a case study investigating soccer team results that build weighted directed adjacency relations in a hierarchically structured world.

We present a new visualization approach for displaying eye tracking data from multiple participants. We aim to show the spatio-temporal data of the gaze points in the context of the underlying image or video stimulus without occlusion. Our technique, denoted as gaze stripes, does not require the explicit definition of areas of interest but directly uses the image data around the gaze points, similar to thumbnails for images. A gaze stripe consists of a sequence of such gaze point images, oriented along a horizontal timeline. By displaying multiple aligned gaze stripes, it is possible to analyze and compare the viewing behavior of the participants over time. Since the analysis is carried out directly on the image data, expensive post-processing or manual annotation are not required. Therefore, not only patterns and outliers in the participants' scanpaths can be detected, but the context of the stimulus is available as well. Furthermore, our approach is especially well suited for dynamic stimuli due to the non-aggregated temporal mapping. Complementary views, i.e., markers, notes, screenshots, histograms, and results from automatic clustering, can be added to the visualization to display analysis results. We illustrate the usefulness of our technique on static and dynamic stimuli. Furthermore, we discuss the limitations and scalability of our approach in comparison to established visualization techniques.

Two types of hyperspectral imager designed for underwater monitor was presented in this article. Active hyperspectral imager with light source compensated the lack of near infared ray in the sea water. The pushbroom hyperspectral imager was built in V-FIN (Vehicle for Instrumentation) and push broom scanning by a boat, which was good for studying some area monitored with full spectral image. The spectral range of both hyperspectral imagers is between 400 and 900nm. The living health of coral reefs and sea grass were the task for ocean ecology observation. These preliminary results was to verify the possibility of pushbroom hyperspectral image grabbing under water and the health monitor of coral reef and sea grass by specta reflectance given from both hyperspectral imager.

The Digital Bibliography and Library Project (DBLP) is a popular computer science bibliography website hosted at the University of Trier in Germany. It currently contains 2,722,212 computer science publications with additional information about the authors and conferences, journals, or books in which these are published. Although the database covers the majority of papers published in this field of research, it is still hard to browse the vast amount of textual data manually to find insights and correlations in it, in particular time-varying ones. This is also problematic if someone is merely interested in all papers of a specific topic and possible correlated scientific words which may hint at related papers. To close this gap, we propose an interactive tool which consists of two separate components, namely data analysis and data visualization. We show the benefits of our tool and explain how it might be used in a scenario where someone is confronted with the task of writing a state-of-the art report on a specific topic. We illustrate how data analysis, data visualization, and the human user supported by interaction features can work together to find insights which makes typical literature search tasks faster.

Software systems typically consist of many lines of source code organized in several files hierarchically structured into directories and packages. Since the code is the key data in software development, in many scenarios an overview of it is required, in particular for similar code passages. In this paper, we investigate the visual analysis of source code similarities for local as well as global code passages. To this end, we first compute all subsequence occurrence frequencies (support metric) and relative occurrence frequencies (confidence metric) in local as well as global code regions. The resulting textual data attached by its occurrence values is displayed in a triangular matrix. Several interaction techniques are integrated in our visualization tool which are illustrated in the corresponding case study illustrating similarities in source code written in Assembler consisting of 10,641 characters.

Module workflows are used to generate custom applications with modular software frameworks. They describe data flow between the modular components and their execution under certain parameter configurations. In many cases, module workflows are modeled in a graphical way by the user. To come up with the final result or to explore multiple solutions, they often undergo many iterations of adaptation. Furthermore, existing workflows may be reused for new applications. We visualize the evolution of module workflows with a focus-and-context approach and visualization techniques for time-dependent data. Our approach provides insight into user behavior and the characteristics of the underlying systems. As our examples show, this can help identify usability issues and indicate options to improve the effectiveness of the system. We demonstrate our approach for module workflows in Vis Trails, a modular visualization system that allows building custom visualizations by combining different modules for processing and visualizing data.

