Ergonomie ist seit Jahren ein wichtiges Thema in allen Bereichen des Lebens. Unter anderem beinhaltet dies auch die Anpassung der Arbeit an den Arbeiter, um so kurz- und langfristige gesundheitliche Probleme zu minimieren. Ergonomen begutachten in diesem Kontext zum Beispiel den Arbeitsplatz eines Werkers. Sie beobachten ihn bei seiner Tätigkeit, führen Messungen durch und untersuchen die erlangten Daten. Nach der Analyse präsentieren sie ihre Erkenntnisse Dritten (bspw. im Rahmen eines Workshops), dem Management und den Werkern. Insbesondere wenn es um die Auswertung über einen längeren Zeitraum geht (eine ganze Schicht oder mehrere Arbeitszyklen) sind gute Visualisierungen Mangelware und beschränken sich häufig nur auf einfache Linien- oder Balkendiagramme. Um den Ergonomen ihre Tätigkeit zu erleichtern, wurde ein Programm entwickelt, dass die ergonomischen Probleme in einer einfachen Weise als Gesamtüberblick dargestellt. Es ist hierbei möglich von der groben Übersicht aller kritischen Situationen bis hin zu einer detaillierten Ansicht eines Einzelfalls zu navigieren. Ungünstige Posen werden als Piktogramme in Form digitaler Menschmodelle repräsentiert und ermöglichen somit eine intuitive Darstellung. Die Visualisierung berücksichtigt mehrere Methoden aus dem Umfeld der Ergonomie. Eine Methode ist zum Beispiel RULA (Rapid Upper Limb Assessment). Hier wird eine statische Pose des Werkers betrachtet, sozusagen eine Momentaufnahme bei der Ausübung seiner Arbeit. Es basiert auf der Betrachtung der Gelenkwinkel und berücksichtigt zusätzlich Gewichte und Kräfte, die der Werker zu tragen hat beziehungsweise die er aufbringen muss. Darüber hinaus werden auch Anzeichen grenzwertiger Arbeitsumweltfaktoren dargestellt. Die finale interaktive Visualisierung wurde während des gesamten Entwicklungsprozesses in unterschiedlichen Studien evaluiert. Dies beinhaltet unter anderem die Erfassung der Benutzeranforderungen durch Expertenbefragungen, iteratives Prototyping und einer abschließenden Benutzbarkeitsstudie. / Ergonomics deals with the analysis and design of work processes. To identify ergonomically critical situations, appropriate evaluation options have to be developed to enable an efficient analysis process. Nowadays, such analyses are typically carried out with the help of digital models of the surrounding area and virtual humans. These produce a multitude of geo-referenced and time-oriented data. A design study was conducted on how to visualize this data to support the ergonomically analysis process optimally. We propose a novel interactive visualization which provides the user an overview of ergonomically critical situations and their causes. Simultaneously, the user obtains the main stress factors in a compressed form by a glyph-based visual design. During the implementation of the prototype, usability and ergonomics experts were consulted and their suggestions were included in the development of the system. Final expert interviews and a usability study depict the utility of the proposed visualization tool.

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Networks of nodes and links are powerful tools to model complex systems, however, when the number of nodes and links grows to the thousands then even the network becomes too complex to comprehend unless we can simplify and highlight the networks underlying structure. The map equation is a method developed by the researchers at IntegratedScience Lab that uses an information theoretic approach to reveal community structure using the flow of information on the network modeled with random walks. Describing these random paths using Huffman codes and utilizing the duality between compression and finding regularities changes the problem of finding community structure into a compression problem. The success of the map equation method depends on the user’s understanding of the method. This thesis describes the design and implementation of a web-based demo application to showcase the mechanics of the map equation aimed to make the map equation more accessible to individuals with no prior knowledge of the method. / Nätverk av noder och länkar är ett kraftigt verktyg för att modellerakomplexa system. Problemet nätverk har är att när antalet noder och länkar växer upp till tusentals noder och länkar, då blir dem snabbt väldigt svåra att tyda och nästan omöjliga att förstå om vi inte kan förenkla och lyfta fram nätverkets underliggande struktur. Kartekvationen är en metod som utvecklats av forskarna vid Integrated Science Labsom använder informationsteori för att avslöja ett nätverks underliggandestruktur med hjälp av informationsflöden på nätverket modellerat med slumpmässiga vandringar. Vi beskriver slumpvandrarens bana när den rör sig på nätverket med Huffman-kodord och använder oss av dualiteten mellan att komprimera data och att hitta regelbundenheter i den data vilket då ändrar problemet av att hitta nätverksstrukturen till ett komprimeringsproblem. Kartekvationens framgång är beroende av användarens förståelse av metoden. Integrated Science Lab vill ha en webbaserad demoapplikation för att introducera användare till kartekvationen och visa upp hur metoden fungerar. Den här avhandlingen beskriver designen och implementationen av den web-baserade demoapplikationenför kartekvationen med syftet att göra kartekvationen mer tillgänglig för individer utan förkunskaper om metoden.

