Multidimensional datasetsare increasingly more prominent and important in data science and many application domains. Such datasets typically consist of a large set of observations, or data points, each which is described by several measurements, or dimensions. During the design of techniques and tools to process such datasets, a key component is to gather insights into their structure and patterns, a goal which is targeted by multidimensional visualization methods. Structures and patterns of high-dimensional data can be described, at a core level, by the notion of similarity of observations. Hence, to visualize such patterns, we need effective and efficient ways to depict similarity relations between a large number of observations, each having a potentially large number of dimensions. Within the realm of multidimensional visualization methods, two classes of techniques exist projections and similarity trees which effectively capture similarity patterns and also scale well to the number of observations and dimensions of the data. However, while such techniques show similarity patterns, understanding and interpreting these patterns in terms of the original data dimensions is still hard. This thesis addresses the development of visual explanatory techniques for the easy interpretation of similarity patterns present in multidimensional projections and similarity trees, by several contributions. First, we proposemethodsthat make the computation of similarity treesefficient for large datasets, and also allow their visual explanation on a multiscale, or several levels of detail. We also propose ways to construct simplified representations of similarity trees, thereby extending their visual scalability even further. Secondly, we propose methods for the visual explanation of multidimensional projections in terms of automatically detected groups of related observations which are also automatically annotated in terms of their similarity in the high-dimensional data space. We show next how these explanatory mechanismscan be adapted to handle both static and time-dependent multidimensional datasets. Our proposed techniques are designed to be easy to use, work nearly automatically, handle any typesof quantitativemultidimensional datasets and multidimensional projection techniques, and are demonstrated on a variety of real-world large datasets obtained from image collections, text archives, scientific measurements, and software engineeering. / Conjuntos de dados multidimensionais são cada vez mais proeminentes e importantes em data science e muitos domínios de aplicação. Esses conjuntos de dados são tipicamente constituídos de um grande número de observações, ou objetos, cada qual descrito por várias medidas, ou dimensões. Durante o projeto de técnicas e ferramentas para processar tais dados, um dos focos principais é prover meios para análise e levantamento de hipóteses a partir das principais estruturas e padrões. Esse objetivo é perseguido por métodos de visualização multidimensional. Estruturas e padrões em dados multidimensionais podem ser descritos, em linhas gerais, pela noção de similaridade das observações. Portanto, para visualizar esses padrões, precisamos de meios efetivos e eficientes para retratar relações de similaridade dentre um grande número de observações, que potencialmente possuem um grande número de dimensões cada. No contexto dos métodos de visualização multidimensional, existem duas categorias de técnicas projeções e árvores de similaridade que efetivamente capturam padrões de similaridade e oferecem boa escalabilidade, tanto para o número de observações e quanto de dimensões. No entanto, embora essas técnicas exibam padrões de similaridade, o entendimento e interpretação desses padrões, em termos das dimensões originais dos dados, ainda é difícil. O trabalho desenvolvido nessa tese visa o desenvolvimento de técnicas explicativas para a fácil interpretação de padrões de similaridade presentes em projeções multidimensionais e árvores de similaridade. Primeiro, propomos métodos que possibilitam a computação eficiente de árvores de similaridade para grandes conjuntos de dados, e também a sua explicação visual em multiescala, ou seja, em vários níveis de detalhe. Também propomos modos de construir representações simplificadas de árvores de similaridade, e desse modo estender ainda mais a sua escalabilidade visual. Segundo, propomos métodos para explicar visualmente projeções multidimensionais em termos de grupos de observações relacionadas, detectadas e anotadas automaticamente para explicitar aspectos de sua similaridade no espaço de alta dimensionalidade. Mostramos em seguida como esses mecanismos explicativos podem ser adaptados para lidar com dados de natureza estática e dependentes no tempo. Nossas técnicas sã construídas visando fácil utilização, funcionamento semi automático, aplicação em quaisquer tipos de dados multidimensionais quantitativos e quaisquer técnicas de projeção multidimensional. Demonstramos a sua utilização em uma variedade de conjuntos de dados reais, obtidos a partir de coleções de imagens, arquivos textuais, medições científicas e de engenharia de software.

