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Item Rendering and Extracting Extremal Features in 3D Fields(The Eurographics Association and John Wiley & Sons Ltd., 2018) Kindlmann, Gordon L.; Chiw, Charisee; Huynh, Tri; Gyulassy, Attila; Reppy, John; Bremer, Peer-Timo; Jeffrey Heer and Heike Leitte and Timo RopinskiVisualizing and extracting three-dimensional features is important for many computational science applications, each with their own feature definitions and data types. While some are simple to state and implement (e.g. isosurfaces), others require more complicated mathematics (e.g. multiple derivatives, curvature, eigenvectors, etc.). Correctly implementing mathematical definitions is difficult, so experimenting with new features requires substantial investments. Furthermore, traditional interpolants rarely support the necessary derivatives, and approximations can reduce numerical stability. Our new approach directly translates mathematical notation into practical visualization and feature extraction, with minimal mental and implementation overhead. Using a mathematically expressive domain-specific language, Diderot, we compute direct volume renderings and particlebased feature samplings for a range of mathematical features. Non-expert users can experiment with feature definitions without any exposure to meshes, interpolants, derivative computation, etc. We demonstrate high-quality results on notoriously difficult features, such as ridges and vortex cores, using working code simple enough to be presented in its entirety.Item Visual-Interactive Preprocessing of Multivariate Time Series Data(The Eurographics Association and John Wiley & Sons Ltd., 2019) Bernard, Jürgen; Hutter, Marco; Reinemuth, Heiko; Pfeifer, Hendrik; Bors, Christian; Kohlhammer, Jörn; Gleicher, Michael and Viola, Ivan and Leitte, HeikePre-processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre-processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre-processing pipelines, human-in-the-loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in-depth research in visual analytics. We present a visual-interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre-processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty-aware pre-processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre-processing in general and for uncertainty-aware pre-processing of multivariate time series in particular.Item Visual Analysis of Charge Flow Networks for Complex Morphologies(The Eurographics Association and John Wiley & Sons Ltd., 2019) Kottravel, Sathish; Falk, Martin; Bin Masood, Talha; linares, mathieu; Hotz, Ingrid; Gleicher, Michael and Viola, Ivan and Leitte, HeikeIn the field of organic electronics, understanding complex material morphologies and their role in efficient charge transport in solar cells is extremely important. Related processes are studied using the Ising model and Kinetic Monte Carlo simulations resulting in large ensembles of stochastic trajectories. Naive visualization of these trajectories, individually or as a whole, does not lead to new knowledge discovery through exploration. In this paper, we present novel visualization and exploration methods to analyze this complex dynamic data, which provide succinct and meaningful abstractions leading to scientific insights. We propose a morphology abstraction yielding a network composed of material pockets and the interfaces, which serves as backbone for the visualization of the charge diffusion. The trajectory network is created using a novel way of implicitly attracting the trajectories to the skeleton of the morphology relying on a relaxation process. Each individual trajectory is then represented as a connected sequence of nodes in the skeleton. The final network summarizes all of these sequences in a single aggregated network. We apply our method to three different morphologies and demonstrate its suitability for exploring this kind of data.Item Visualizing Multidimensional Data with Order Statistics(The Eurographics Association and John Wiley & Sons Ltd., 2018) Raj, Mukund; Whitaker, Ross T.; Jeffrey Heer and Heike Leitte and Timo RopinskiMultidimensional data sets are common in many domains, and dimensionality reduction methods that determine a lower dimensional embedding are widely used for visualizing such data sets. This paper presents a novel method to project data onto a lower dimensional space by taking into account the order statistics of the individual data points, which are quantified by their depth or centrality in the overall set. Thus, in addition to conveying relative distances in the data, the proposed method also preserves the order statistics, which are often lost or misrepresented by existing visualization methods. The proposed method entails a modification of the optimization objective of conventional multidimensional scaling (MDS) by introducing a term that penalizes discrepancies between centrality structures in the original space and the embedding. We also introduce two strategies for visualizing lower dimensional embeddings of multidimensional data that takes advantage of the coherent representation of centrality provided by the proposed projection method. We demonstrate the effectiveness of our visualization with comparisons on different kinds of multidimensional data, including categorical and multimodal, from a variety of domains such as botany and health care.Item Robust Extraction and Simplification of 2D Symmetric Tensor Field Topology(The Eurographics Association and John Wiley & Sons Ltd., 2019) Jankowai, Jochen; Wang, Bei; Hotz, Ingrid; Gleicher, Michael and Viola, Ivan and Leitte, HeikeIn this work, we propose a controlled simplification strategy for degenerated points in symmetric 2D tensor fields that is based on the topological notion of robustness. Robustness measures the structural stability of the degenerate points with respect to variation in the underlying field. We consider an entire pipeline for generating a hierarchical set of degenerate points based on their robustness values. Such a pipeline includes the following steps: the stable extraction and classification of degenerate points using an edge labeling algorithm, the computation and assignment of robustness values to the degenerate points, and the construction of a simplification hierarchy. We also discuss the challenges that arise from the discretization and interpolation of real world data.Item Feature-Driven Visual Analytics of Chaotic Parameter-Dependent Movement(The Eurographics Association and John Wiley & Sons Ltd., 2015) Luboschik, Martin; Röhlig, Martin; Bittig, Arne T.; Andrienko, Natalia; Schumann, Heidrun; Tominski, Christian; H. Carr, K.