The scenario-based design process, particularly with an emphasis on claims, captures and shares design knowledge that has promise in teaching design. However, the best methods are not fully understood yet. This thesis examines the areas of scenario-based design, claims relationships, and learning through cases with the goal of learning what can be improved to better support these processes. In investigating those areas, an information visualization tool is created for learning design through case studies. The tool combined claims from multiple visualization techniques to create an approach for supporting problem-based learning goals. In creating the tool, scenario-based design was used, with a particular emphasis on claims for driving the development and evaluation. All design rationale was captured in claims to create a tool that addressed the current problem issues identified. A recently developed claims relationship framework, used for the first time in development efforts, aided in structuring the design rationale. Using these relationships, the design was tracked from requirements analysis to problem claims to design claims and finally to claims that formed the basis of an evaluation. This thesis presents a case study of using claims relationships to drive the development and evaluation of a tool for supporting problem-based learning through case studies of scenario-based design. In creating a novel tool for learning about cases, a unique example of claims-centric scenario based design emerges, presenting as open questions new directions for merging scenarios, claims, and cases in knowledge storage, access, and learning systems. / Master of Science

Conselho Nacional de Desenvolvimento Científico e Tecnológico / The interpretation and understanding of large quantities of data is a challenge for current information visualization methods. The visualization of information is important as it makes the appropriate acquisition of the information through the visualization possible. The choice of the most appropriate information visualization method before commencing with the resolution of a given visual problem is primordial to obtaining an efficient solution. This article has as its objective to describe an information visualization classification approach based on Treemap, which is able to identify the best information visualization model for a given problem. The implementation of the model involves choosing components and also the architecture that stands its necessary functions effectively. This is understood through the construction of an adequate information visualization meta-model. Firstly, the actual state of the visualization field is described, and then the rules and criteria used in our research are shown, with the aim of presenting a meta-model proposal based on treemap visualization methods. Besides this, the author present a case study with the information contained in the periodic table visualization meta-model along with an analysis of the information search time complexity in each of the two meta-models. Finally, an evaluation of the results is presented through the experiments conducted with users and a comparative analysis of the methods based on Treemap and the Periodic Table. / Interpretar e entender uma grande quantidade de dados é um desafio dos modelos de Visualização de Informação. A Visualização da Informação é importante para possibilitar uma apropriada obtenção da informação através da visualização. A escolha do modelo de visualização de informação mais apropriado antes de iniciar a resolução de um determinado problema visual é primordial para obtenção de uma solução eficaz. A implementação do modelo envolve escolher também componentes e a arquitetura que suporte as suas funcionalidades necessárias, de maneira eficaz. Esta dissertação descreve uma abordagem de classificação das visualizações da informação baseado em treemap, capaz de buscar e identificar o melhor modelo de visualização da informação para um determinado problema. Isso compreende na criação de um metamodelo de Visualização da Informação adequado. Primeiramente, o atual estado do campo de visualização foi descrito e então, são mostradas as regras e critérios utilizados na pesquisa dessa dissertação, a fim de apresentar uma proposta de metamodelo baseado em treemap para modelos de visualização. Além disso, é apresentado um estudo de caso com as informações contidas no metamodelo de visualização da tabela periódica e a análise da complexidade de tempo da busca da informação em cada um dos dois metamodelos. Finalmente, uma avaliação dos resultados é apresentada, através de experimentos com usuários e análise comparativa entre os modelos baseados em treemap e na tabela periódica. / Mestre em Ciências

<p>In many command and control situations the understanding of dynamic events is crucial. With today’s development of hard- and software architecture, we have the possibility to visualize data in two-dimensional (2D) and three-dimensional (3D) images. The aim of this thesis is therefore to investigate different approaches to visualizing dynamic events. The visualization techniques investigated include 2D animation and time representations as markings on a 2D map. In 3D the visualization technique investigated is the “space time-cube” A further aim is to study whether the Cue Probability Learning (CPL) paradigm can be used to evaluate visualizations.</p><p>By mapping time onto a spatial dimension, in the 2D visualization as lines with different densities and in 3D as height over the map, a simultaneous visualization of space and time is possible. The findings are that this mapping of time onto space is beneficial to users as compared with animations, but the two mapping techniques are not interchangeable. If a task requires judgments of metric spatial properties, a 2D visualization is more beneficial; however, if the task only requires judgments of more qualitative aspects, a 3D visualization is more beneficial.</p><p>When we look at a 3D visualization, we utilize different sources of depth information. These sources are always present and each defines either a 3D scene or a projection surface. By using these different sources of depth information wisely, a visualization can be created that efficiently shows relevant information to a user while requiring a minimal amount of specialized hardware.</p><p>Finally, the CPL paradigm seems to be a worthwhile option as an experimental paradigm in visualization experiments. One of the advantages of CPL is that novice users can be trained to be task experts in a controlled and time-efficient way.</p>