Phenology studies recurrent periodic phenomena of plants and their relationship to environmental conditions. Monitoring forest ecosystems using digital cameras allows the study of several phenological events, such as leaf expansion or leaf fall. Since phenological phenomena are cyclic, the comparative analysis of successive years is capable of identifying interesting variation on annual patterns. However, the number of images collected rapidly gets significant since the goal is to compare data from several years. Instead of performing the analysis over images, experts prefer to use derived statistics (such as average values). We propose PhenoVis, a visual analytics tool that provides insightful ways to analyze phenological data. The main idea behind PhenoVis is the Chronological Percentage Maps (CPMs), a visual mapping that offers a summary view of one year of phenological data. CPMs are highly customizable, encoding more information about the images using a pre-defined histogram, a mapping function that translates histogram values into colors, and a normalized stacked bar chart to display the results. PhenoVis supports different color encodings, visual pattern analysis over CPMs, and similarity searches that rank vegetation patterns found at various time periods. Results for datasets comprising data of up to nine consecutive years show that PhenoVis is capable of finding relevant phenological patterns along time. Fenologia estuda os fenômenos recorrentes e periódicos que ocorrem com as plantas. Estes podem vir a ser relacionados com as condições ambientais. O monitoramento de florestas, através de câmeras, permite o estudo de eventos fenológicos como o crescimento e queda de folhas. Uma vez que os fenômenos fenológicos são cíclicos, análises comparativas de anos sucessivos podem identificar variações interessantes no comportamento destes. No entanto, o número de imagens cresce rapidamente para que sejam comparadas lado a lado. PhenoVis é uma ferramenta para análise visual que apresenta formas para analisar dados fenológicos através de comparações estatísticas (preferência dos especialistas) derivadas dos valores dos pixels destas imagens. A principal ideia por trás de PhenoVis são os mapas percentuais cronológicos (CPMs), um mapeamento visual com uma visão resumida de um período de um ano de dados fenológicos. CPMs são personalizáveis e conseguem representar mais informações sobre as imagens do que um gráfico de linha comum. Isto é possível pois o processo envolve o uso de histogramas pré-definidos, um mapeamento que transforma valores em cores e um empilhamento dos mapas de percentagem que visa a criação da CPM. PhenoVis suporta diferentes codificações de cores e análises de padrão visual sobre as CPMs. Pesquisas de similaridade ranqueiam padrões parecidos encontrados nos diferentes anos. Dados de até nove anos consecutivos mostram que PhenoVis é capaz de encontrar padrões fenológicos relevantes ao longo do tempo.

Visual analytics tools play an important role in the scenario of big data solutions, combining data analysis and interactive visualization techniques in effective ways to support the incremental exploration of large data collections from a wide range of domains. One particular challenge for visual analytics is the analysis of multidimensional datasets, which consist of many observations, each being described by a large number of dimensions, or attributes. Finding and understanding data-related patterns present in such spaces, such as trends, correlations, groups of related observations, and outliers, is hard. Dimensionality reduction methods, or projections, can be used to construct low (two or three) dimensional representations of high-dimensional datasets. The resulting representation can then be used as a proxy for the visual interpretation of the high-dimensional space to efficiently and effectively support the above-mentioned data analysis tasks. Projections have important advantages over other visualization techniques for multidimensional data, such as visual scalability, high degree of robustness to noise and low computational complexity. However, a major obstacle to the effective practical usage of projections relates to their difficult interpretation. Two main types of interpretation challenges for projections are studied in this thesis. First, while projection techniques aim to preserve the so-called structure of the original dataset in the final produced layout, and effectively achieve the proxy effect mentioned earlier, they may introduce a certain amount of errors that influence the interpretation of their results. However, it is hard to convey to users where such errors occur in the projection, how large they are, and which specific data-interpretation aspects they affect. Secondly, interpreting the visual patterns that appear in the projection space is far from trivial, beyond the projections ability to show groups of similar observations. In particular, it is hard to explain these patterns in terms of the meaning of the original data dimensions. In this thesis we focus on the design and development of novel visual explanatory techniques to address the two interpretation challenges of multidimensional projections outlined