-L. Ma, and G. SantucciAnalyzing movements in their spatial and temporal context is a complex task. We are additionally interested in understanding the movements' dependency on parameters that govern the processes behind the movement. We propose a visual analytics approach combining analytic, visual, and interactive means to deal with the added complexity. The key idea is to perform an analytical extraction of features that capture distinct movement characteristics. Different parameter configurations and extracted features are then visualized in a compact fashion to facilitate an overview of the data. Interaction enables the user to access details about features, to compare features, and to relate features back to the original movement. We instantiate our approach with a repository of more than twenty accepted and novel features to help analysts in gaining insight into simulations of chaotic behavior of thousands of entities over thousands of data points. Domain experts applied our solution successfully to study dynamic groups in such movements in relation to thousands of parameter configurations.Item Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization(The Eurographics Association and John Wiley & Sons Ltd., 2019) Byška, Jan; Trautner, Thomas; Marques, Sérgio M.; Damborský, Jiří; Kozlíková, Barbora; Waldner, Manuela; Gleicher, Michael and Viola, Ivan and Leitte, HeikeAnalyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio-temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.Item Visualizing Expanded Query Results(The Eurographics Association and John Wiley & Sons Ltd., 2018) Mazurek, Michael; Waldner, Manuela; Jeffrey Heer and Heike Leitte and Timo RopinskiWhen performing queries in web search engines, users often face difficulties choosing appropriate query terms. Search engines therefore usually suggest a list of expanded versions of the user query to disambiguate it or to resolve potential term mismatches. However, it has been shown that users find it difficult to choose an expanded query from such a list. In this paper, we describe the adoption of set-based text visualization techniques to visualize how query expansions enrich the result space of a given user query and how the result sets relate to each other. Our system uses a linguistic approach to expand queries and topic modeling to extract the most informative terms from the results of these queries. In a user study, we compare a common text list of query expansion suggestions to three set-based text visualization techniques adopted for visualizing expanded query results - namely, Compact Euler Diagrams, Parallel Tag Clouds, and a List View - to resolve ambiguous queries using interactive query expansion. Our results show that text visualization techniques do not increase retrieval efficiency, precision, or recall. Overall, users rate Parallel Tag Clouds visualizing key terms of the expanded query space lowest. Based on the results, we derive recommendations for visualizations of query expansion results, text visualization techniques in general, and discuss alternative use cases of set-based text visualization techniques in the context of web search.Item Hierarchical Correlation Clustering in Multiple 2D Scalar Fields(The Eurographics Association and John Wiley & Sons Ltd., 2018) Liebmann, Tom; Weber, Gunther H.; Scheuermann, Gerik; Jeffrey Heer and Heike Leitte and Timo RopinskiSets of multiple scalar fields can be used to model many types of variation in data, such as uncertainty in measurements and simulations or time-dependent behavior of scalar quantities. Many structural properties of such fields can be explained by dependencies between different points in the scalar field. Although these dependencies can be of arbitrary complexity, correlation, i.e., the linear dependency, already provides significant structural information. Existing methods for correlation analysis are usually limited to positive correlation, handle only local dependencies, or use combinatorial approximations to this continuous problem. We present a new approach for computing and visualizing correlated regions in sets of 2-dimensional scalar fields. This paper describes the following three main contributions: (i) An algorithm for hierarchical correlation clustering resulting in a dendrogram, (ii) a generalization of topological landscapes for dendrogram visualization, and (iii) a new method for incorporating negative correlation values in the clustering and visualization. All steps are designed to preserve the special properties of correlation coefficients. The results are visualized in two linked views, one showing the cluster hierarchy as 2D landscape and the other providing a spatial context in the scalar field's domain. Different coloring and texturing schemes coupled with interactive selection support an exploratory data analysis.Item Topic Tomographies (TopTom): a Visual Approach to Distill Information From Media Streams(The Eurographics Association and John Wiley & Sons Ltd., 2019) Gobbo, Beatrice; Balsamo, Duilio; Mauri, Michele; Bajardi, Paolo; Panisson, André; CIUCCARELLI, PAOLO; Gleicher, Michael and Viola, Ivan and Leitte, HeikeIn this paper we present TopTom, a digital platform whose goal is to provide analytical and visual solutions for the exploration of a dynamic corpus of user-generated messages and media articles, with the aim of i) distilling the information from thousands of documents in a low-dimensional space of explainable topics, ii) cluster them in a hierarchical fashion while allowing to drill down to details and stories as constituents of the topics, iii) spotting trends and anomalies. TopTom implements a batch processing pipeline able to run both in near-real time with time stamped data from streaming sources and on historical data with a temporal dimension in a cold start mode. The resulting output unfolds along three main axes: time, volume and semantic similarity (i.e. topic hierarchical aggregation). To allow the browsing of data in a multiscale fashion and the identification of anomalous behaviors, three visual metaphors were adopted from biological and medical fields to design visualizations, i.e. the flowing of particles in a coherent stream, tomographic cross sectioning and contrast-like analysis of biological tissues. The platform interface is composed by three main visualizations with coherent and smooth navigation interactions: calendar view, flow view, and temporal cut view. The integration of these three visual models with the multiscale analytic pipeline proposes a novel system for the identification and exploration of topics from unstructured texts. We evaluated the system using a collection of documents about the emerging opioid epidemics in the United States.