30-Issue 3
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Item Visual Boosting in Pixel-based Visualizations(The Eurographics Association and Blackwell Publishing Ltd., 2011) Oelke, Daniela; Janetzko, Halldor; Simon, Svenja; Neuhaus, Klaus; Keim, Daniel A.; H. Hauser, H. Pfister, and J. J. van WijkPixel-based visualizations have become popular, because they are capable of displaying large amounts of data and at the same time provide many details. However, pixel-based visualizations are only effective if the data set is not sparse and the data distribution not random. Single pixels - no matter if they are in an empty area or in the middle of a large area of differently colored pixels - are perceptually difficult to discern and may therefore easily be missed. Furthermore, trends and interesting passages may be camouflaged in the sea of details. In this paper we compare different approaches for visual boosting in pixel-based visualizations. Several boosting techniques such as halos, background coloring, distortion, and hatching are discussed and assessed with respect to their effectiveness in boosting single pixels, trends, and interesting passages. Application examples from three different domains (document analysis, genome analysis, and geospatial analysis) show the general applicability of the techniques and the derived guidelines.Item Prostate Cancer Visualization from MR Imagery and MR Spectroscopy(The Eurographics Association and Blackwell Publishing Ltd., 2011) Marino, Joseph; Kaufman, Arie; H. Hauser, H. Pfister, and J. J. van WijkProstate cancer is one of the most prevalent cancers among males, and the use of magnetic resonance imaging (MRI) has been suggested for its detection. A framework is presented for scoring and visualizing various MR data in an efficient and intuitive manner. A classification method is introduced where a cumulative score volume is created which takes into account each of three acquisition types. This score volume is integrated into a volume rendering framework which allows the user to view the prostate gland, the multi-modal score values, and the sur- rounding anatomy. A visibility persistence mode is introduced to automatically avoid full occlusion of a selected score and indicate overlaps. The use of GPU-accelerated multi-modal single-pass ray casting provides an inter- active experience. User driven importance rendering allows the user to gain insight into the data and can assist in localization of the disease and treatment planning. We evaluate our results against pathology and radiologists' determinations.Item A Framework for Exploring Multidimensional Data with 3D Projections(The Eurographics Association and Blackwell Publishing Ltd., 2011) Poco, Jorge; Etemadpour, Ronak; Paulovich, F. V.; Long, T. V.; Rosenthal, P.; Oliveira, M. C. F.; Linsen, Lars; Minghim, R.; H. Hauser, H. Pfister, and J. J. van WijkVisualization of high-dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e.g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high-dimensional data to 3D visual spaces, based on a generalization of the Least- Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the framework's applicability for both visual exploration of multidimensional abstract (non-spatial) data as well as the feature space of multi-variate spatial data.Item Comparison of Multiple Weighted Hierarchies: Visual Analytics for Microbe Community Profiling(The Eurographics Association and Blackwell Publishing Ltd., 2011) Dinkla, Kasper; Westenberg, M. A.; Timmerman, H. M.; Hijum, S.A.F.T. van; Wijk, J. J. van; H. Hauser, H. Pfister, and J. J. van WijkWe propose visual analytics techniques to support concurrent comparison of hundreds of cumulatively weighted instances of a single hierarchy. This includes a node-link representation of the hierarchy where nodes depict the weights of all instances with high-density heat maps that are grouped and aligned to ease cross-referencing. Hierarchy exploration is facilitated by smoothly animated expansion and collapse of its branches. Detailed infor- mation about hierarchy structure, weights, and meta-data is provided by secondary linked visualizations. These techniques have been implemented in a prototype tool, in which the computational analysis concerns have been strictly separated from the visualization concerns. The analysis algorithms are extensible via a script engine. We discuss the effectiveness of our techniques for the visual analytic process of microbe community profiling experts.Item Stable Morse Decompositions for Piecewise Constant Vector Fields on Surfaces(The Eurographics Association and Blackwell Publishing Ltd., 2011) Szymczak, Andrzej; H. Hauser, H. Pfister, and J. J. van WijkNumerical simulations and experimental observations are inherently imprecise. Therefore, most vector fields of interest in scientific visualization are known only up to an error. In such cases, some topological features, especially those not stable enough, may be artifacts of the imprecision of the input. This paper introduces a technique to compute topological features of user-prescribed stability with respect to perturbation of the input vector field. In