Nous proposons une approche semi-automatique pour la rétro-ingénierie des diagrammes de séquence d’UML. Notre approche commence par un ensemble de traces d'exécution qui sont automatiquement alignées pour déterminer le comportement commun du système. Les diagrammes de séquence sont ensuite extraits avec l’aide d’une visualisation interactive, qui permet la navigation dans les traces d'exécution et la production des opérations d'extraction. Nous fournissons une illustration concrète de notre approche avec une étude de cas, et nous montrons en particulier que nos diagrammes de séquence générés sont plus significatifs et plus compacts que ceux qui sont obtenus par les méthodes automatisées. / We propose a semi-automated approach for the reverse engineering of UML sequence diagrams. Our approach starts with a set of execution traces that are automatically aligned to determine the common behavior. Sequence diagrams are then extracted with the help of an interactive visualization, which allows navigating though execution traces and performing extraction operations. We provide a concrete illustration of our approach with a case study, and show in particular that the resulting diagrams are more meaningful and more compact than those extracted by automated approaches.

The Deepwater Horizon oil spill that started on April 20, 2010, in the Gulf of Mexico was the largest marine oil spill in the history of the petroleum industry. There was an unexpected and prolonged sedimentation event of oil-associated marine snow to the seafloor due to the oil spill. The sedimentation event occurred because of the coagulation process among oil associated marine particles. Marine scientists are developing models for the coagulation process of marine particles and oil, in order to estimate the amount of oil that may reach the seafloor along with marine particles. These models, used certain assumptions regarding the shape and the texture parameters of marine particles. Such assumptions may not be based on accurate information or may vary during and after the oil spill. The work performed here provided a quantitative analysis of the assumptions used in modeling the coagulation process of marine particles. It also investigated the changes in model parameters (shape and texture) during and after the Deepwater Horizon oil spill in different seasons (spring and summer). An Interactive Visualization Application was developed for data exploration and visual analysis of the trends in these parameters. An Interactive Statistical Analysis Application was developed to create a statistical summary of these parameter values.

<p>This study has been carried out in cooperation with Unilever and partly with the EC founded project, Smartdoc IST-2000-28137. </p><p>In areas of statistics and image processing, both the amount of data and the dimensions are increasing rapidly and an interactive visualization tool that lets the user perform real-time analysis can save valuable time. Real-time cropping and drill-down considerably facilitate the analysis process and yield more accurate decisions. </p><p>In the Smartdoc project, there has been a request for a component used for smart filtering in multidimensional data sets. As the Smartdoc project aims to develop smart, interactive components to be used on low-end systems, the implementation of the self-organizing map algorithm proposes which dimensions to visualize. </p><p>Together with Dr. Robert Treloar at Unilever, the SOM Visualizer - an application for interactive visualization and analysis of multidimensional data - has been developed. The analytical part of the application is based on Kohonen’s self-organizing map algorithm. In cooperation with the Smartdoc project, a component has been developed that is used for smart filtering in multidimensional data sets. Microsoft Visual Basic and components from the graphics library AVS OpenViz are used as development tools.</p>

<p>Understanding the structure of a network is an essential part in understanding the behavior of the system it represents, but as the system becomes really large, a visualization of the full network looses its potential to reveal important structural relationships. Then we need ways to simplify and highlight the important structures of the network while the details are filtered out, just like good maps do. We have developed an interactive application that utilizes mathematical methods based on network and information theory to reveal the important patterns hidden in huge amount of interaction data. The application gives the professional as well as the nonprofessional user the ability to load his or hers own file containing the network data, from mobile phone networks and social online networks to transport networks and financial networks and lets you explore the data and generate a customized map which highlights the influential patterns in your data. A demo application is also developed to demonstrate the mathematical and information-theoretical principles behind the map generation.</p> / <p>För att koppla form till funktion är nätverk ett oumbärligt verktyg, men när systemen blir riktigt stora förlorar nätverken sin förmåga att avslöja viktiga strukturella samband. Då behövs det kraftfulla metoder för att förenkla och framhäva de viktiga strukturerna i nätverken samtidigt som detaljerna filtreras bort, precis som bra kartor gör. Vi har utvecklat en interaktiv applikation som utnyttjar matematiska metoder baserat på nätverks- och in- formationsteori för att avslöja viktiga mönster som ligger dolt i myllret av interaktionsdata. Du kan läsa in din egen fil med nätverksdata, från telekommunikationsnätverk och sociala online-nätverk till transportnärverk och finansiella nätverk, och få tillbaka en skräddarsydd karta som låter dig upptäcka de inflytelserika mönstren i nätverket. En demo-applikation är också utvecklad för att demonstrera de matematiska och informationsteoretiska principerna bakom kartgenereringen.</p>