The visual exploration of spatio-temporal eye movement data is challenging, especially if we are interested in the movement patterns of a large number of study participants. For example, if popular visualization techniques like heat maps or gaze plots are used, we may lose the temporal information or get lost in visual clutter. To address these issues, we propose an approach for filtering saccadic eye movement data called hierarchical filter wheels, which employs a radial representation of saccade information. It supports the analysis of sequences of saccades by filtering them with respect to direction and length. The focus of our approach is a fast initial analysis of data from eye tracking studies without the need of defining areas of interest (AOIs) or other preprocessing of the data. The hierarchical filters are interactively generated on users' demand by creating a hierarchy of multiple filter wheels each filtering one element of the sequence. We use a bubble tree layout to represent the generated filter hierarchy. The node positions in our layout directly represent the spatial properties of the filter criteria allowing an intuitive incremental generation and understanding of filter hierarchies. We illustrate the approach by applying it to eye movement data formerly recorded in an eye tracking study investigating the readability of different node-link tree diagrams. We further demonstrate how the hierarchical filter wheels can be used in combination with gaze plots.

Word clouds provide a simple and effective means to visually communicate the most frequent words of text documents. However, only few word cloud visualizations support the contrastive analysis of multiple texts. This paper introduces Concentri Cloud, a layered word cloud layout that merges the words from several text documents into a single visualization. The weighted words are arranged in a concentric layout, with those representing the individual documents on the outer circle and the merged ones on inner circles. Interaction techniques allow to analyze the word cloud composition and to provide details on demand. The approach has been implemented and tested on several examples. A qualitative evaluation indicates the general value of Concentri Cloud and reveals benefits and limitations.

Visualizing dynamic graphs is challenging due to the many data dimensions to be displayed such as graph vertices and edges with their attached weights or attributes and the additional time dimension. Moreover, edge directions with multiplicities and the graph topology are also important inherent features. However, in many dynamic graph visualization techniques each graph in a sequence is treated the same way, i.e., it is visually encoded in the same visual metaphor or even in the same layout. This visualization strategy can be problematic if the graphs are changing topologically over time, i.e., if a sparse graph becomes denser and denser over time or a star pattern is changing into a dense cluster of connected vertices. Such a dynamic graph data scenario demands for a visualization approach which is able to adapt the applied visual metaphor to each graph separately. In this paper we show an idea to solve this problem by using multiple visual metaphors for dynamic graphs which are computed automatically by algorithms analyzing each individual graph based on a given repertoire of graph features. The biggest issue in this technique for the graph dynamics, however, is the preservation of the viewer's mental map at metaphor changes, i.e., to guide him through the graph changes with the goal to explore the data for time-varying patterns. To reach this goal we support the analyst by an interactive highlighting feature.

Graph visualization is essential for the analysis of networks and relational data sets. Often, most of the effort is expended on computing sophisticated layouts of the visual representation of the graph. Even though this is increasingly accelerated by use of graphics processing units (GPUs), the rendering is often considered as circumstantial. In this paper, we present a coherent approach to graph visualization that utilizes all features of modern GPUs. We describe specialized data structures and our GPU-centric pipeline for computing and rendering a layout, while enabling steering and interaction. We evaluate technical aspects of our approach as well as its applicability to huge real-world graphs.

Work processes involving dozens or hundreds of collaborators are complex and difficult to manage. Problems within the process may have severe organizational and financial consequences. Visualization helps monitor and analyze those processes. In this paper, we study the development of large software systems as an example of a complex work process. We introduce Developer Rivers, a timeline-based visualization technique that shows how developers work on software modules. The flow of developers' activity is visualized by a river metaphor: activities are transferred between modules represented as rivers. Interactively switching between hierarchically organized modules and workload metrics allows for exploring multiple facets of the work process. We study typical development patterns by applying our visualization to Python and the Linux kernel.

During eye tracking studies, vast amounts of spatio-temporal data in the form of eye gaze trajectories are recorded. Finding insights into these time-varying data sets is a challenging task. Visualization techniques such as heat maps or gaze plots help find patterns in the data but highly aggregate the data (heat maps) or are difficult to read due to overplotting (gaze plots). In this paper, we propose transforming eye movement data into a dynamic graph data structure to explore the visualization problem from a new perspective. By aggregating gaze trajectories of participants over time periods or Areas of Interest (AOIs), a fair trade-off between aggregation and details is achieved. We show that existing dynamic graph visualizations can be used to display the transformed data and illustrate the approach by applying it to eye tracking data recorded for investigating the readability of tree diagrams.

