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Comparative Uncertainty Visualization for High-Level Analysis of Scalar- and Vector-Valued Ensembles
(2022)
With this thesis, I contribute to the research field of uncertainty visualization, considering parameter dependencies in multi valued fields and the uncertainty of automated data analysis. Like uncertainty visualization in general, both of these fields are becoming more and more important due to increasing computational power, growing importance and availability of complex models and collected data, and progress in artificial intelligence. I contribute in the following application areas:
Uncertain Topology of Scalar Field Ensembles.
The generalization of topology-based visualizations to multi valued data involves many challenges. An example is the comparative visualization of multiple contour trees, complicated by the random nature of prevalent contour tree layout algorithms. I present a novel approach for the comparative visualization of contour trees - the Fuzzy Contour Tree.
Uncertain Topological Features in Time-Dependent Scalar Fields.
Tracking features in time-dependent scalar fields is an active field of research, where most approaches rely on the comparison of consecutive time steps. I created a more holistic visualization for time-varying scalar field topology by adapting Fuzzy Contour Trees to the time-dependent setting.
Uncertain Trajectories in Vector Field Ensembles.
Visitation maps are an intuitive and well-known visualization of uncertain trajectories in vector field ensembles. For large ensembles, visitation maps are not applicable, or only with extensive time requirements. I developed Visitation Graphs, a new representation and data reduction method for vector field ensembles that can be calculated in situ and is an optimal basis for the efficient generation of visitation maps. This is accomplished by bringing forward calculation times to the pre-processing.
Visually Supported Anomaly Detection in Cyber Security.
Numerous cyber attacks and the increasing complexity of networks and their protection necessitate the application of automated data analysis in cyber security. Due to uncertainty in automated anomaly detection, the results need to be communicated to analysts to ensure appropriate reactions. I introduce a visualization system combining device readings and anomaly detection results: the Security in Process System. To further support analysts I developed an application agnostic framework that supports the integration of knowledge assistance and applied it to the Security in Process System. I present this Knowledge Rocks Framework, its application and the results of evaluations for both, the original and the knowledge assisted Security in Process System. For all presented systems, I provide implementation details, illustrations and applications.
Sound surrounds us all the time and in every place in our daily life, may it be pleasant music in a concert hall or disturbing noise emanating from a busy street in front of our home. The basic properties are the same for both kinds of sound, namely sound waves propagating from a source, but we perceive it in different ways depending on our current mood or if the sound is wanted or not. In this thesis both pleasant sound as well as disturbing noise is examined by means of simulating the sound and visualizing the results thereof. However, although the basic properties of music and traffic noise are the same, one is interested in different features. For example, in a concert hall, the reverberation time is an important quality measure, but if noise is considered only the resulting sound level, for example on ones balcony, is of interest. Such differences are reflected in different methods of simulation and required visualizations, therefore this thesis is divided into two parts. The first part about room acoustics deals with the simulation and novel visualizations for indoor sound and acoustic quality measures, such as definition (original "Deutlichkeit") and clarity index (original "Klarheitsmaß"). For the simulation two different methods, a geometric (phonon tracing) and a wave based (FEM) approach, are applied and compared. The visualization techniques give insight into the sound behaviour and the acoustic quality of a room from a global as well as a listener based viewpoint. Furthermore, an acoustic rendering equation is presented, which is used to render interference effects for different frequencies. Last but not least a novel visualization approach for low frequency sound is presented, which enables the topological analysis of pressure fields based on room eigenfrequencies. The second part about environmental noise is concerned with the simulation and visualization of outdoor sound with a focus on traffic noise. The simulation instruction prescribed by national regulations is discussed in detail, and an approach for the computation of noise volumes, as well as an extension to the simulation, allowing interactive noise calculation, are presented. Novel visualization and interaction techniques for the calculated noise data, incorporated in an interactive three dimensional environment, enabling the easy comprehension of noise problems, are presented. Furthermore additional information can be integrated into the framework to enhance the visualization of noise and the usability of the framework for different usages.