order to make our approach simple and efficient, we develop our algorithms for the case of piecewise constant (PC) vector fields. Our approach is based on a super-transition graph, a common graph representation of all PC vector fields whose vector value in a mesh triangle is contained in a convex set of vectors associated with that triangle. The graph is used to compute a Morse decomposition that is coarse enough to be correct for all vector fields satisfying the constraint. Apart from computing stable Morse decompositions, our technique can also be used to estimate the stability of Morse sets with respect to perturbation of the vector field or to compute topological features of continuous vector fields using the PC framework.Item Depth of Field Effects for Interactive Direct Volume Rendering(The Eurographics Association and Blackwell Publishing Ltd., 2011) Schott, Mathias; Grosset, A. V. Pascal; Martin, Tobias; Pegoraro, Vincent; Smith, Sean T.; Hansen, Charles D.; H. Hauser, H. Pfister, and J. J. van WijkIn this paper, a method for interactive direct volume rendering is proposed for computing depth of field effects, which previously were shown to aid observers in depth and size perception of synthetically generated images. The presented technique extends those benefits to volume rendering visualizations of 3D scalar fields from CT/MRI scanners or numerical simulations. It is based on incremental filtering and as such does not depend on any precomputation, thus allowing interactive explorations of volumetric data sets via on-the-fly editing of the shading model parameters or (multi-dimensional) transfer functions.Item Visualising Errors in Animal Pedigree Genotype Data(The Eurographics Association and Blackwell Publishing Ltd., 2011) Graham, Martin; Kennedy, Jessie; Paterson, Trevor; Law, Andy; H. Hauser, H. Pfister, and J. J. van WijkGenetic analysis of a breeding animal population involves determining the inheritance pattern of genotypes for multiple genetic markers across the individuals in the population pedigree structure. However, experimental pedigree genotype data invariably contains errors in both the pedigree structure and in the associated individual genotypes, introducing inconsistencies into the dataset, rendering them useless for further analysis. The resolution of these errors requires consideration of genotype inheritance patterns in the context of the pedigree structure. Existing pedigree visualisations are typically more suited to human pedigrees and are less suitable for large complex animal pedigrees which may exhibit cross generational inbreeding. Similarly, table-based viewers of genotype marker data can highlight where errors become apparent but lack the functionality and interactive visual feedback to allow users to locate the origin of errors within the pedigree. In this paper, we detail a design study steered by biologists who work with pedigree data, and describe successive iterations through approaches and prototypes for viewing genotyping errors in the context of a displayed pedigree. We describe how each approach performs with real pedigree genotype data and why even-tually we deemed them unsuitable. Finally, a novel prototype visualisation for pedigrees, which we term the 'sandwich view', is detailed and we demonstrate how the approach effectively communicates errors in the pedigree context, supporting the biologist in the error identification task.Item Interactive Visual Analysis of Temporal Cluster Structures(The Eurographics Association and Blackwell Publishing Ltd., 2011) Turkay, Cagatay; Parulek, J.; Reuter, N.; Hauser, Helwig; H. Hauser, H. Pfister, and J. J. van WijkCluster analysis is a useful method which reveals underlying structures and relations of items after grouping them into clusters. In the case of temporal data, clusters are defined over time intervals where they usually exhibit structural changes. Conventional cluster analysis does not provide sufficient methods to analyze these structural changes, which are, however, crucial in the interpretation and evaluation of temporal clusters. In this paper, we present two novel and interactive visualization techniques that enable users to explore and interpret the structural changes of temporal clusters. We introduce the temporal cluster view, which visualizes the structural quality of a number of temporal clusters, and temporal signatures, which represents the structure of clusters over time. We discuss how these views are utilized to understand the temporal evolution of clusters. We evaluate the proposed techniques in the cluster analysis of mixed lipid bilayers.Item Visual Exploration of Time-Series Data with Shape Space Projections(The Eurographics Association and Blackwell Publishing Ltd., 2011) Ward, Matthew O.; Guo, Zhenyu; H. Hauser, H. Pfister, and J. J. van WijkTime-series data is a common target for visual analytics, as they appear in a wide range of application domains. Typical tasks in analyzing time-series data include identifying cyclic behavior, outliers, trends, and periods of time that share distinctive shape characteristics. Many methods for visualizing time series data