Visualization by heat maps is a powerful technique for showing frequently visited areas in displayed stimuli. However, by aggregating the spatio-temporal data, heat maps lose the information about the transitions between fixations, i.e., the saccades. In gaze plots, instead, trajectories are shown as overplotted polylines, leading to much visual clutter, which makes those diagrams difficult to read. In this paper, we introduce Saccade Plots as a novel technique that combines the benefits of both approaches: it shows the gaze frequencies as a heat map and the saccades in the form of color-coded triangular matrices that surround the heat map. We illustrate the usefulness of our technique by applying it to a representative example from a previously conducted eye tracking study.

Several algorithms, approaches, and implementations have been developed to support comparison of scan paths and finding of interesting scan path structures. In this work we contribute a visual approach to support scan path comparison. A key feature of this approach is the combination of a clustering algorithm using Levenshtein distance with the parallel scan path visualization technique. The combination of computational methods with an interactive visualization allows us to use both the power of pattern finding algorithms and the human ability to visually recognize patterns. To use the concept in practice we implemented the approach in a prototype and show its application in two scan path analysis scenarios from automobile usability testing and visualization research.

Several algorithms, approaches, and implementations have been developed to support comparison of scan paths and finding of interesting scan path structures. In this work we contribute a visual approach to support scan path comparison. A key feature of this approach is the combination of a clustering algorithm using Levenshtein distance with the parallel scan path visualization technique. The combination of computational methods with an interactive visualization allows us to use both the power of pattern finding algorithms and the human ability to visually recognize patterns. To use the concept in practice we implemented the approach in a prototype and show its application in two scan path analysis scenarios from automobile usability testing and visualization research.

Exploring hierarchical organizations such as software systems is a challenging task. This gets even harder when these are large, deeply nested, and attached by a list of additional attributes of either quantitative, ordinal, or categorical nature. Tree maps have been designed to graphically represent such hierarchical structures in a space-filling way where two attributes for each hierarchy element can be visualized in each tree map box at the same time: by area and by color. Having more than two attributes attached to each hierarchy element can also be visualized by this concept when allowing an analyst to frequently browse the pair wisely represented attributes in the color dimension leaving the box sizes fixed due to mental map preservation. In this paper we enrich standard tree map visualizations by such a browsing feature and additional interaction techniques such as expanding or collapsing them to different levels of hierarchical granularity. To further support the comparison of similar boxes for either one, two, or a list of attributes we add the concept of similarity links whose display thresholds can also interactively be chosen.

Word clouds are a popular means for summarizing text documents. They usually visualize the word frequencies from single text sources, sometimes along with other attributes. However, the visualization of several text documents in one word cloud has rarely been addressed so far. This paper presents Rad Cloud, a technique for text visualization based on multiple word clouds merged into a single view. Inspired by the Rad Viz approach, the words are radially arranged in an overlap-free layout. The text sources are indicated by the spatial word arrangement and stacked bar charts. The approach has been implemented in an interactive text visualization tool and its usefulness is illustrated by an example.

Partially drawn links in graph visualizations have been introduced as a concept for further reducing visual clutter caused by many link crossings in combination to a formerly applied layout algorithm focusing on aesthetic graph drawing criteria. Although this visualization strategy has been shown to be useful for a faster and more accurate visual exploration of node-link diagrams in some scenarios, it has not been integrated into a graph visualization tool as an interaction feature to this end. Apart from representing the entire displayed graph in the partially drawn link style we allow the viewer to apply this feature to several nodes, links, or regions of interest. To avoid possible ambiguities introduced by the disconnectedness of start and target nodes further interaction techniques can be applied to mitigate this situation by showing the complete link information for single selected vertices or edges again.

The visualization of relational data by node-link diagrams quickly leads to a degradation of performance at some exploration tasks when the diagrams show visual clutter and overdraw. To address this challenge of large-data graph visualization, we introduce Graph Metric Views, a technique that enriches the visualization of traditional layout strategies for node-link diagrams by additionally allowing an analyst to interactively explore graph-specific metrics such as number of nodes, number of link crossings, link coverage, or degree of orthogonality. To this end, we support an analyst with additional histogram-like representations at the axes of the display space for graph-specific metrics. In this way, a cluttered and densely packed node-link diagram becomes more explorable even for dense graph regions: The user can use the distribution of metric values as an overview and then select regions of interest for further investigation and filtering.

