The purpose of the thesis is to develop a new visualization method for Gene Ontologiesand hierarchical clustering. These are both important tools in biology andmedicine to study high-throughput data such as transcriptomics and metabolomicsdata. Enrichment of ontology terms in the data is used to identify statistically overrepresentedontology terms, that give insight into relevant biological processes orfunctional modules. Hierarchical clustering is a standard method to analyze andvisualize data to nd relatively homogeneous clusters of experimental data points.Both methods support the analysis of the same data set, but are usually consideredindependently. However, often a combined view such as: visualizing a large data setin the context of an ontology under consideration of a clustering of the data.The result of the current work is a user-friendly program that combines twodi erent views for analysing Gene Ontology and Cluster simultaneously. To makeexplorations of such a big data possible we developed new visualization approach.

L'analyse de réseaux (représentés par des graphes) est une composante importante dans la compréhension de systèmes complexes issus de nombreuses disciplines telles que la biologie, la géographie ou la sociologie. Nous nous intéressons dans cette thèse aux décompositions de ces réseaux. Ces décompositions sont utiles pour la compression des données, la détection de communautés ou la visualisation de graphes. Une décomposition possible est un partitionnement hiérarchique des sommets du graphe. Nous traitons de l'évaluation de la qualité de telles structures (leur capacité à bien capturer la topologie du graphe) par le biais de mesures de qualité. Nous discutons ensuite l'utilisation de ces mesures en tant que fonctions objectives à maximiser dans le cadre d'algorithmes de partitionnement. Enfin, nous nous intéressons à la définition de métaphores visuelles efficaces permettant de représenter différentes décompositions de graphes. / Network analysis is an important step in the understanding of complex systems studied in various areas such as biology, geography or sociology. This thesis focuses on the problems related to the decomposition of those networks when they are modeled by graphs. Graph decomposition methods are useful for data compression, community detection or network visualisation. One possible decomposition is a hierarchical partition of the set of vertices. We propose a method to evaluate the quality of such structures using quality measures and algorithms to maximise those measures. We also discuss the design of effective visual metaphors to represent various graph decompositions.

Indiana University-Purdue University Indianapolis (IUPUI) / A Multigraph is a set of graphs with a common set of nodes but different sets of edges. Multigraph visualization has not received much attention so far. In this thesis, I will introduce an interactive application in brain network data analysis that has a strong need for multigraph visualization. For this application, multigraph was used to represent brain connectome networks of multiple human subjects. A volumetric data set was constructed from the matrix representation of the multigraph. A volume visualization tool was then developed to assist the user to interactively and iteratively detect network features that may contribute to certain neurological conditions. I applied this technique to a brain connectome dataset for feature detection in the classification of Alzheimer's Disease (AD) patients. Preliminary results showed significant improvements when interactively selected features are used.

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Un graphe est un objet mathématique modélisant des relations sur un ensemble d'éléments. Il est utilisé dans de nombreux domaines à des fins de modélisation. La taille et la complexité des graphes manipulés de nos jours entraînentdes besoins de visualisation afin de mieux les analyser. Dans cette thèse, nous présentons différents travaux en visualisation interactive de graphes qui s'attachent à exploiter les architectures de calcul parallèle (CPU et GPU) disponibles sur les stations de travail contemporaines. Un premier ensemble de travaux s'intéresse à des problématiques de dessin de graphes. Dessiner un graphe consiste à le plonger visuellement dans un plan ou un espace. La première contribution dans cette thématique est un algorithmede regroupement d'arêtes en faisceaux appelé Winding Roads.Cet algorithme intuitif, facilement implémentable et parallélisable permet de réduireconsidérablement les problèmes d'occlusion dans un dessin de graphedus aux nombreux croisements d'arêtes.La seconde contribution est une méthode permettant dedessiner un réseau métabolique complet. Ce type deréseau modélise l'ensemble des réactions biochimiquesse produisant dans les cellules d'un organise vivant.L'avantage de la méthode est de prendre en compte la décompositiondu réseau en sous-ensembles fonctionnels ainsi que de respecterles conventions de dessin biologique.Un second ensemble de travaux porte sur des techniques d'infographiepour la visualisation interactive de graphes. La première contribution dans cette thématique est une technique de rendude courbes paramétriques exploitant pleinement le processeur graphique. La seconde contribution est une méthodede rendu nommée Edge splatting permettant de visualiserla densité des faisceaux d'arêtes dans un dessin de grapheavec regroupement d'arêtes. La dernière contribution portesur des techniques permettant de mettre en évidence des sous-graphesd'intérêt dans le contexte global d'une visualisation de graphes. / A graph is a mathematical object used to model relations over a set of elements.It is used in numerous fields for modeling purposes. The size and complexityof graphs manipulated today call a need for visualization to better analyze them.In that thesis, we introducedifferent works in interactive graph visualisation which aim at exploiting parallel computing architectures (CPU and GPU) available on contemporary workstations.A first set of works focuses on graph drawing problems.Drawing a graph consists of embedding him in a plane or a space.The first contribution in that theme is an edge bundling algorithmnamed Winding Roads. That intuitive, easyly implementable and parallelizable algorithmallows to considerably reduce clutter due to numerous edge crossings in a graph drawing.The second contribution is a method to draw a complete metabolicnetwork. That kind of network models the whole set of biochemical reactionsoccurring within cells of a living organism. The advantage of the methodis to take into account the decomposition of the network into functionnal subsetsbut also to respect biological drawing conventions.A second set of works focuses on computer graphics techniquesfor interactive graph visualisation. The first contributionin that theme is a technique for rendering parametric curvesthat fully exploits the graphical processor unit. The second contributionis a rendering technique named Edge splatting that allowsto visualize the bundles densities in an edge bundled layout. Thelast contribution introduces some techniques for emphasizingsub-graphs of interest in the global context of a graph visualization.