In urban planning, sophisticated simulation models are key tools to estimate future population growth for measuring the impact of planning decisions on urban developments and the environment. Simulated population projections usually result in large, macro-scale, multivariate geospatial data sets. Millions of records have to be processed, stored, and visualized to help planners explore and analyze complex population patterns. We introduce a database driven framework for visualizing geospatial multidimensional simulation data based on the output from UrbanSim, a software for the analysis and planning of urban developments. The designed framework is extendable and aims at integrating empirical-stochastic methods and urban simulation models with techniques developed for information visualization and cartography. First, we develop an empirical model for the estimation of residential building types based on demographic household characteristics. The predicted dwelling type information is important for the analysis of future material use, carbon footprint calculations, and for visualizing simultaneously the results of land usage, density, and other significant parameters in 3D space. Our model uses multinomial logistic regression to derive building types at different scales. The estimated regression coefficients are applied to UrbanSim output in order to predict residential building types. The simulation results and the estimated building types are managed in an object-relational geodatabase. From the database, density, building types, and significant demographic variables are visually encoded as scalable, georeferenced 3D geometries and displayed on top of aerial photographs in a Google Earth visual synthesis. The geodatabase can be accessed and the visualization parameters can be chosen through a web-based user interface. The geometries are encoded in KML, Google's markup language, as ready-to-visualize data sets. The goal is to enhance human cognition by displaying abstract representations of multidimensional data sets in a realistic context and thus to support decision making in planning processes.
Multi-Field Visualization
(2011)
Modern science utilizes advanced measurement and simulation techniques to analyze phenomena from fields such as medicine, physics, or mechanics. The data produced by application of these techniques takes the form of multi-dimensional functions or fields, which have to be processed in order to provide meaningful parts of the data to domain experts. Definition and implementation of such processing techniques with the goal to produce visual representations of portions of the data are topic of research in scientific visualization or multi-field visualization in the case of multiple fields. In this thesis, we contribute novel feature extraction and visualization techniques that are able to convey data from multiple fields created by scientific simulations or measurements. Furthermore, our scalar-, vector-, and tensor field processing techniques contribute to scattered field processing in general and introduce novel ways of analyzing and processing tensorial quantities such as strain and displacement in flow fields, providing insights into field topology. We introduce novel mesh-free extraction techniques for visualization of complex-valued scalar fields in acoustics that aid in understanding wave topology in low frequency sound simulations. The resulting structures represent regions with locally minimal sound amplitude and convey wave node evolution and sound cancellation in time-varying sound pressure fields, which is considered an important feature in acoustics design. Furthermore, methods for flow field feature extraction are presented that facilitate analysis of velocity and strain field properties by visualizing deformation of infinitesimal Lagrangian particles and macroscopic deformation of surfaces and volumes in flow. The resulting adaptive manifolds are used to perform flow field segmentation which supports multi-field visualization by selective visualization of scalar flow quantities. The effects of continuum displacement in scattered moment tensor fields can be studied by a novel method for multi-field visualization presented in this thesis. The visualization method demonstrates the benefit of clustering and separate views for the visualization of multiple fields.
In urban planning, both measuring and communicating sustainability are among the most recent concerns. Therefore, the primary emphasis of this thesis concerns establishing metrics and visualization techniques in order to deal with indicators of sustainability.
First, this thesis provides a novel approach for measuring and monitoring two indicators of sustainability - urban sprawl and carbon footprints – at the urban neighborhood scale. By designating different sectors of relevant carbon emissions as well as different household categories, this thesis provides detailed information about carbon emissions in order to estimate impacts of daily consumption decisions and travel behavior by household type. Regarding urban sprawl, a novel gridcell-based indicator model is established, based on different dimensions of urban sprawl.
Second, this thesis presents a three-step-based visualization method, addressing predefined requirements for geovisualizations and visualizing those indicator results, introduced above. This surface-visualization combines advantages from both common GIS representation and three-dimensional representation techniques within the field of urban planning, and is assisted by a web-based graphical user interface which allows for accessing the results by the public.
In addition, by focusing on local neighborhoods, this thesis provides an alternative approach in measuring and visualizing both indicators by utilizing a Neighborhood Relation Diagram (NRD), based on weighted Voronoi diagrams. Thus, the user is able to a) utilize original census data, b) compare direct impacts of indicator results on the neighboring cells, and c) compare both indicators of sustainability visually.