Dynamic graph visualization techniques can be based on animated or static diagrams showing the evolution over time. In this paper, we apply the concept of small multiples representations to visually illustrate the dynamics of a graph. Node-link diagrams are used as the basic visual metaphor for displaying individual graphs of the sequence. To improve the readability of the diagram and reduce visual clutter we apply an edge splatting technique. Here, we discuss the benefits of splatted radial graph layouts on a modifiable 2D grid. Moreover, to obtain a more scalable dynamic graph visualization we interactively support a graph analyst by a Rapid Serial Visual Presentation (RSVP) feature to rapidly flip between the sequences of displayed graphs. The usefulness of the technique is illustrated in two case studies investigating a dynamic call graph and an evolving social network that consists of more than 1,000 graphs.

We present a visual representation for dynamic, weighted graphs based on the concept of adjacency lists. Two orthogonal axes are used: one for all nodes of the displayed graph, the other for the corresponding links. Colors and labels are employed to identify the nodes. The usage of color allows us to scale the visualization to single pixel level for large graphs. In contrast to other techniques, we employ an asymmetric mapping that results in an aligned and compact representation of links. Our approach is independent of the specific properties of the graph to be visualized, but certain graphs and tasks benefit from the asymmetry. As we show in our results, the strength of our technique is the visualization of dynamic graphs. In particular, sparse graphs benefit from the compact representation. Furthermore, our approach uses visual encoding by size to represent weights and therefore allows easy quantification and comparison. We evaluate our approach in a quantitative user study that confirms the suitability for dynamic and weighted graphs. Finally, we demonstrate our approach for two examples of dynamic graphs.

We present the results of an eye tracking study that compares different visualization methods for long, dense, complex, and piecewise linear spatial trajectories. Typical sources of such data are from temporally discrete measurements of the positions of moving objects, for example, recorded GPS tracks of animals in movement ecology. In the repeated-measures within-subjects user study, four variants of node-link visualization techniques are compared, with the following representations of directed links: standard arrow, tapered, equidistant arrows, and equidistant comets. In addition, we investigate the effect of rendering order for the halo visualization of those links as well as the usefulness of node splatting. All combinations of link visualization techniques are tested for different trajectory density levels. We used three types of tasks: tracing of paths, identification of longest links, and estimation of the density of trajectory clusters. Results are presented in the form of the statistical evaluation of task completion time, task solution accuracy, and two eye tracking metrics. These objective results are complemented by a summary of subjective feedback from the participants. The main result of our study is that tapered links perform very well. However, we discuss that equidistant comets and equidistant arrows are a good option to perceive direction information independent of zoom-level of the display.

We investigate visual task solution strategies when exploring traditional, orthogonal, and radial node-link tree layouts, four orientations of the non-radial layouts, as well as varying difficulty of the task. The strategies are identified by examining eye movement data recorded in a controlled user study previously conducted by Burch et al. For detailed analysis of the spatio-temporal structures and patterns in the eye tracking data, we employ visual analytics techniques adopted from related methodology for geographic movement data by Andrienko et al. In this way, we complement the statistical analysis of task completion times and error rates reported by Burch et al. with spatio-temporal strategies that explain the variation in completion times. We identify differences between task solution strategies dependent on layout type, orientation, and task difficulty. Furthermore, we examine differences between groups of participants split according to completion time. Our analysis identifies that for all layouts it took nearly the same time to find the task solution node, but in the radial layout the solution was not confirmed directly. Instead, a more frequent cross-checking occurs afterwards, which is the main reason for the impaired performance of radial layouts.

Mapping a dynamic graph dataset to an inappropriate visualization leads to a degradation of visualization performance at some task. To tap the full potential of existing dynamic graph visualization techniques, we propose a methodology for matching application requirements with dynamic graph visualization profiles. We target at supporting experts choosing the right visualization technique. Our methodology describes both the application requirements and the visualization techniques as profiles covering important aesthetic criteria for visualizing dynamic graphs. Characteristics of the graph and task are used to derive the application profile. The probably most appropriate visualization technique is the one whose profile matches best the required application profile. We compile exemplary visualization profiles for representatives of dynamic graph visualization approaches and demonstrate the methodology in a case study.