La quantité d’informations stockée dans les bases de données est en constante augmentation rendant ainsi nécessaire la mise au point de systémes d’analyse et de visualisation. Nous nous intéressons dans cette thèse aux données relationnelles et plus particulièrement aux données biologiques. Cette thèse s’oriente autour de trois axes principaux : tout d’abord, la décomposition de graphes en groupes d’éléments ”similaires” a?n de détecter d’éventuelles structures de communauté ; le deuxième aspect consiste à mettre en évidence ces structures dans un système de visualisation, et dans un dernier temps, nous nous intéressons à l’utilisabilité de l’un de ces systèmes de visualisation via une évaluation expérimentale. Les travaux de cette thèse ont été appliqués sur des données réelles provenant de deux domaines de la biologie : les réseaux métaboliques et les réseaux d’interactions génes- protéines. / The amount of information stored in databases is constantly increasing making necessary to develop systems for analysis and visualization. In this thesis, we are interested in relational data and in particular, in biological data. This thesis focuses on three main axes : ?rstly, the decomposition of graph into clusters of ”similar” elements in order to detect the community structures ; the second aspect is to highlight these structures in a visualization system; and thirdly, we are interested in the usability of one of these visualization systems through an experimental evaluation. The work presented in this thesis was applied on real data from two ?elds of biology : the metabolic networks and the gene-protein interaction networks.

Graphs have been successfully employed in avariety of problems and applications, being the object of study in modeling, analysis and construction of visual representations. While different approaches exist for graph visualization,most of them suffer from the severe clutter when the number of nodes or edges is large. Among the approaches that handle such problem, edge bundling techniques attained relative success on improving the quality of the visual representations by bending and aggregating edges in order to produce an organized layout. Despite this success, most of the exiting techniques create edge bundles based only on the visual space information, that is, there is no explicit connection between the edge bundling layout and the original data. There fore, these techniques generates less meaningful bundles and may lead users to misinterpret the data. This masters research presents a novel edge bundling technique based on the similarity relationships among vertices. We developed such technique based on two assumptions. First, it supports the hypothesis that edge bundling can better represent the data when there is an inherent connection between the proximity among the elements in the information space and the proximity between edges in the edge bundling layout. We address this question by presenting a similarity bundling framework, that considers the similarity between vertices when performing the edges bending. To guide the bundling, we create a similarity hierarchy, called backbone. This is based on a multilevel partition of the data, which groups edges of similar vertices. Second, we also support that a multiscale representation improves the visual and complexity scalability of bundling layouts. We present a multiscale edge bundling, which allows an overview plus detailed exploration, coarsening or revealing the bundling at different levelsof the same visualization. Our evaluation framework shows that our backbone produces a balanced hierarchy with a good representation of similarity relationships among vertices. Moreover, the edge bundling layout guided by the backbone reduces the visual clutter and surpass state-of-the-art techniques in displaying global and local edge patterns. / Grafos são empregados com sucesso em uma grande variedade de problemas e aplicações, sendo objeto de estudo na modelagem, análise e na construção de representações visuais. Embora existam diferentes formas para a visualização de grafos, a maioria delas sofrem pela desorganização do espaço visual quando o número de vértices ou arestas é alto. Entre as abordagens que lidam com este problema, as técnicas de agrupamentos visuais de arestas obtiveram sucesso na melhora da representação visual pelo encurvamento e agrupamento de arestas que aperfeiçoam a organização da representação. Apesar deste sucesso, a maioria das técniques criam grupos de arestas baseados apenas na informação do espaço visual, não existindo conexão explícita entre o desenho no espaço visual e o conjunto de dados original. Dessa forma, estas técnicas produzem agrupamentos de arestas com baixa significância e podem levar o usuário a uma interpretação incorreta da informação. Esta pesquisa de mestrado apresenta uma nova técnica de agrupamento visual de arestas baseado nas relações de similaridade entre os vértices. Nós desenvolvemos esta técnica com base em duas premissas. Primeiro, ela defende a hipótese que a representação por agrupamento de arestas pode representar melhor o conjunto de dados se existir uma conexão inerente entre a proximidade dos elementos no espaço de informação e a proximidade entre arestas no desenho de arestas agrupadas. Nós atendemos esta questão apresentando um arcabouço para o agrupamento de arestas baseado em similaridade, que considera a similaridade entre vértices para realizar o encurvamento das arestas. Para guiar este encurvamento, nós criamos uma estrutura de similaridade, denominada backbone. Esta estrutura é baseada em um particionamento multi-nível do conjunto de dados, que agrupa arestas de vértices similares. A segunda premissa, nós também defendemos que uma representação multiescala melhora a escalabilidade computacional e visual da representação visual de arestas agrupadas. Nós apresentamos um agrupamento visual multi-nível de arestas que permite uma exploração generalizada e detalhada, revelando detalhes em múltiplos níveis da visualização. Nosso processo de avaliação mostra que a construção do backbone produz uma hierarquia balanceada e com boa representação das relações de similaridade entre os vértices. Além disso, a visualização com arestas guiadas pelo backbone reduz a desordem visual e melhora as técnicas do estado-da-arte na identificação de padrões de arestas globais e locais.