Ultraschall ist eines der am häufigsten genutzen, bildgebenden Verfahren in der Kardiologie. Dies ist durch die günstige Erzeugung, die Nicht-Invasivität und die Unschädlichkeit für die Patienten begründet. Nachteilig an den existierenden Geräten ist der Umstand, daß lediglich zwei-dimensionale Bilder generiert werden können. Zusätzlich können diese Bilder aufgrund anatomischer Gegebenheiten nicht aus einer wahlfreien Position akquiriert werden. Dies erschwert die Analyse der Daten und folglich die Diagnose. Mit dieser Arbeit wurden neue, algorithmische Aspekte des vier-dimensionalen, kardiologischen Ultraschalls ausgehend von der Akquisition der Rohdaten, deren Synchronisation und Rekonstruktion bis hin zur Visualisierung bearbeitet. In einem zusätzlichen Kapitel wurde eine neue Technik zur weiteren Aufwertung der Visualisierung, sowie zur visuellen Bearbeitung der Ultraschalldaten entwickelt. Durch die hier entwickelten Verfahren ist es möglich bestimmte Einschränkungen des kardiologischen Ultraschalls aufzuheben oder zumindest zu mildern. Hierunter zählen vor allem die Einschränkung auf zwei-dimensionale Schnittbilder, sowie die eingeschränkte Sichtwahl.
Die Computerisierung der Gesellschaft bedingt ein ständiges Zunehmen der Geschwindigkeit, mit der neue Daten erzeugt werden. Parallel zu dieser Entwicklung steigt der Bedarf an geeigneten Analyseverfahren, die in diesen großen und oftmals heterogenen Datenmengen Muster finden, Zusammenhänge entdecken und damit Wissen erzeugen. Das in dieser Arbeit entwickelte Verfahren findet die passende Struktur in einer ungeordneten, abstrakten Datenmenge, ordnet die zugrunde liegenden Informationen und bündelt diese somit für eine gezielte Anwendung. Dieser Prozess des Information Clustering ist zweistufig, es erfolgt zuerst ein generelles Clustering, an das sich eine interpretierende Visualisierung anschliesst. Für das Clustering wird das Verfahren der Voronoidiagramme erweitert. Durch den Einsatz einer generellen Distanzfunktion wird die Modellierung der durch die großen Datenmengen entstehenden multidimensionalen Parameter sowie weiterer Gewichte ermöglicht. Eine anschließende Visualisierung aus dem Bereich der Informationsvisualisierung unterstützt die Interpretation der neu gewonnenen Informationen. Für die praktische Anwendung wird die Stadtplanung betrachtet. In der Stadtplanung wird das Modell des Planungsablaufes eingesetzt, mit dem verschiedene Planungsalternativen erzeugt werden. Dieses Modell ist jedoch zu starr, um den dynamischen Anforderungen in der Realität gerecht zu werden. Das Information Clustering erweitert den klassischen Planungsablauf, die Flexibilität des Modells wird dadurch erhöht und die Komplexität reduziert. Das Ergebnis der Berechnung ist genau eine Planungsalternative, die sämtliche Eingabeparameter kanalisiert.
Knowledge discovery from large and complex collections of today’s scientific datasets is a challenging task. With the ability to measure and simulate more processes at increasingly finer spatial and temporal scales, the increasing number of data dimensions and data objects is presenting tremendous challenges for data analysis and effective data exploration methods and tools. Researchers are overwhelmed with data and standard tools are often insufficient to enable effective data analysis and knowledge discovery. The main objective of this thesis is to provide important new capabilities to accelerate scientific knowledge discovery form large, complex, and multivariate scientific data. The research covered in this thesis addresses these scientific challenges using a combination of scientific visualization, information visualization, automated data analysis, and other enabling technologies, such as efficient data management. The effectiveness of the proposed analysis methods is demonstrated via applications in two distinct scientific research fields, namely developmental biology and high-energy physics. Advances in microscopy, image analysis, and embryo registration enable for the first time measurement of gene expression at cellular resolution for entire organisms. Analysis of highdimensional spatial gene expression datasets is a challenging task. By integrating data clustering and visualization, analysis of complex, time-varying, spatial gene expression patterns and their formation becomes possible. The analysis framework MATLAB and the visualization have been integrated, making advanced analysis tools accessible to biologist and enabling bioinformatic researchers to directly integrate their analysis with the visualization. Laser wakefield particle accelerators (LWFAs) promise to be a new compact source of highenergy particles and radiation, with wide applications ranging from medicine to physics. To gain insight into the complex physical processes of particle acceleration, physicists model LWFAs computationally. The datasets produced by LWFA simulations are (i) extremely large, (ii) of varying spatial and temporal resolution, (iii) heterogeneous, and (iv) high-dimensional, making analysis and knowledge discovery from complex LWFA simulation data a challenging task. To address these challenges this thesis describes the integration of the visualization system VisIt and the state-of-the-art index/query system FastBit, enabling interactive visual exploration of extremely large three-dimensional particle datasets. Researchers are especially interested in beams of high-energy particles formed during the course of a simulation. This thesis describes novel methods for automatic detection and analysis of particle beams enabling a more accurate and efficient data analysis process. By integrating these automated analysis methods with visualization, this research enables more accurate, efficient, and effective analysis of LWFA simulation data than previously possible.