Tag clouds are a popular way to visually represent word frequencies. However, one major limitation is that they do not relate different word forms but treat every form as an individual tag. This results not only in a non-efficient use of screen space but, in particular, leaves the viewer with no indication whether there are other forms of a word or not. To overcome this limitation, we introduce prefix tag clouds: a visualization technique that uses a prefix tree to group different word forms and visualizes the sub trees as tag cloud. The grouping is emphasized by color, while the relative frequencies of the word forms are indicated by font size. A circular tag cloud layout supports the quick identification of the most frequent words and word forms. We show the usefulness of the approach for a large dataset of paper titles from the computer science bibliography DBLP.

We introduce a technique for bundling edges in graphs where a hierarchical organization of the vertices is not available. Instead of applying time-complex force-directed edge bundling, we adopt the concept of Rapidly-Exploring Random Trees (RRTs). We use RRTs for fast computation of a hierarchical space organization that is independent of the spatial structure of the graph layout. Due to this independency, edge bundling can be applied to any graph layout and even allows us to define spatial obstacles through which no bundles may lead. Furthermore, when adding or removing graph nodes and edges on-the-fly, the bundling structure remains stable, which cannot be guaranteed for force-directed bundling. The main benefit of RRT bundling is its high efficiency, supporting interactive exploration. We rely on the low runtime complexity for a new interaction technique for visual clutter reduction in node-link diagrams that we refer to as the RRT edge bundling lens.

We introduce a matrix-based visualization technique for exploring time-varying directed and weighted graphs. Two overview representations are shown: one for the time-aggregated relations with attached quantitative weighted attributes and one for the results of an automatic dynamic pattern identification algorithm, i.e., relations accompanied by categorical attributes. Apart from a dynamic edge pattern categorization, our tool can also compute graph-specific properties---such as shortest paths or the existence of cliques---and highlight their evolution over time. The visualization method is complemented by interaction techniques that allow the user to navigate, explore, and browse the data, based on the Visual Information Seeking Mantra---overview first, zoom and filter, then details-on-demand. If an additional hierarchical organization of the vertices is available, this is attached to the matrix by vertical and horizontal layered icicle plots allowing one to explore the data on different levels of hierarchical granularity. The usefulness of the tool is demonstrated by applying it to time-varying migration data in the hierarchically structured world.

We propose a novel radial layered matrix visualization for dynamic directed weighted graphs in which the vertices can also be hierarchically organized. Edges are represented as color-coded arcs within the radial diagram. Their positions are defined by polar coordinates instead of Cartesian coordinates as in traditional adjacency matrix representations: the angular position of an edge within an annulus is given by the angle bisector of the two related vertices, the radial position depends linearly on the angular distance between these vertices. The exploration of time-varying relational data is facilitated by aligning graph patterns radially. Furthermore, our approach incorporates several interaction techniques to explore dynamic patterns such as trends and countertrends. The usefulness is illustrated by two case studies analyzing large dynamic call graphs acquired from open source software projects.

Abstract It is difficult to explore and analyze eye gaze trajectories for commonly applied visual task solution strategies because such data shows complex spatio‐temporal structure. In particular, the traditional eye gaze plots of scan paths fail for a large number of study participants since these plots lead to much visual clutter. To address this problem we introduce the AOI Rivers technique as a novel interactive visualization method for investigating time‐varying fixation frequencies, transitions between areas of interest (AOIs), and the sequential order of gaze visits to AOIs in a visual stimulus of an eye tracking experiment. To this end, we extend the ThemeRiver technique by influents, effluents, and transitions similar to the concept of Sankey diagrams. The AOI Rivers visualization is complemented by linked spatial views of the data in the form of heatmaps, gaze plots, or display of the visual stimulus. The usefulness of our technique is demonstrated for gaze trajectory data recorded in a previously conducted eye tracking experiment.

Abstract It is difficult to create appropriate bar charts for data that cover large value ranges. The usual approach for these cases employs a logarithmic scale, which, however, suffers from issues inherent to its non‐linear mapping: for example, a quantitative comparison of different values is difficult. We present a new approach for bar charts that combines the advantages of linear and logarithmic scales, while avoiding their drawbacks. Our scale‐stack bar charts use multiple scales to cover a large value range, while the linear mapping within each scale preserves the ability to visually compare quantitative ratios. Scale‐stack bar charts can be used for the same applications as classic bar charts; in particular, they can readily handle stacked bar representations and negative values. Our visualization technique is demonstrated with results for three different application areas and is assessed by an expert review and a quantitative user study confirming advantages of our technique for quantitative comparisons.