above. We propose several methods to quantify, classify, and visually represent several types of projection errors, and how their explicit depiction helps interpreting data patterns. Next we show how projections can be visually explained in terms of the highdimensional data attributes, both in a global and a local way. Our proposals are designed to be easily added, and used with, any projection technique, and in any application context using such techniques. Their added value is demonstrated by presenting several exploration scenarios involving various types of multidimensional datasets, ranging from measurements, scientific simulations, software quality metrics, software system structure, and networks. / Ferramentas de análise visual desempenham um papel importante no cenário de soluções para grandes volumes de dados (big data), combinando análise de dados e técnicas interativas de visualização de forma eficaz para apoiar a exploração incremental de coleções de dados em diversos domínios. Um desafio importante em análise visual é a exploração de conjuntos de dados multidimensionais, que consistem em muitas observações, sendo cada uma descrita por um grande número de dimensões, ou atributos. Encontrar e compreender os padrões presentes em tais espaços, tais como tendências, correlações, grupos de observações relacionadas e valores extremos, é difícil. Técnicas de redução de dimensionalidade ou projeções são utilizadas para construir, a partir de conjuntos de dados multidimensionais, representações de duas ou três dimensões que podem então ser utilizadas com substitutas do espaço original para sua interpretação visual, apoiando de forma eficiente as tarefas de análise de dados acima mencionadas. Projeções apresentam vantagens importantes sobre outras técnicas de visualização para dados multidimensionais, tais como escalabilidade visual, resistência a ruídos e baixa complexidade computacional. No entanto, um grande obstáculo para o uso prático de projeções vem da sua difícil interpretação. Dois principais tipos de desafios de interpretação de projeções são estudados nesta tese. Em primeiro lugar, mesmo que as técnicas de projeção tenham como objetivo preservar, na representação final, a estrutura do conjunto de dados original, elas podem introduzir uma certa quantidade de erros que influenciam a interpretação dos seus resultados. No entanto, é difícil transmitir aos usuários onde tais erros ocorrem na projeção, quão severos eles são e que aspectos específicos da interpretação dos dados eles afetam. Em segundo lugar, interpretar os padrões visuais que aparecem em uma projeção, além da percepção de grupos de observações semelhantes, está longe de ser trivial. Em particular, é difícil explicar tais padrões em termos do significado das dimensões dos dados originais. O trabalho desenvolvido nesta tese concentra-se no projeto e desenvolvimento de novas técnicas visuais explicativas para lidar com os dois desafios de interpretação de projeções multidimensionais descritos acima. São propostos alguns métodos para quantificar, classificar e representar visualmente diversos tipos de erros de projeção, e é descrito como essas representações explícitas ajudam na interpretação dos padrões dos dados. Além disso, também são propostas técnicas visuais para explicar projeções em termos dos atributos dos dados multidimensionais, tanto de forma global quanto local. As propostas apresentadas foram concebidas para serem facilmente incorporadas e usadas com qualquer técnica de projeção e em qualquer contexto de aplicação. As contribuições são demonstradas pela apresentação de vários cenários de exploração, envolvendo vários tipos de conjuntos de dados multidimensionais, desde medições e simulações científicas até métricas de qualidade de software, estruturas de sistema de software e redes.

The thesis deals with problematics of tools marked as Self Service Business Intelligence (SSBI) which is paid close attention by its users as well as producers due to expected progressive growth of this trend. Two products are evaluated: SAS Visual Analytics and IBM Cognos Analytics, both tools are evaluated by their extent of user´s autonomy and support of data visualization. Basic principles and concepts connected to the area of business intelligence are defined in theoretical part of the thesis. The problematics of data visualization is introduced and trend of self-service business intelligence is discussed with analyzing its benefits as well as restrictions. Technological factors which helped to fast development of SSBI are mentioned as well. A methodology has been created within practical part of the thesis. It is enforceable for any other tool from the family of SSBI and it is possible to assess the extent of support within observed categories through this methodology: Using information, Creation of information, Creation of sources and Visualization. A set of criteria on basis of which the evaluator can create evaluation form corresponding to his/her needs was defined for each of the stated categories. Usability of the methodology is illustratively demonstrated at mutual comparison of chosen modules of both tools while those parts are sufficiently described before the evaluation itself.