Die vorliegende Arbeit beschäftigt sich mit der visuellen Kontrolle raumplanerischer Entwürfe. Grundlage der Überlegungen ist das gegenwärtige Verfahren, der Planungsprozess, das zur Erstellung der Entwürfe führt. Der Entscheidungsweg hin zum endgültigen Ergebnis erfolgt zurzeit noch ohne Rechnerunterstützung. Die in den Planungsprozess Involvierten stützen ihre Entscheidungen bspw. auf Pläne, eigene Erfahrungen und Statistiken und fertigen im Verlauf von Diskussionsrunden verschiedene Entwürfe an. Dieser Ablauf ist komplex, aufgrund der eingehenden Daten und der damit zusammenhängenden Diskussionen, und langwierig da erst nach einigen Iterationsschritten ein Ergebnis vorliegt. Die Arbeit verfolgt das Ziel, die Akteure durch eine Rechnerunterstützung schneller und zielgerichtet zu einer Entscheidungsfindung zu führen. Meine Untersuchung des Anwendungsumfeldes hat ergeben, dass dies nur möglich ist, wenn zum Einen das entstehende System in der Lage ist, die großen, heterogenen Datenmengen zu verarbeiten und andererseits die Visualisierung der Ergebnisse in einer Form erfolgt, die den Akteuren vom bisherigen Planungsprozess her bekannt ist. Die Visualisierung darf dabei keine bewertende Aussage treffen, sondern muss die Informationen der Analyse neutral in einem dem Nutzer bekannten Format abbilden. Als Ansatzpunkt stellt sich der informelle Bereich der Entscheidungsfindung dar. Es werden zwei Lösungswege aus dem Bereich der Clusteringalgorithmen verfolgt, die die großen Datenmengen verarbeiten und analysieren. Als Ergebnis erhalten die Akteure durch das Voronoi-Diagramm direkt einen Entwurf, der die Einschätzungen aller Akteure widerspiegelt und durch ein Übereinanderlegen mit der Karte des Plangebietes dem klassischen Format im Rahmen des Planungsprozesses entspricht. Dadurch wird die Akzeptanz der Rechnerunterstützung bei den Beteiligten des Planungsprozesses gesteigert. Sollte dieser Entwurf noch keine direkte Zustimmung finden, kann über die entwickelte Informationsvisualisierung eine Anzeige und in der Folge eine Anpassung der Eingangsgrößen erfolgen und somit sehr schnell ein neuer Entwurf entwickelt werden. Die Visualisierung übernimmt dabei die Funktion der bisher in Papierform erstellten Pläne im Entscheidungsprozess und bietet damit auch fachfremden Beteiligten eine visuelle Kontrollmöglichkeit der Qualität des Entwurfes. Insgesamt werden mit dem Tool IKone die Akteure in Anlehnung an die standardmäßigen Abläufe und visuellen Darstellungen mittels eines rechnergestützten Systems unterstützt.