exist, generally mapping the data values to positions or colors. While each can be used to perform a subset of the above tasks, none to date is a complete solution. In this paper we present a novel approach to time-series data visualization, namely creating multivariate data records out of short subsequences of the data and then using multivariate visualization methods to display and explore the data in the resulting shape space. We borrow ideas from text analysis, where the use of N-grams is a common approach to decomposing and processing unstructured text. By mapping each temporal N-gram to a glyph, and then positioning the glyphs via PCA (basically a projection in shape space), many different kinds of patterns in the sequence can be readily identified. Interactive selection via brushing, in conjunction with linking to other visualizations, provides a wide range of tools for exploring the data. We validate the usefulness of this approach with examples from several application domains and tasks, comparing our methods with traditional time-series visualizations.Item A Visual Analytics Approach for Peak-Preserving Prediction of Large Seasonal Time Series(The Eurographics Association and Blackwell Publishing Ltd., 2011) Hao, M. C.; Janetzko, H.; Mittelstädt, S.; Hill, W.; Dayal, U.; Keim, D. A.; Marwah, M.; Sharma, R. K.; H. Hauser, H. Pfister, and J. J. van WijkTime series prediction methods are used on a daily basis by analysts for making important decisions. Most of these methods use some variant of moving averages to reduce the number of data points before prediction. However, to reach a good prediction in certain applications (e.g., power consumption time series in data centers) it is important to preserve peaks and their patterns. In this paper, we introduce automated peak-preserving smoothing and prediction algorithms, enabling a reliable long term prediction for seasonal data, and combine them with an advanced visual interface: (1) using high resolution cell-based time series to explore seasonal patterns, (2) adding new visual interaction techniques (multi-scaling, slider, and brushing & linking) to incorporate human expert knowledge, and (3) providing both new visual accuracy color indicators for validating the predicted results and certainty bands communicating the uncertainty of the prediction. We have integrated these techniques into a wellfitted solution to support the prediction process, and applied and evaluated the approach to predict both power consumption and server utilization in data centers with 70-80% accuracy.Item Pathway Preserving Representation of Metabolic Networks(The Eurographics Association and Blackwell Publishing Ltd., 2011) Lambert, Antoine; Dubois, J.; Bourqui, Romain; H. Hauser, H. Pfister, and J. J. van WijkImprovements in biological data acquisition and genomes sequencing now allow to reconstruct entire metabolic networks of many living organisms. The size and complexity of these networks prohibit manual drawing and thereby urge the need of dedicated visualization techniques. An efficient representation of such a network should preserve the topological information of metabolic pathways while respecting biological drawing conventions. These constraints complicate the automatic generation of such visualization as it raises graph drawing issues. In this paper we propose a method to lay out the entire metabolic network while preserving the pathway information as much as possible. That method is flexible as it enables the user to define whether or not node duplication should be performed, to preserve or not the network topology. Our technique combines partitioning, node placement and edge bundling to provide a pseudo-orthogonal visualization of the metabolic network. To ease pathway information retrieval, we also provide complementary interaction tools that emphasize relevant pathways in the entire metabolic context.Item Flowstrates: An Approach for Visual Exploration of Temporal Origin-Destination Data(The Eurographics Association and Blackwell Publishing Ltd., 2011) Boyandin, Ilya; Bertini, Enrico; Bak, Peter; Lalanne, Denis; H. Hauser, H. Pfister, and J. J. van WijkMany origin-destination datasets have become available in the recent years, e.g. flows of people, animals, money, material, or network traffic between pairs of locations, but appropriate techniques for their exploration still have to be developed. Especially, supporting the analysis of datasets with a temporal dimension remains a significant challenge. Many techniques for the exploration of spatio-temporal data have been developed, but they prove to be only of limited use when applied to temporal origin-destination datasets.We present Flowstrates, a new interactive visualization approach in which the origins and the destinations of the flows are displayed in two separate maps, and the changes over time of the flow magnitudes are represented in a separate heatmap view in the middle. This allows the users to perform spatial visual queries, focusing on different regions of interest for the origins and destinations, and to analyze the changes over time provided with the means of flow ordering, filtering and