Bancos de dados relacionais são fontes de dados rigidamente estruturadas, caracterizadas por relacionamentos complexos entre um conjunto de relações (tabelas). Entender tais relacionamentos é um desafio, porque os usuários precisam considerar múltiplas relações, entender restrições de integridade, interpretar vários atributos, e construir consultas SQL para cada tentativa de exploração. Neste cenário, introduz-se uma metodologia em duas etapas; primeiro utiliza-se um grafo organizado como uma estrutura hierárquica para modelar os relacionamentos do banco de dados, e então, propõe-se uma nova técnica de visualização para exploração relacional. Os resultados demonstram que a proposta torna a exploração de bases de dados significativamente simplificada, pois o usuário pode navegar visualmente pelos dados com pouco ou nenhum conhecimento sobre a estrutura subjacente. Além disso, a navegação visual de dados remove a necessidade de consultas SQL, e de toda complexidade que elas requerem. Acredita-se que esta abordagem possa trazer um paradigma inovador no que tange à compreensão de dados relacionais / Relational databases are rigid-structured data sources characterized by complex relationships among a set of relations (tables). Making sense of such relationships is a challenging problem because users must consider multiple relations, understand their ensemble of integrity constraints, interpret dozens of attributes, and draw complex SQL queries for each desired data exploration. In this scenario, we introduce a twofold methodology; we use a hierarchical graph representation to efficiently model the database relationships and, on top of it, we designed a visualization technique for rapidly relational exploration. Our results demonstrate that the exploration of databases is deeply simplified as the user is able to visually browse the data with little or no knowledge about its structure, dismissing the need of complex SQL queries. We believe our findings will bring a novel paradigm in what concerns relational data comprehension.

<p>Vizz3D is a graph visualization tool developed at Växjö University. It is used to visualize different aspects of software systems in 3D, based on the static analysis of source code. It can optionally use Java3D or OpenGL as a graphics library.</p><p>In order to visualize huge 3D structures performance is very important. This comes from the fact that the structures must be redrawn with no delay when a user interacts with the system. If there were a delay the user would loose the cognitive orientation because his interaction and the feedback would not fit. Vizz3D was not capable to run huge visualizations fast enough, and therefore careful optimisation was essential. Additionally, the Vizz3D tool is just at the beginning of its software life cycle.</p><p>For optimisation, JOGL (Java Bindings for OpenGL) was chosen. The extension with a JOGL version was necessary since the GL4Java (OpenGL for Java) wrapper used for the implementation of Vizz3D is no longer supported. JOGL was therefore needed for assuring future maintainability.</p><p>The JOGL version of Vizz3D was optimised to be able to visualize huge graphs with acceptable performance. To determine what areas of Vizz3D that consumed most of its resources, the process of profiling were used. The system performance was improved according to several aspects: Computational performance, Scalability, Perceived performance, RAM footprint and Start-up time. The results were then evaluated by using benchmarking techniques. After optimisation, the performance of Vizz3D was improved a lot which led to that huge graphs now could be visualized with acceptable performance.</p>

This thesis describes our experience of creating an interactive and customizable graph visualization engine, referred to as SVG graph visualization engine, or SVG Editor for short, implemented entirely in SVG and ECMAScript. Typical operations of this visualization engine include filtering, searching, collapsing, expanding of graph elements, and hierarchical as well as historical viewing of graphs and subgraphs. This visualization engine has great potential because it runs on many platforms and in many applications due to the availability of SVG plug-ins. SVG Editor can be customized to fit various domains. To illustrate the editor's customizability, we discuss three sample applications of SVG Editor. The first example is the instantiation of SVG Editor for the visualization of software structures. This kind of visualization is used by reverse engineering tools to support program comprehension. The second example is the instantiation of SVG Editor for information modeling with the Eclipse Modeling Framework (EMF). Another example is to use SVG Editor to show three distinct viewpoints of a subject Web site: developer-view, server-view, and client-view in a web site reverse engineering tool, and for a reverse engineer to explore and navigate mappings between them. Also, we assess selected aspects of SVG Editor’s scalability, extensibility, customizability, usability, and reusability.