L'expansion du World Wide Web et la multiplication des sources de données (capteurs, services Web, programmes scientifiques, outils d'analyse, etc.) ont conduit à la prolifération de données hétérogènes et complexes. La phase d'extraction de connaissance et de recherche de corrélation devient ainsi de plus en plus difficile.Typiquement, une telle analyse est effectuée en utilisant les outils logiciels qui combinent: des techniques de visualisation, permettant aux utilisateurs d'avoir une meilleure compréhension des données, et des programmes d'analyse qui effectuent des opérations d'analyses complexes et longues.La visualisation analytique (visual analytics) vise à combiner la visualisation des donnéesavec des tâches d'analyse et de fouille. Etant donnée la complexité et la volumétrie importante des données scientifiques (par exemple, les données associées à des processus biologiques ou physiques, données des réseaux sociaux, etc.), la visualisation analytique est appelée à jouer un rôle important dans la gestion des données scientifiques.La plupart des plateformes de visualisation analytique actuelles utilisent des mécanismes en mémoire centrale pour le stockage et le traitement des données, ce qui limite le volume de données traitées. En outre, l'intégration de nouveaux algorithmes dans le processus de traitement nécessite du code d'intégration ad-hoc. Enfin, les plate-formes de visualisation actuelles ne permettent pas de définir et de déployer des processus structurés, où les utilisateurs partagent les données et, éventuellement, les visualisations.Ce travail, à la confluence des domaines de la visualisation analytique interactive et des bases de données, apporte deux contributions. (i) Nous proposons une architecture générique pour déployer une plate-forme de visualisation analytique au-dessus d'un système de gestion de bases de données (SGBD). (ii) Nous montrons comment propager les changements des données dans le SGBD, au travers des processus et des visualisations qui en font partie. Notre approche permet à l'application de visualisation analytique de profiter du stockage robuste et du déploiement automatique de processus à partir d'une spécification déclarative, supportés par le SGBD.Notre approche a été implantée dans un prototype appelé EdiFlow, et validée à travers plusieurs applications. Elle pourrait aussi s'intégrer dans une plate-forme de workflow scientifique à usage intensif de données, afin d'en augmenter les fonctionnalités de visualisation. / The increasing amounts of electronic data of all forms, produced by humans (e.g. Web pages, structured content such as Wikipedia or the blogosphere etc.) and/or automatic tools (loggers, sensors, Web services, scientific programs or analysis tools etc.) leads to a situation of unprecedented potential for extracting new knowledge, finding new correlations, or simply making sense of the data.Visual analytics aims at combining interactive data visualization with data analysis tasks. Given the explosion in volume and complexity of scientific data, e.g., associated to biological or physical processes or social networks, visual analytics is called to play an important role in scientific data management.Most visual analytics platforms, however, are memory-based, and are therefore limited in the volume of data handled. Moreover, the integration of each new algorithm (e.g. for clustering) requires integrating it by hand into the platform. Finally, they lack the capability to define and deploy well-structured processes where users with different roles interact in a coordinated way sharing the same data and possibly the same visualizations.This work is at the convergence of three research areas: information visualization, database query processing and optimization, and workflow modeling. It provides two main contributions: (i) We propose a generic architecture for deploying a visual analytics platform on top of a database management system (DBMS) (ii) We show how to propagate data changes to the DBMS and visualizations, through the workflow process. Our approach has been implemented in a prototype called EdiFlow, and validated through several applications. It clearly demonstrates that visual analytics applications can benefit from robust storage and automatic process deployment provided by the DBMS while obtaining good performance and thus it provides scalability.Conversely, it could also be integrated into a data-intensive scientific workflow platform in order to increase its visualization features.

This thesis project set out to find and implement a comfortable way to explore vast, multidimensional datasets using interactive multiscale visualizations to combat the ever-growing information overload that the digitized world is generating. Starting at the realization that even for people not working in the fields of information visualization and data science the size of interesting datasets often outgrows the capabilities of standard spreadsheet applications such as Microsoft Excel. This project established requirements for a system to overcome this problem. In this thesis report, we describe existing solutions, related work, and in the end designs and implementation of a working tool for initial data exploration that utilizes novel multiscale visualizations to make complex coherences comprehensible and has proven successful in a practical evaluation with two case studies.