The recognition of patterns and structures has gained importance for dealing with the growing amount of data being generated by sensors and simulations. Most existing methods for pattern recognition are tailored for scalar data and non-correlated data of higher dimensions. The recognition of general patterns in flow structures is possible, but not yet practically usable, due to the high computation effort. The main goal of this work is to present methods for comparative visualization of flow data, amongst others, based on a new method for efficient pattern recognition on flow data. This work is structured in three parts: At first, a known feature-based approach for pattern recognition on flow data, the Clifford convolution, has been applied to color edge detection, and been extended to non-uniform grids. However, this method is still computationally expensive for a general pattern recognition, since the recognition algorithm has to be applied for numerous different scales and orientations of the query pattern. A more efficient and accurate method for pattern recognition on flow data is presented in the second part. It is based upon a novel mathematical formulation of moment invariants for flow data. The common moment invariants for pattern recognition are not applicable on flow data, since they are only invariant on non-correlated data. Because of the spatial correlation of flow data, the moment invariants had to be redefined with different basis functions to satisfy the demands for an invariant mapping of flow data. The computation of the moment invariants is done by a multi-scale convolution of the complete flow field with the basis functions. This pre-processing computation time almost equals the time for the pattern recognition of one single general pattern with the former algorithms. However, after having computed the moments once, they can be indexed and used as a look-up-table to recognize any desired pattern quickly and interactively. This results in a flexible and easy-to-use tool for the analysis of patterns in 2d flow data. For an improved rendering of the recognized features, an importance driven streamline algorithm has been developed. The density of the streamlines can be adjusted by using importance maps. The result of a pattern recognition can be used as such a map, for example. Finally, new comparative flow visualization approaches utilizing the streamline approach, the flow pattern matching, and the moment invariants are presented.
Today’s digital world would be unthinkable without complex data sets. Whether in private, business or industrial environments, complex data provide the basis for important and critical decisions and determine many processes, some of which are automated. This is often associated with Big Data. However, often only one aspect of the usual Big Data definitions is sufficient and a human observer can no longer capture the data completely and correctly. In this thesis, different approaches are presented in order to master selected challenges in a more effective, efficient and userfriendly way. The approaches range from easier pre-processing of data sets for later analysis and the identification of design guidelines of such assistants, new visualization techniques for presenting uncertainty, extensions of existing visualizations for categorical data, concepts for time-saving selection methods for subsets of data points and faster navigation and zoom interaction–especially in the web-based area with enormous amounts of data–to new and innovative orientation-based interaction metaphors for mobile devices as well as stationary working environments. Evaluations and appropriate use case of the individual approaches show the usability also in comparison with state-of-the-art techniques.
Due to the steadily growing flood of data, the appropriate use of visualizations for efficient data analysis is as important today as it has never been before. In many application domains, the data flood is based on processes that can be represented by node-link diagrams. Within such a diagram, nodes may represent intermediate results (or products), system states (or snapshots), milestones or real (and possibly georeferenced) objects, while links (edges) can embody transition conditions, transformation processes or real physical connections. Inspired by the engineering sciences application domain and the research project “SinOptiKom: Cross-sectoral optimization of transformation processes in municipal infrastructures in rural areas”, a platform for the analysis of transformation processes has been researched and developed based on a geographic information system (GIS). Caused by the increased amount of available and interesting data, a particular challenge is the simultaneous visualization of several visible attributes within one single diagram instead of using multiple ones. Therefore, two approaches have been developed, which utilize the available space between nodes in a diagram to display additional information.
Motivated by the necessity of appropriate result communication with various stakeholders, a concept for a universal, dashboard-based analysis platform has been developed. This web-based approach is conceptually capable of displaying data from various data sources and has been supplemented by collaboration possibilities such as sharing, annotating and presenting features.
In order to demonstrate the applicability and usability of newly developed applications, visualizations or user interfaces, extensive evaluations with human users are often inevitable. To reduce the complexity and the effort for conducting an evaluation, the browser-based evaluation framework (BREF) has been designed and implemented. Through its universal and flexible character, virtually any visualization or interaction running in the browser can be evaluated with BREF without any additional application (except for a modern web browser) on the target device. BREF has already proved itself in a wide range of application areas during the development and has since grown into a comprehensive evaluation tool.
Angesichts des anhaltenden demografischen, ökonomischen und gesellschaftlichen Wandels steht die Stadtplanung großen Herausforderungen gegenüber. Insbesondere die Reaktivierung und Revitalisierung der innerstädtischen Bereiche als den räumlichen, funk-tionalen und emotionalen Zentren der Städte bilden hierbei ein wesentliches Handlungsfeld. Neben den Auswirkungen auf den gesamten Planungsprozess beeinflussen die daraus resultierenden Handlungserfordernisse insbesondere den stadtplanerischen Entwurf im Sinne der Organisation räumlicher Strukturen und ihrer Wechselwirkungen. Aufgrund der hohen Komplexität der zu bewältigenden Aufgaben beim Entwerfen im innerstädtischen Kontext kommt hierbei den informationstechnischen Möglichkeiten der I&K-Technologien in Form von Visualisierungen und Simulationen große Bedeutung zu.