The vast amount of contents posted to microblogging services each day offers a rich source of information for analytical tasks. The aggregated posts provide a broad sense of the informal conversations complementing other media. However, analyzing the textual content is challenging due to its large volume, heterogeneity, and time-dependence. In this paper, we exploit the idea of tag clouds to visually analyze microblog content. As a major contribution, tag clouds are extended by an interactive visualization technique that we refer to as time-varying co-occurrence highlighting. It combines colored histograms with visual highlighting of co-occurrences, thus allowing for a time-dependent analysis of term relations. An example dataset of Twitter posts illustrates the applicability and usefulness of the approach.

Rapid Serial Visual Presentation is an effective approach for browsing and searching large amounts of data. By presenting subsequent images at high frequency, we utilize the perceptual abilities of the human visual system to rapidly process certain visual features. While this concept is successfully used in video and image browsing, we demonstrate how it can be applied to dynamic graph visualization. In this paper, we introduce a visualization technique for time-varying graphs that is scalable with respect to the number of time steps. The graph visualization is based on the Parallel Edge Splatting technique, which employs a space-efficient display of a sequence of dynamically changing graphs. To illustrate the usefulness of our approach we analyzed method call graphs recorded during the execution of the open source software system JHotDraw. Furthermore, we studied a time-varying social network representing researchers and their dynamic communication structure while attending the ACM Hypertext 2009 conference.

Eye movement analysis is gaining popularity as a tool for evaluation of visual displays and interfaces. However, the existing methods and tools for analyzing eye movements and scanpaths are limited in terms of the tasks they can support and effectiveness for large data and data with high variation. We have performed an extensive empirical evaluation of a broad range of visual analytics methods used in analysis of geographic movement data. The methods have been tested for the applicability to eye tracking data and the capability to extract useful knowledge about users' viewing behaviors. This allowed us to select the suitable methods and match them to possible analysis tasks they can support. The paper describes how the methods work in application to eye tracking data and provides guidelines for method selection depending on the analysis tasks.

We introduce a novel technique for visualizing dense time-varying directed and weighted multi-graphs with an additional hierarchical organization of the graph nodes. Combining Indented Tree Plots and TimeRadarTrees, we show the temporal evolution of relations in a static view. The graph edges are layered around thumbnail wheels consisting of color-coded sectors that are representatives of the graph nodes. These sectors generate implicit representations of graph edges. Start and target vertices are perceived by inspecting the color coding of sectors in the context of other sectors and their orientations. The technique puts emphasis on newer relations and hence, these are mapped to a larger display space in the radial diagram. The benefit of our technique is reduction of visual clutter from which node-link diagrams typically suffer. The visualization focuses on an easy exploration of trends, countertrends, periodicity, temporal shifts, and anomalies in time-varying relational data. We demonstrate the usefulness of the approach by applying it to dense dynamic graph data acquired from a soccer match of the 2D Soccer Simulation League.

Node-link diagrams are an effective and popular visualization approach for depicting hierarchical structures and for showing parent-child relationships. In this paper, we present the results of an eye tracking experiment investigating traditional, orthogonal, and radial node-link tree layouts as a piece of empirical basis for choosing between those layouts. Eye tracking was used to identify visual exploration behaviors of participants that were asked to solve a typical hierarchy exploration task by inspecting a static tree diagram: finding the least common ancestor of a given set of marked leaf nodes. To uncover exploration strategies, we examined fixation points, duration, and saccades of participants' gaze trajectories. For the non-radial diagrams, we additionally investigated the effect of diagram orientation by switching the position of the root node to each of the four main orientations. We also recorded and analyzed correctness of answers as well as completion times in addition to the eye movement data. We found out that traditional and orthogonal tree layouts significantly outperform radial tree layouts for the given task. Furthermore, by applying trajectory analysis techniques we uncovered that participants cross-checked their task solution more often in the radial than in the non-radial layouts.