Social media channels such as Twitter and Facebook often contribute to disseminate initiatives that seek to inform and empower citizens concerned with government actions. On the other hand, certain actions and statements by governmental institutions, or parliament members and political journalists that appear on the conventional media tend to reverberate on the social media. This scenario produces a lot of textual data that can reveal relevant information on governmental actions and policies. Nonetheless, the target audience still lacks appropriate tools capable of supporting the acquisition, correlation and interpretation of potentially useful information embedded in such text sources. In this scenario, this work presents two system for the analysis of government and social media data. One of the systems introduces a new visualization, based on the river metaphor, for the analysis of the temporal evolution of topics in Twitter in connection with political debates. For this purpose, the problem was initially modeled as a clustering problem and a domain-independent text segmentation method was adapted to associate (by clustering) Twitter content with parliamentary speeches. Moreover, a version of the MONIC framework for cluster transition detection was employed to track the temporal evolution of debates (or clusters) and to produce a set of time-stamped clusters. The other system, named ATR-Vis, combines visualization techniques with active retrieval strategies to involve the user in the retrieval of Twitters posts related to political debates and associate them to the specific debate they refer to. The framework proposed introduces four active retrieval strategies that make use of the Twitters structural information increasing retrieval accuracy while minimizing user involvement by keeping the number of labeling requests to a minimum. Evaluations through use cases and quantitative experiments, as well as qualitative analysis conducted with three domain experts, illustrates the effectiveness of ATR-Vis in the retrieval of relevant tweets. For the evaluation, two Twitter datasets were collected, related to parliamentary debates being held in Brazil and Canada, and a dataset comprising a set of top news stories that received great media attention at the time. / Mídias sociais como o Twitter e o Facebook atuam, em diversas situações, como canais de iniciativas que buscam ampliar as ações de cidadania. Por outro lado, certas ações e manifestações na mídia convencional por parte de instituições governamentais, ou de jornalistas e políticos como deputados e senadores, tendem a repercutir nas mídias sociais. Como resultado, gerase uma enorme quantidade de dados em formato textual que podem ser muito informativos sobre ações e políticas governamentais. No entanto, o público-alvo continua carente de boas ferramentas que ajudem a levantar, correlacionar e interpretar as informações potencialmente úteis associadas a esses textos. Neste contexto, este trabalho apresenta dois sistemas orientados à análise de dados governamentais e de mídias sociais. Um dos sistemas introduz uma nova visualização, baseada na metáfora do rio, para análise temporal da evolução de tópicos no Twitter em conexão com debates políticos. Para tanto, o problema foi inicialmente modelado como um problema de clusterização e um método de segmentação de texto independente de domínio foi adaptado para associar (por clusterização) tweets com discursos parlamentares. Uma versão do algorimo MONIC para detecção de transições entre agrupamentos foi empregada para rastrear a evolução temporal de debates (ou agrupamentos) e produzir um conjunto de agrupamentos com informação de tempo. O outro sistema, chamado ATR-Vis, combina técnicas de visualização com estratégias de recuperação ativa para envolver o usuário na recuperação de tweets relacionados a debates políticos e associa-os ao debate correspondente. O arcabouço proposto introduz quatro estratégias de recuperação ativa que utilizam informação estrutural do Twitter melhorando a acurácia do processo de recuperação e simultaneamente minimizando o número de pedidos de rotulação apresentados ao usuário. Avaliações por meio de casos de uso e experimentos quantitativos, assim como uma análise qualitativa conduzida com três especialistas ilustram a efetividade do ATR-Vis na recuperação de tweets relevantes. Para a avaliação, foram coletados dois conjuntos de tweets relacionados a debates parlamentares ocorridos no Brasil e no Canadá, e outro formado por um conjunto de notícias que receberam grande atenção da mídia no período da coleta.