Un grand nombre d'activités utilise des outils informatisés s'appuyant sur des représentations graphiques interactives. En tant que vecteurs de transmission d'informations, ces visualisations visent à optimiser la bande passante entre le visuel et l'utilisateur. Pour créer des visualisations efficaces, les concepteurs utilisent des méthodes basées sur leur expertise et sur des tests utilisateurs. Ces méthodes altèrent le processus de design : elles sont chronophages et ne reposent pas sur des fondements théoriques précis qui pourraient éviter des erreurs de conception en amont. Afin de mieux étudier les visualisations, nous proposons de les caractériser, c'est-à-dire d'en énumérer et d'en définir systématiquement les propriétés, tout en explicitant les phénomènes visuels qui engendrent la transmission d'informations. La thèse que nous soutenons consiste à dire que la caractérisation des visualisations est importante pour leurs conceptions, et que son opérationnalisation par des méthodes et des outils permet de concevoir de meilleurs systèmes interactifs. Ainsi, nous avons déterminé qu'une caractérisation est composée de deux éléments : son contenu et sa mise en forme. Nous montrons que le modèle de Data Flow, modèle usuel permettant la description de la construction de visualisations, ne permet pas de les caractériser. Nous proposons d'autres modèles qui permettent de caractériser l'aspect dynamique, les informations émergentes (et notamment celles issues des visualisations dites "écologiques"), ainsi que l'accumulation graphique. En utilisant des visualisations issues du milieu du transport aérien, nous montrons comment la caractérisation permet de comprendre leurs similitudes et leurs différences, de lever les ambiguïtés de communication entre concepteurs, et de concevoir, par exemple, une nouvelle visualisation schématique de routes aériennes. Enfin, nous avons réifié le processus de transformation d'un ensemble de données vers une visualisation sous la forme d'un logiciel d'exploration et de visualisation de grandes quantités de données multidimensionnelles : From Data To Display (FromDaDy). Ce logiciel s'appuie notamment sur l'exploration des configurations visuelles, et sur un ensemble d'interactions originales avec une implémentation performante. FromDady permet de configurer et de manipuler en temps réel des visualisations riches et originales, comme celles utilisant l'accumulation.

Acquérir et traiter des données est de moins en moins coûteux, à la fois en matériel et en temps, mais encore faut-il pouvoir les analyser et les interpréter malgré leur complexité. La dimensionnalité est un des aspects de cette complexité intrinsèque. Pour aider à interpréter et à appréhender ces données le recours à la visualisation est indispensable au cours du processus d’analyse. La projection représente les données sous forme d’un nuage de points 2D, indépendamment du nombre de dimensions. Cependant cette technique de visualisation souffre de distorsions dues à la réduction de dimension, ce qui pose des problèmes d’interprétation et de confiance. Peu d’études ont été consacrées à la considération de l’impact de ces artefacts, ainsi qu’à la façon dont des utilisateurs non-familiers de ces techniques peuvent analyser visuellement une projection. L’approche soutenue dans cette thèse repose sur la prise en compte interactive des artefacts, afin de permettre à des analystes de données ou des non-experts de réaliser de manière fiable les tâches d’analyse visuelle des projections. La visualisation interactive des proximités colore la projection en fonction des proximités d’origine par rapport à une donnée de référence dans l’espace des données. Cette technique permet interactivement de révéler les artefacts de projection pour aider à appréhender les détails de la structure sous-jacente aux données. Dans cette thèse, nous revisitons la conception de cette technique et présentons ses apports au travers de deux expérimentations contrôlées qui étudient l’impact des artefacts sur l’analyse visuelle des projections. Nous présentons également une étude de l’espace de conception d’une technique basée sur la métaphore de lentille et visant à s’affranchir localement des problématiques d’artefacts de projection. / The cost of data acquisition and processing has radically decreased in both material and time. But we also need to analyze and interpret the large amounts of complex data that are stored. Dimensionality is one aspect of their intrinsic complexity. Visualization is essential during the analysis process to help interpreting and understanding these data. Projection represents data as a 2D scatterplot, regardless the amount of dimensions. However, this visualization technique suffers from artifacts due to the dimensionality reduction. Its lack of reliability implies issues of interpretation and trust. Few studies have been devoted to the consideration of the impact of these artifacts, and especially to give feedbacks on how non-expert users can visually analyze projections. The main approach of this thesis relies on an taking these artifacts into account using interactive techniques, in order to allow data scientists or non-expert users to perform a trustworthy visual analysis of projections. The interactive visualization of the proximities applies a coloring of the original proximities relatives to a reference in the data-space. This interactive technique allows revealing projection artifacts in order to help grasping details of the underlying data-structure. In this thesis, we redesign this technique and we demonstrate its potential by presenting two controlled experiments studying the impact of artifacts on the visual analysis of projections. We also present a design-space based on the lens metaphor, in order to improve this technique and to locally visualize a projection free of artifacts issues.

