Kaiserslautern - Fachbereich Informatik
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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.
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.
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.
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.
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.
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 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.
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.
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.
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.