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This report documents the program and outcomes of the Dagstuhl Seminar 19262 "Astrographics: Interactive Data-Driven Journeys through Space". The seminar consisted of introductory talks, which are presented first in this documents, followed by discussions in break-out groups whose results were reported back to all participants after each break-out session.

This report documents the program and outcomes of the "'. The miniaturization of optical devices and advances in computer vision, as well as a lower cost point, have led to an increased integration of gaze sensing capabilities in computing systems. Eye tracking is no longer restricted to a well controlled laboratory setting, but moving into everyday settings. Therefore, this Dagstuhl Seminar brought together experts in computer graphics, signal processing, visualization, human-computer interaction, data analytics, pattern analysis and classification along with researchers who employ eye tracking across a diverse set of disciplines: geo-information systems, medicine, aviation, psychology, and neuroscience, to explore future applications and to identify requirements for reliable gaze sensing technology. This fostered a dialog and allowed: (1) computing scientists to understand the problems that are faced in recording and interpreting gaze data; (2) gaze researchers to consider how modern computing techniques could potentially advance their research. Other issues concerning the ubiquitous deployment of gaze sensing and interaction were also discussed, such ethical and privacy concerns when deploying gaze monitoring devices in everyday settings.

We present an algorithmic and visual grouping of participants and eye-tracking metrics derived from recorded eye-tracking data. Our method utilizes two well-established visualization concepts. First, parallel coordinates are used to provide an overview of the used metrics, their interactions, and similarities, which helps select suitable metrics that describe characteristics of the eye-tracking data. Furthermore, parallel coordinates plots enable an analyst to test the effects of creating a combination of a subset of metrics resulting in a newly derived eye-tracking metric. Second, a similarity matrix visualization is used to visually represent the affine combination of metrics utilizing an algorithmic grouping of subjects that leads to distinct visual groups of similar behavior. To keep the diagrams of the matrix visualization simple and understandable, we visually encode our eye-tracking data into the cells of a similarity matrix of participants. The algorithmic grouping is performed with a clustering based on the affine combination of metrics, which is also the basis for the similarity value computation of the similarity matrix. To illustrate the usefulness of our visualization, we applied it to an eye-tracking data set involving the reading behavior of metro maps of up to 40 participants. Finally, we discuss limitations and scalability issues of the approach focusing on visual and perceptual issues., Comment: 17 pages, 15 figures

Dynamic Mode Decomposition (DMD) is a data-driven decomposition technique extracting spatio-temporal patterns of time-dependent phenomena. In this paper, we perform a comprehensive theoretical analysis of various variants of DMD. We provide a systematic advancement of these and examine the interrelations. In addition, several results of each variant are proven. Our main result is the exact reconstruction property. To this end, a new modification of scaling factors is presented and a new concept of an error scaling is introduced to guarantee an error-free reconstruction of the data.

Although many tools have been presented in the research literature of software visualization, there is little evidence of their adoption. To choose a suitable visualization tool, practitioners need to analyze various characteristics of tools such as their supported software concerns and level of maturity. Indeed, some tools can be prototypes for which the lifespan is expected to be short, whereas others can be fairly mature products that are maintained for a longer time. Although such characteristics are often described in papers, we conjecture that practitioners willing to adopt software visualizations require additional support to discover suitable visualization tools. In this paper, we elaborate on our efforts to provide such support. To this end, we systematically analyzed research papers in the literature of software visualization and curated a catalog of 70 available tools that employ various visualization techniques to support the analysis of multiple software concerns. We further encapsulate these characteristics in an ontology. VISON, our software visualization ontology, captures these semantics as concepts and relationships. We report on early results of usage scenarios that demonstrate how the ontology can support (i) developers to find suitable tools for particular development concerns, and (ii) researchers who propose new software visualization tools to identify a baseline tool for a controlled experiment., Comment: 11 pages, 12 figures, 2 tables. VISSOFT 2019

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., Comment: 4 pages

Generalized Pythagoras trees were developed for visualizing hierarchical data, producing organic, fractal-like representations. However, the drawback of the original layout algorithm is visual overlap of tree branches. To avoid such overlap, we introduce an adapted drawing algorithm using ellipses instead of circles to recursively place tree nodes representing the subhierarchies. Our technique is demonstrated by resolving overlap in diverse real-world and generated datasets, while comparing the results to the original approach.