En biologie structurale, l’étude théorique de structures moléculaires comporte quatre activités principales organisées selon le processus séquentiel suivant : la collecte de données expérimentales/théoriques, la visualisation des structures 3d, la simulation moléculaire, l’analyse et l’interprétation des résultats. Cet enchaînement permet à l’expert d’élaborer de nouvelles hypothèses, de les vérifier de manière expérimentale et de produire de nouvelles données comme point de départ d’un nouveau processus.L’explosion de la quantité de données à manipuler au sein de cette boucle pose désormais deux problèmes. Premièrement, les ressources et le temps relatifs aux tâches de transfert et de conversion de données entre chacune de ces activités augmentent considérablement. Deuxièmement, la complexité des données moléculaires générées par les nouvelles méthodologies expérimentales accroît fortement la difficulté pour correctement percevoir, visualiser et analyser ces données.Les environnements immersifs sont souvent proposés pour aborder le problème de la quantité et de la complexité croissante des phénomènes modélisés, en particulier durant l’activité de visualisation. En effet, la Réalité Virtuelle offre entre autre une perception stéréoscopique de haute qualité utile à une meilleure compréhension de données moléculaires intrinsèquement tridimensionnelles. Elle permet également d’afficher une quantité d’information importante grâce aux grandes surfaces d’affichage, mais aussi de compléter la sensation d’immersion par d’autres canaux sensorimoteurs.Cependant, deux facteurs majeurs freinent l’usage de la Réalité Virtuelle dans le domaine de la biologie structurale. D’une part, même s’il existe une littérature fournie sur la navigation dans les scènes virtuelles réalistes et écologiques, celle-ci est très peu étudiée sur la navigation sur des données scientifiques abstraites. La compréhension de phénomènes 3d complexes est pourtant particulièrement conditionnée par la capacité du sujet à se repérer dans l’espace. Le premier objectif de ce travail de doctorat a donc été de proposer des paradigmes navigation 3d adaptés aux structures moléculaires complexes. D’autre part, le contexte interactif des environnements immersif favorise l’interaction directe avec les objets d’intérêt. Or les activités de collecte et d’analyse des résultats supposent un contexte de travail en "ligne de commande" ou basé sur des scripts spécifiques aux outils d’analyse. Il en résulte que l’usage de la Réalité Virtuelle se limite souvent à l’activité d’exploration et de visualisation des structures moléculaires. C’est pourquoi le second objectif de thèse est de rapprocher ces différentes activités, jusqu’alors réalisées dans des contextes interactifs et applicatifs indépendants, au sein d’un contexte interactif homogène et unique. Outre le fait de minimiser le temps passé dans la gestion des données entre les différents contextes de travail, il s’agit également de présenter de manière conjointe et simultanée les structures moléculaires et leurs analyses et de permettre leur manipulation par des interactions directes.Notre contribution répond à ces objectifs en s’appuyant sur une approche guidée à la fois par le contenu et la tâche. Des paradigmes de navigation ont été conçus en tenant compte du contenu moléculaire, en particulier des propriétés géométriques, et des tâches de l’expert, afin de faciliter le repérage spatial et de rendre plus performante l’activité d’exploration. Par ailleurs, formaliser la nature des données moléculaires, leurs analyses et leurs représentations visuelles, permettent notamment de proposer à la demande et interactivement des analyses adaptées à la nature des données et de créer des liens entre les composants moléculaires et les analyses associées. Ces fonctionnalités passent par la construction d’une représentation sémantique unifiée et performante rendant possible l’intégration de ces activités dans un contexte interactif unique. / In structural biology, the theoretical study of molecular structures has four main activities organized in the following scenario: collection of experimental and theoretical data, visualization of 3D structures, molecular simulation, analysis and interpretation of results. This pipeline allows the expert to develop new hypotheses, to verify them experimentally and to produce new data as a starting point for a new scenario.The explosion in the amount of data to handle in this loop has two problems. Firstly, the resources and time dedicated to the tasks of transfer and conversion of data between each of these four activities increases significantly. Secondly, the complexity of molecular data generated by new experimental methodologies greatly increases the difficulty to properly collect, visualize and analyze the data.Immersive environments are often proposed to address the quantity and the increasing complexity of the modeled phenomena, especially during the viewing activity. Indeed, virtual reality offers a high quality stereoscopic perception, useful for a better understanding of inherently three-dimensional molecular data. It also displays a large amount of information thanks to the large display surfaces, but also to complete the immersive feeling with other sensorimotor channels (3D audio, haptic feedbacks,...).However, two major factors hindering the use of virtual reality in the field of structural biology. On one hand, although there are literature on navigation and environmental realistic virtual scenes, navigating abstract science is still very little studied. The understanding of complex 3D phenomena is however particularly conditioned by the subject’s ability to identify themselves in a complex 3D phenomenon. The first objective of this thesis work is then to propose 3D navigation paradigms adapted to the molecular structures of increasing complexity. On the other hand, the interactive context of immersive environments encourages direct interaction with the objects of interest. But the activities of: results collection, simulation and analysis, assume a working environment based on command-line inputs or through specific scripts associated to the tools. Usually, the use of virtual reality is therefore restricted to molecular structures exploration and visualization. The second thesis objective is then to bring all these activities, previously carried out in independent and interactive application contexts, within a homogeneous and unique interactive context. In addition to minimizing the time spent in data management between different work contexts, the aim is also to present, in a joint and simultaneous way, molecular structures and analyses, and allow their manipulation through direct interaction.Our contribution meets these objectives by building on an approach guided by both the content and the task. More precisely, navigation paradigms have been designed taking into account the molecular content, especially geometric properties, and tasks of the expert, to facilitate spatial referencing in molecular complexes and make the exploration of these structures more efficient. In addition, formalizing the nature of molecular data, their analysis and their visual representations, allows to interactively propose analyzes adapted to the nature of the data and create links between the molecular components and associated analyzes. These features go through the construction of a unified and powerful semantic representation making possible the integration of these activities in a unique interactive context.