We present a novel dynamic graph visualization technique based on node-link diagrams. The graphs are drawn side-byside from left to right as a sequence of narrow stripes that are placed perpendicular to the horizontal time line. The hierarchically organized vertices of the graphs are arranged on vertical, parallel lines that bound the stripes; directed edges connect these vertices from left to right. To address massive overplotting of edges in huge graphs, we employ a splatting approach that transforms the edges to a pixel-based scalar field. This field represents the edge densities in a scalable way and is depicted by non-linear color mapping. The visualization method is complemented by interaction techniques that support data exploration by aggregation, filtering, brushing, and selective data zooming. Furthermore, we formalize graph patterns so that they can be interactively highlighted on demand. A case study on software releases explores the evolution of call graphs extracted from the JUnit open source software project. In a second application, we demonstrate the scalability of our approach by applying it to a bibliography dataset containing more than 1.5 million paper titles from 60 years of research history producing a vast amount of relations between title words.

Graphs are a mathematical method to model relations between objects. The most common metaphor to visualize graphs is the node-link technique, which typically suffers from visual clutter caused by many edge crossings. Much research has been done on the development of sophisticated algorithms aimed at enhancing the layout with respect to edge crossings and a series of other aesthetic criteria. In this paper we propose a novel visual metaphor, called Time-SpiderTrees, which is based on a radial layout. In our technique, relations are visually indicated by orientation instead of connectedness to circumvent the problem of edge crossings. The strength of this novel visualization technique lies in the visual encoding of time-series relational data in a single view without animation, which helps to preserve the mental map and hence to reduce cognitive efforts.

Radial visualizations play an important role in the information visualization community. But the decision to choose a radial coordinate system is rather based on intuition than on scientific foundations. The empirical approach presented in this paper aims at uncovering strengths and weaknesses of radial visualizations by comparing them to equivalent ones in Cartesian coordinate systems. We identified memorizing positions of visual elements as a generic task when working with visualizations. A first study with 674 participants provides a broad data spectrum for exploring differences between the two visualization types. A second, complementing study with fewer participants focuses on further questions raised by the first study. Our findings document that Cartesian visualizations tend to outperform their radial counterparts especially with respect to answer times. Nonetheless, radial visualization seem to be more appropriate for focusing on a particular data dimension.

Most research on the readability of graph visualization focuses on node-link diagrams of static graphs. But in many applications graphs are not static, but change over time, or graphs are too dense to be drawn as node-link diagrams. In this paper we look at dynamic graph visualizations: We translate the general goal of graph visualization-to convey the underlying information of a graph-into aesthetic dimensions that are applicable in practice. These aesthetic dimensions help to design, compare, and evaluate dynamic graph visualizations.

Abstract Compound digraphs are a widely used model in computer science. In many application domains these models evolve over time. Only few approaches to visualize such dynamic compound digraphs exist and mostly use animation to show the dynamics. In this paper we present a new visualization tool called TimeArcTrees that visualizes weighted, dynamic compound digraphs by drawing a sequence of node‐link diagrams in a single view. Compactness is achieved by aligning the nodes of a graph vertically. Edge crossings are reduced by drawing upward and downward edges separately as colored arcs. Horizontal alignment of the instances of the same node in different graphs facilitates comparison of the graphs in the sequence. Many interaction techniques allow to explore the given graphs. Smooth animation supports the user to better track the transitions between views and to preserve his or her mental map. We illustrate the usefulness of the tool by looking at the particular problem of how shortest paths evolve over time. To this end, we applied the system to an evolving graph representing the German Autobahn and its traffic jams.

In many applications transactions between the elements of an information hierarchy occur over time. For example, the product offers of a department store can be organized into product groups and subgroups to form an information hierarchy. A market basket consisting of the products bought by a customer forms a transaction. Market baskets of one or more customers can be ordered by time into a sequence of transactions. Each item in a transaction is associated with a measure, for example, the amount paid for a product.

Abstract The evolution of dependencies in information hierarchies can be modeled by sequences of compound digraphs with edge weights. In this paper we present a novel approach to visualize such sequences of graphs. It uses radial tree layout to draw the hierarchy, and circle sectors to represent the temporal change of edges in the digraphs. We have developed several interaction techniques that allow the users to explore the structural and temporal data. Smooth animations help them to track the transitions between views. The usefulness of the approach is illustrated by examples from very different application domains.