Vor dem Hintergrund der Herausforderungen des Entwerfens im innerstädtischen Kontext liegt das Ziel der Arbeit in der Erarbeitung von Ansätzen zur Qualifizierung des stadtpla-nerischen Entwurfsprozesses durch computerbasierte Visualisierungen und Simulationen. Hierbei gilt es zunächst, die gegebenen Rahmenbedingungen und Entwicklungstendenzen in den Innenstädten hinsichtlich der daraus resultierenden Handlungserfordernisse für das stadtplanerische Handeln zu untersuchen. Besagte Erfordernisse umfassen beispielsweise die Stärkung der Innenstädte als Wohnstandort, als Zentren des Handels, der Dienstleistung und der Kultur sowie als Bereiche hoher Dichte und Nutzungsmischung. Gleichzeitig gilt es, den Forderungen der integrierten Innenstadtentwicklung gerecht zu werden, in deren Rahmen ein tragfähiger stadtplanerischer Entwurf, unter Berücksichti-gung ganzheitlicher Strategien, zum raumverträglichen und zukunftsfähigen Ausgleich der unterschiedlichen Ansprüche und Interessen beitragen muss.
Bezogen auf die Möglichkeiten zur Unterstützung und Qualifizierung des Entwurfsprozes-ses durch Visualisierung raumbezogener Informationen und möglicher Entwicklungszu-stände steht neben der Analyse bestehender Techniken und Anwendungen die Ausei-nandersetzung mit einer Vielzahl technischer Neuerungen im Fokus. Diese technischen Neuerungen zeigen sich insbesondere in den Bereichen der GI-Systeme, der 3D-Stadtmodelle, der sog. ‚Neogeografie’ sowie der ‚Augmented Reality’ und ‚Virtual Reality’. Im Rahmen der Notwendigkeit zur Simulation dynamischer urbaner Prozesse bildet die Suche nach flexiblen, modular erweiterbaren Simulationsarchitekturen sowie die Möglich-keiten zur Simulation räumlicher und gesellschaftlicher Prozesse mittels automatenbasier-ter Modelle den Schwerpunkt.
Zusammenfassend erfordert der effektive und ökonomische Einsatz von Anwendungen der Visualisierung und Simulation die enge Verknüpfung und Abstimmung der gegebenen Einsatzmöglichkeiten mit den inhaltlichen und methodischen Anforderungen des stadtpla-nerischen Entwurfs.
Im Ergebnis werden zwei Ebenen zur Qualifizierung des innerstädtischen Entwurfspro-zesses durch Techniken und Anwendungen der Visualisierung und Simulation identifiziert und daraus Ansätze für neue Entwurfstools abgeleitet. Die erste Ebene beinhaltet die me-thodisch-inhaltliche Auseinandersetzung mit den Möglichkeiten zur Verknüpfung von Vi-sualisierungen und Simulationen mit den jeweiligen Stufen des stadtplanerischen Ent-wurfsprozesses sowie den dort zu leistenden Aufgaben. Die zweite Ebene bildet schließlich die Auseinandersetzung mit den Möglichkeiten zur zielgerichteten Weiterentwicklung und/ oder Synthese bestehender Techniken und Anwendungen.
Due to remarkable technological advances in the last three decades the capacity of computer systems has improved tremendously. Considering Moore's law, the number of transistors on integrated circuits has doubled approximately every two years and the trend is continuing. Likewise, developments in storage density, network bandwidth, and compute capacity show similar patterns. As a consequence, the amount of data that can be processed by today's systems has increased by orders of magnitude. At the same time, however, the resolution of screens has hardly increased by a factor of ten. Thus, there is a gap between the amount of data that can be processed and the amount of data that can be visualized. Large high-resolution displays offer a way to deal with this gap and provide a significantly increased screen area by combining the images of multiple smaller display devices. The main objective of this dissertation is the development of new visualization and interaction techniques for large high-resolution displays.