Visual analytics (VA) solutions emerged in the past decade and tackled many problems in a variety of domains. The power of combining the abilities of human and machine creates fertile ground for new solutions to grow. However, the rise of these hybrid solutions complicates the process of evaluation. Unlike automated solutions, VA solutions behavior depends on the user who operates them. This creates a dimension of variability in measured performance. The existence of a human, on the other hand, allows researchers to borrow evaluation methods from domains, such as sociology. The challenge in these methods, however, lies in gathering and analyzing qualitative data to build valid evidence of usefulness.<div>This thesis tackles the challenge of evaluating the usefulness of VA solutions. We survey existing evaluation methods that have been used to assess VA solutions. We then analyze these methods in terms of validity and generalizability of their findings, as well as the feasibility of using them. Subsequently, we propose an evaluation framework which suggests evaluating VA solutions based on judgment analysis theory. The analysis provided by our framework is capable of quantitatively assessing the performance of a solution while providing a reason for the captured performance.<br></div><div>We have conducted multiple case studies in social spambot labeling domain to apply our theoretical discussion. We have developed a VA solution that tackles social spambot labeling problem, then use this solution to apply existing evaluation methods and showcase some of their limitations. Furthermore, we have used our solution to show the benefit yielded by our proposed evaluation framework.</div>

Spatial data is playing a bigger role within many fields outside of Geographic Information Systems (GIS) and spatial analysis. With more and more users with varying levels of previous spatial analysis experience using this kind of data, there is a growing demand on how this information is best presented to the user. This user-centered design is an increasingly common theme in other adjacent fields, but is still in its infancy in the field of GIS. Currently there is no obvious generalized solution that provides the answer to how to present data, no matter if it is spatial or not. How to present data in a smart and comprehensive way is still an everyday challenge across many fields. The objective of this thesis is to create a prototype of a web based Network Information System (NIS) where the layman user is in the center of the entire design process. This includes both the actual visualizations, but also the choice of tools and the interface design. The prototype is designed around the role of the customer service representative in a utility company using a NIS. This type of layman user is the kind of user that today works in a system that is designed with neither their role nor their GIT experience or training in mind. From this prototype, the efficacy of different visualization techniques on layman users is evaluated, producing more general guidelines for user-centered development directed at layman users. The first step of this user-centered design process is to understand the user. By interviewing users of the system, their current work flows and opinions of their current system are better understood. From this, information about which tools they need, which current features work well and which need revising is gathered. Based on this, a mock-up is created which is then transformed into a prototype. Finally, the prototype is evaluated by the target audience with comments on a presentation as well as a larger survey. The results show that in general the prototype is well-received with regards to existing functionality and how it is presented through the interface design. The implemented visualizations are well understood by most of the expert users, but are less successful with the layman users in the survey. Especially with regards to the icon choices and other point representations, there is a discrepancy between the intended visualization and the perception of the survey takers, which may partly be due to the lack of context presented. An appreciated fact is that the functionality implemented in the prototype is tailored to the requirements put forth by the users. User-centric design processes depend heavily on the developer's understanding of the user and their needs. This is as true for functionality as for visualizations, where familiarity and associations can be both beneficial and detrimental, depending on how well understood they are. Using icons to represent objects is very efficient, as long as the context and the meaning of the icons themselves are well defined and conveyed. Finally, it is imperative to not throw too much information at the user. Whether in the shape of too many tools and options, or by displaying too much on the map, the same clutter-problem occurs. When presenting a situation or a scenario, the core message cannot be obfuscated by unnecessary features, functions or choices.