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Now showing 1 - 10 of 47
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    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, Heike
    Pre-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.
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    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, Heike
    In 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.
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    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, Heike
    In 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.
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    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, Heike
    Analyzing 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.
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    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, Heike
    In 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.
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    A User-based Visual Analytics Workflow for Exploratory Model Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Cashman, Dylan; Humayoun, Shah Rukh; Heimerl, Florian; Park, Kendall; Das, Subhajit; Thompson, John; Saket, Bahador; Mosca, Abigail; Stasko, John; Endert, Alex; Gleicher, Michael; Chang, Remco; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well-known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.
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    Interactive Curation of Datasets for Training and Refining Generative Models
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Ye, Wenjie; Dong, Yue; Peers, Pieter; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    We present a novel interactive learning-based method for curating datasets using user-defined criteria for training and refining Generative Adversarial Networks. We employ a novel batch-mode active learning strategy to progressively select small batches of candidate exemplars for which the user is asked to indicate whether they match the, possibly subjective, selection criteria. After each batch, a classifier that models the user's intent is refined and subsequently used to select the next batch of candidates. After the selection process ends, the final classifier, trained with limited but adaptively selected training data, is used to sift through the large collection of input exemplars to extract a sufficiently large subset for training or refining the generative model that matches the user's selection criteria. A key distinguishing feature of our system is that we do not assume that the user can always make a firm binary decision (i.e., ''meets'' or ''does not meet'' the selection criteria) for each candidate exemplar, and we allow the user to label an exemplar as ''undecided''. We rely on a non-binary query-by-committee strategy to distinguish between the user's uncertainty and the trained classifier's uncertainty, and develop a novel disagreement distance metric to encourage a diverse candidate set. In addition, a number of optimization strategies are employed to achieve an interactive experience. We demonstrate our interactive curation system on several applications related to training or refining generative models: training a Generative Adversarial Network that meets a user-defined criteria, adjusting the output distribution of an existing generative model, and removing unwanted samples from a generative model.
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    SurgeryCuts: Embedding Additional Information in Maps without Occluding Features
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Angelini, Marco; Buchmüller, Juri; Keim, Daniel A.; Meschenmoser, Philipp; Santucci, Giuseppe; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Visualizing contextual information to a map often comes at the expense of overplotting issues. Especially for use cases with relevant map features in the immediate vicinity of an information to add, occlusion of the relevant map context should be avoided. We present SurgeryCuts, a map manipulation technique for the creation of additional canvas area for contextual visualizations on maps. SurgeryCuts is occlusion-free and does not shift, zoom or alter the map viewport. Instead, relevant parts of the map can be cut apart. The affected area is controlledly distorted using a parameterizable warping function fading out the map distortion depending on the distance to the cut. We define extended metrics for our approach and compare to related approaches. As well, we demonstrate the applicability of our approach at the example of tangible use cases and a comparative user study.
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    VIAN: A Visual Annotation Tool for Film Analysis
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Halter, Gaudenz; Ballester-Ripoll, Rafael; Flueckiger, Barbara; Pajarola, Renato; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    While color plays a fundamental role in film design and production, existing solutions for film analysis in the digital humanities address perceptual and spatial color information only tangentially. We introduce VIAN, a visual film annotation system centered on the semantic aspects of film color analysis. The tool enables expert-assessed labeling, curation, visualization and classification of color features based on their perceived context and aesthetic quality. It is the first of its kind that incorporates foreground-background information made possible by modern deep learning segmentation methods. The proposed tool seamlessly integrates a multimedia data management system, so that films can undergo a full color-oriented analysis pipeline.
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    Linking and Layout: Exploring the Integration of Text and Visualization in Storytelling
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Zhi, Qiyu; Ottley, Alvitta; Metoyer, Ronald; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Modern web technologies are enabling authors to create various forms of text visualization integration for storytelling. This integration may shape the stories' flow and thereby affect the reading experience. In this paper, we seek to understand two text visualization integration forms: (i) different text and visualization spatial arrangements (layout), namely, vertical and slideshow; and (ii) interactive linking of text and visualization (linking). Here, linking refers to a bidirectional interaction mode that explicitly highlights the explanatory visualization element when selecting narrative text and vice versa. Through a crowdsourced study with 180 participants, we measured the effect of layout and linking on the degree to which users engage with the story (user engagement), their understanding of the story content (comprehension), and their ability to recall the story information (recall). We found that participants performed significantly better in comprehension tasks with the slideshow layout. Participant recall was better with the slideshow layout under conditions with linking versus no linking. We also found that linking significantly increased user engagement. Additionally, linking and the slideshow layout were preferred by the participants. We also explored user reading behaviors with different conditions.