Understanding the structure of a network is an essential part in understanding the behavior of the system it represents, but as the system becomes really large, a visualization of the full network looses its potential to reveal important structural relationships. Then we need ways to simplify and highlight the important structures of the network while the details are filtered out, just like good maps do. We have developed an interactive application that utilizes mathematical methods based on network and information theory to reveal the important patterns hidden in huge amount of interaction data. The application gives the professional as well as the nonprofessional user the ability to load his or hers own file containing the network data, from mobile phone networks and social online networks to transport networks and financial networks and lets you explore the data and generate a customized map which highlights the influential patterns in your data. A demo application is also developed to demonstrate the mathematical and information-theoretical principles behind the map generation. / För att koppla form till funktion är nätverk ett oumbärligt verktyg, men när systemen blir riktigt stora förlorar nätverken sin förmåga att avslöja viktiga strukturella samband. Då behövs det kraftfulla metoder för att förenkla och framhäva de viktiga strukturerna i nätverken samtidigt som detaljerna filtreras bort, precis som bra kartor gör. Vi har utvecklat en interaktiv applikation som utnyttjar matematiska metoder baserat på nätverks- och in- formationsteori för att avslöja viktiga mönster som ligger dolt i myllret av interaktionsdata. Du kan läsa in din egen fil med nätverksdata, från telekommunikationsnätverk och sociala online-nätverk till transportnärverk och finansiella nätverk, och få tillbaka en skräddarsydd karta som låter dig upptäcka de inflytelserika mönstren i nätverket. En demo-applikation är också utvecklad för att demonstrera de matematiska och informationsteoretiska principerna bakom kartgenereringen.

Nous proposons une approche semi-automatique pour la rétro-ingénierie des diagrammes de séquence d’UML. Notre approche commence par un ensemble de traces d'exécution qui sont automatiquement alignées pour déterminer le comportement commun du système. Les diagrammes de séquence sont ensuite extraits avec l’aide d’une visualisation interactive, qui permet la navigation dans les traces d'exécution et la production des opérations d'extraction. Nous fournissons une illustration concrète de notre approche avec une étude de cas, et nous montrons en particulier que nos diagrammes de séquence générés sont plus significatifs et plus compacts que ceux qui sont obtenus par les méthodes automatisées. / We propose a semi-automated approach for the reverse engineering of UML sequence diagrams. Our approach starts with a set of execution traces that are automatically aligned to determine the common behavior. Sequence diagrams are then extracted with the help of an interactive visualization, which allows navigating though execution traces and performing extraction operations. We provide a concrete illustration of our approach with a case study, and show in particular that the resulting diagrams are more meaningful and more compact than those extracted by automated approaches.

[pt] A visualização volumétrica é uma importante técnica para a exploração de dados tridimensionais complexos, como, por exemplo, o resultado de análises numéricas usando o método dos elementos finitos. A aplicação eficiente dessa técnica a malhas não-estruturadas tem sido uma importante área de pesquisa nos últimos anos. Há dois métodos básicos para a visualização dos dados volumétricos: extração de superfícies e renderização direta de volumes. Na primeira, iso-superfícies de um campo escalar são extraídas explicitamente. Na segunda, que é a utilizada neste trabalho, dados escalares são classificados a partir de uma função de transferência, que mapeia valores do campo escalar em cor e opacidade, para serem visualizados. Com a evolução das placas gráficas (GPU) dos computadores pessoais, foram desenvolvidas novas técnicas para visualização volumétrica interativa de malhas não-estruturadas. Os novos algoritmos tiram proveito da aceleração e da possibilidade de programação dessas placas, cujo poder de processamento cresce a um ritmo superior ao dos processadores convencionais (CPU). Este trabalho avalia e compara dois algoritmos para visualização volumétrica de malhas não-estruturadas, baseados em GPU: projeção de células independente do observador e traçado de raios. Adicionalmente, são propostas duas adaptações dos algoritmos estudados. Para o algoritmo de projeção de células, propõe-se uma estruturação dos dados na GPU para eliminar o alto custo de transferência de dados para a placa gráfica. Para o algoritmo de traçado de raios, propõe-se fazer a integração da função de transferência na GPU, melhorando a qualidade da imagem final obtida e permitindo a alteração da função de transferência de maneira interativa. / [en] Volume visualization is an important technique for the exploration of threedimensional complex data sets, such as the results of numerical analysis using the finite elements method. The efficient application of this technique to unstructured meshes has been an important area of research in the past few years. There are two basic methods to visualize volumetric data: surface extraction and direct volume rendering. In the first, the iso-surfaces of the scalar field are explicitly extracted. In the second, which is the one used in this work, scalar data are classified by a transfer function, which maps the scalar values to color and opacity, to be visualized. With the evolution of personal computer graphics cards (GPU), new techniques for volume visualization have been developed. The new algorithms take advantage of modern programmable graphics cards, whose processing power increases at a faster rate than the one observed in conventional processors (CPU). This work evaluates and compares two GPU- based algorithms for volume visualization of unstructured meshes: view- independent cell projection (VICP) and ray-tracing. In addition, two adaptations of the studied algorithms are proposed. For the cell projection algorithm, we propose a GPU data structure in order to eliminate the high costs of the CPU to GPU data transfer. For the raytracing algorithm, we propose to integrate the transfer function in the GPU, which increases the quality of the generated image and allows to interactively change the transfer function.