aggregation in the heatmap. In this paper, we discuss the challenges associated with the visualization of temporal origin-destination data, introduce our solution, and present several usage scenarios showing how the tool we have developed supports them.Item A User Study of Visualization Effectiveness Using EEG and Cognitive Load(The Eurographics Association and Blackwell Publishing Ltd., 2011) Anderson, Erik W.; Potter, K. C.; Matzen, L. E.; Shepherd, J. F.; Preston, G. A.; Silva, C. T.; H. Hauser, H. Pfister, and J. J. van WijkEffectively evaluating visualization techniques is a difficult task often assessed through feedback from user studies and expert evaluations. This work presents an alternative approach to visualization evaluation in which brain activity is passively recorded using electroencephalography (EEG). These measurements are used to compare different visualization techniques in terms of the burden they place on a viewer's cognitive resources. In this paper, EEG signals and response times are recorded while users interpret different representations of data distributions. This information is processed to provide insight into the cognitive load imposed on the viewer. This paper describes the design of the user study performed, the extraction of cognitive load measures from EEG data, and how those measures are used to quantitatively evaluate the effectiveness of visualizations.Item Visual Coherence for Large-Scale Line-Plot Visualizations(The Eurographics Association and Blackwell Publishing Ltd., 2011) Muigg, Philipp; Hadwiger, Markus; Doleisch, Helmut; Gröller, Eduard; H. Hauser, H. Pfister, and J. J. van WijkDisplaying a large number of lines within a limited amount of screen space is a task that is common to many different classes of visualization techniques such as time-series visualizations, parallel coordinates, link-node diagrams, and phase-space diagrams. This paper addresses the challenging problems of cluttering and overdraw inherent to such visualizations. We generate a 2x2 tensor field during line rasterization that encodes the distribution of line orientations through each image pixel. Anisotropic diffusion of a noise texture is then used to generate a dense, coherent visualization of line orientation. In order to represent features of different scales, we employ a multi-resolution representation of the tensor field. The resulting technique can easily be applied to a wide variety of line-based visualizations. We demonstrate this for parallel coordinates, a time-series visualization, and a phase-space diagram. Furthermore, we demonstrate how to integrate a focus+context approach by incorporating a second tensor field. Our approach achieves interactive rendering performance for large data sets containing millions of data items, due to its image-based nature and ease of implementation on GPUs. Simulation results from computational fluid dynamics are used to evaluate the performance and usefulness of the proposed method.Item Dynamic Insets for Context-Aware Graph Navigation(The Eurographics Association and Blackwell Publishing Ltd., 2011) Ghani, Sohaib; Riche, N. Henry; Elmqvist, Niklas; H. Hauser, H. Pfister, and J. J. van WijkMaintaining both overview and detail while navigating in graphs, such as road networks, airline route maps, or social networks, is difficult, especially when targets of interest are located far apart. We present a navigation technique called Dynamic Insets that provides context awareness for graph navigation. Dynamic insets utilize the topological structure of the network to draw a visual inset for off-screen nodes that shows a portion of the surrounding area for links leaving the edge of the screen. We implement dynamic insets for general graph navigation as well as geographical maps. We also present results from a set of user studies that show that our technique is more efficient than most of the existing techniques for graph navigation in different networks.Item Energy-scale Aware Feature Extraction for Flow Visualization(The Eurographics Association and Blackwell Publishing Ltd., 2011) Pobitzer, A.; Tutkun, M.; Andreassen, Ø.; Fuchs, R.; Peikert, R.; Hauser, H.; H. Hauser, H. Pfister, and J. J. van WijkIn the visualization of flow simulation data, feature detectors often tend to result in overly rich response, making some sort of filtering or simplification necessary to convey meaningful images. In this paper we present an approach that builds upon a decomposition of the flow field according to dynamical importance of different scales of motion energy. Focusing on the high-energy scales leads to a reduction of the flow field while retaining the underlying physical process. The presented method acknowledges the intrinsic structures of the flow according to its energy and therefore allows to focus on the energetically most interesting aspects of the flow. Our analysis shows that this approach can be used for methods based on both local feature extraction and particle integration and we provide a discussion of the error caused by the approximation. Finally, we illustrate the use of the proposed approach for both a local and a global feature detector