Good code quality is a prerequisite for efficiently developing maintainable software. In this paper, we present a novel approach to generate exploranative (explanatory and exploratory) data-driven documents that report code quality in an interactive, exploratory environment. We employ a template-based natural language generation method to create textual explanations about the code quality, dependent on data from software metrics. The interactive document is enriched by different kinds of visualization, including parallel coordinates plots and scatterplots for data exploration and graphics embedded into text. We devise an interaction model that allows users to explore code quality with consistent linking between text and visualizations; through integrated explanatory text, users are taught background knowledge about code quality aspects. Our approach to interactive documents was developed in a design study process that included software engineering and visual analytics experts. Although the solution is specific to the software engineering scenario, we discuss how the concept could generalize to multivariate data and report lessons learned in a broader scope., Comment: IEEE VIS VAST 2019

In this paper, we propose a perceptually-guided visualization sharpening technique. We analyze the spectral behavior of an established comprehensive perceptual model to arrive at our approximated model based on an adapted weighting of the bandpass images from a Gaussian pyramid. The main benefit of this approximated model is its controllability and predictability for sharpening color-mapped visualizations. Our method can be integrated into any visualization tool as it adopts generic image-based post-processing, and it is intuitive and easy to use as viewing distance is the only parameter. Using highly diverse datasets, we show the usefulness of our method across a wide range of typical visualizations., Comment: Symposium of Applied Perception'19

This article outlines a program of ethnoontology that brings together empirical research in the ethnosciences with ontological debates in philosophy. First, we survey empirical evidence from heterogeneous cultural contexts and disciplines. Second, we propose a model of cross-cultural relations between ontologies beyond a simple divide between universalist and relativist models. Third, we argue for an integrative model of ontology building that synthesizes insights from different fields such as biological taxonomy, cognitive science, cultural anthropology, and political ecology. We conclude by arguing that a program of ethnoontology provides philosophers both with insights about traditional issues such as debates about natural kinds and with novel strategies for connecting philosophy with pressing global issues such as the conservation of local environments and the self-determination of Indigenous communities.

We present a technique to perform dimensionality reduction on data that is subject to uncertainty. Our method is a generalization of traditional principal component analysis (PCA) to multivariate probability distributions. In comparison to non-linear methods, linear dimensionality reduction techniques have the advantage that the characteristics of such probability distributions remain intact after projection. We derive a representation of the PCA sample covariance matrix that respects potential uncertainty in each of the inputs, building the mathematical foundation of our new method: uncertainty-aware PCA. In addition to the accuracy and performance gained by our approach over sampling-based strategies, our formulation allows us to perform sensitivity analysis with regard to the uncertainty in the data. For this, we propose factor traces as a novel visualization that enables to better understand the influence of uncertainty on the chosen principal components. We provide multiple examples of our technique using real-world datasets. As a special case, we show how to propagate multivariate normal distributions through PCA in closed form. Furthermore, we discuss extensions and limitations of our approach.

Developers are usually unaware of the impact of code changes to the performance of software systems. Although developers can analyze the performance of a system by executing, for instance, a performance test to compare the performance of two consecutive versions of the system, changing from a programming task to a testing task would disrupt the development flow. In this paper, we propose the use of a city visualization that dynamically provides developers with a pervasive view of the continuous performance of a system. We use an immersive augmented reality device (Microsoft HoloLens) to display our visualization and extend the integrated development environment on a computer screen to use the physical space. We report on technical details of the design and implementation of our visualization tool, and discuss early feedback that we collected of its usability. Our investigation explores a new visual metaphor to support the exploration and analysis of possibly very large and multidimensional performance data. Our initial result indicates that the city metaphor can be adequate to analyze dynamic performance data on a large and non-trivial software system., Comment: ICPE'19 vision, 4 pages, 2 figure, conference