and in the context of numerical flow simulations.Item Progressive Splatting of Continuous Scatterplots and Parallel Coordinates(The Eurographics Association and Blackwell Publishing Ltd., 2011) Heinrich, Julian; Bachthaler, S.; Weiskopf, Daniel; H. Hauser, H. Pfister, and J. J. van WijkContinuous scatterplots and parallel coordinates are used to visualize multivariate data defined on a continuous domain. With the existing techniques, rendering such plots becomes prohibitively slow, especially for large scientific datasets. This paper presents a scalable and progressive rendering algorithm for continuous data plots that allows exploratory analysis of large datasets at interactive framerates. The algorithm employs splatting to produce a series of plots that are combined using alpha blending to achieve a progressively improving image. For each individual frame, splats are obtained by transforming Gaussian density kernels from the 3-D domain of the input dataset to the respective data domain. A closed-form analytic description of the resulting splat footprints is derived to allow pre-computation of splat textures for efficient GPU rendering. The plotting method is versatile because it supports arbitrary reconstruction or interpolation schemes for the input data and the splatting technique is scalable because it chooses splat samples independently from the size of the input dataset. Finally, the effectiveness of the method is compared to existing techniques regarding rendering performance and quality.Item WaveMap: Interactively Discovering Features From Protein Flexibility Matrices Using Wavelet-based Visual Analytics(The Eurographics Association and Blackwell Publishing Ltd., 2011) Barlowe, Scott; Liu, Yujie; Yang, Jing; Livesay, Dennis R.; Jacobs, Donald J.; Mottonen, James; Verma, Deeptak; H. Hauser, H. Pfister, and J. J. van WijkThe knowledge gained from biology datasets can streamline and speed-up pharmaceutical development. However, computational models generate so much information regarding protein behavior that large-scale analysis by traditional methods is almost impossible. The volume of data produced makes the transition from data to knowledge difficult and hinders biomedical advances. In this work, we present a novel visual analytics approach named WaveMap for exploring data generated by a protein flexibility model. WaveMap integrates wavelet analysis, visualizations, and interactions to facilitate the browsing, feature identification, and comparison of protein attributes represented by two-dimensional plots. We have implemented a fully working prototype of WaveMap and illustrate its usefulness through expert evaluation and an example scenario.Item An Evaluation of Visualization Techniques to Illustrate Statistical Deformation Models(The Eurographics Association and Blackwell Publishing Ltd., 2011) Caban, Jesus J.; Rheingans, Penny; Yoo, T.; H. Hauser, H. Pfister, and J. J. van WijkAs collections of 2D/3D images continue to grow, interest in effective ways to visualize and explore the statistical morphological properties of a group of images has surged. Recently, deformation models have emerged as simple methods to capture the variability and statistical properties of a collection of images. Such models have proven to be effective in tasks such as image classification, generation, registration, segmentation, and analysis of modes of variation. A crucial element missing from most statistical models has been an effective way to summarize and visualize the statistical morphological properties of a group of images. This paper evaluates different visualization techniques that can be extended and used to illustrate the information captured by such statistical models. First, four illustration techniques are described as methods to summarize the statistical morphological properties as captured by deformation models. Second, results of a user study conducted to compare the effectiveness of each visualization technique are presented. After comparing the performance of 40 subjects, we found that statistical annotation techniques present significant benefits when analyzing the structural properties of a group of images.Item Curve Density Estimates(The Eurographics Association and Blackwell Publishing Ltd., 2011) Lampe, Ove Daae; Hauser, Helwig; H. Hauser, H. Pfister, and J. J. van WijkIn this work, we present a technique based on kernel density estimation for rendering smooth curves. With this approach, we produce uncluttered and expressive pictures, revealing frequency information about one, or, multiple curves, independent of the level of detail in the data, the zoom level, and the screen resolution. With this technique the visual representation scales seamlessly from an exact line drawing, (for low-frequency/low-complexity curves) to a probability density estimate for more intricate situations. This scale-independence facilitates displays based on non-linear time, enabling high-resolution accuracy of recent values, accompanied by long historical series for context. We demonstrate the functionality of this approach in the context of prediction scenarios and in the context of streaming data.