[pt] Pesquisas sobre visualização de dados estatísticos demonstram a necessidade de sistemas que apóiem tomadas da decisões e análises visuais. Constatando problemas de construção de visualizações por usuários inexperientes, nossa questão de pesquisa foi: Como apoiar usuários inexperientes na construção de visualizações eficientes com dados estatísticos? Assim, desenvolvemos ViSC, um sistema de recomendações que apóia a construção interativa de gráficos para visualizar dados estatísticos, através de uma série de recomendações baseadas nos dados selecionados e na interação do usuário com a ferramenta. O sistema explora uma ontologia de visualização para oferecer um conjunto de gráficos que ajudam a responder questões baseadas em informação relacionadas aos dados exibidos no gráfico. Percorrendo os gráficos recomendados através de suas questões relacionadas, o usuário implicitamente adquire conhecimento tanto do domínio quanto dos recursos de visualização que melhor representam os conceitos do domínio de interesse. Esta dissertação apresenta os problemas que motivaram a pesquisa, descreve a ferramenta ViSC e apresenta os resultados de uma pesquisa qualitativa realizada para avaliar ViSC. Para a avaliação, utilizamos o Método de Inspeção Semiótica (MIS) e o Retrospective Communicability Evaluation (RCE) — uma combinação do Método de Avaliação da Comunicabilidade (MAC) e Retrospective Think Aloud Protocol. Concentramo-nos em verificar como as recomendações influenciam na realização de uma tarefa e nas visualizações geradas para então endereçar nossa questão mais ampla. / [en] Research on statistical data visualization emphasizes the need for systems that assist in decision-making and visual analysis. Having found problems in chart construction by novice users, we decided to research the following question: How can we support novice users to create efficient visualizations with statistical data? Thus we, created ViSC, a recommender system that supports the interactive construction of charts to visualize statistical data by offering a series of recommendations based on the selected data and the user interaction with the tool. The system explores a visualization ontology to offer a set of graphs that help to answer information-based questions related to the current graph data. By traversing the recommended graphs through their related questions, the user implicitly acquires knowledge both on the domain and on visualization resources that better represent the domain concepts of interest. This dissertation presents the problems that motivated the research, describes the ViSC tool and presents the results of a qualitative study conducted to evaluate ViSC. We used two methods in our evaluation: the Semiotic Inspection Method (SIM) and the Retrospective Communicability Evaluation (RCE) — a combination of the Communicability Evaluation Method (CEM) and Retrospective Think Aloud Protocol. We first analyze how the questions influence the users traversal through the graph and, then, we address the broader question.

Simulações por computador permitem reduzir custo e, muitas vezes, realizar experimentos que na vida real seriam impraticáveis, ou por questões ambientais (explosões nucleares), ou por fatores que estão fora do controle do ser humano (colisões entre estrelas). Entretanto, e muito difícil manipular e analisar as centenas de gigabytes, ou mesmo terabytes, que tais simulações produzem como resultado. Os trabalhos que lidam com tais conjuntos de dados, tipicamente, empregam tanto técnicas de visualização científica como técnicas de visualização da informação, em geral refletindo o comportamento dos dados em um único instante de tempo. Entretanto, a análise da evolução temporal e a disponibilização de representações visuais integradas ainda é um grande desafio. Esse trabalho introduz diversas estratégias buscando tratar estes problemas, as quais tem em comum a utilização de projeções multidimensionais para apoiar a análise exploratória dos de dados, tanto em um instante de tempo específico, como ao longo da evolução temporal. O objetivo é favorecer a localização de grupos de elementos com comportamento similar e acompanhar sua evolução ao longo da simulação. Uma das estratégias introduzidas resume o comportamento temporal dos dados multidimensionais em uma única visualização, o que permite rastrear as entidades com comportamento similar e analisá-las ao longo da simulação / Computer simulations of physical phenomena allow reducing costs and studying behavior that would be unfeasible to observe in real life situations, either due to environmental limitations, e.g., a nuclear explosion, or due to factors that are beyond human control (e.g., collisions between stars). Millions of primitives (voxels, vertices or particle) may be required to accurately capture system behavior, thus generating very large data sets that are typically time-varying and multidimensional, as multiple simulation variables describe each primitive. Therefore, analyzing the hundreds of gigabytes or even terabytes resulting from these simulations remains a challenge. Current solutions that handle this type of data usually rely on Scientific or Information Visualization techniques, but typically revealing data behavior at a particular time instant. It remains a major challenge to provide visualizations capable of assisting analysts trying to inspect and understand behavior along the temporal domain. This work is an attempt in this direction, introducing several strategies to handle these problems. They have in common the use of multidimensional projection techniques to support exploratory analysis of simulation data, both at specic time instants and along the simulation as a whole. The goal is to favor the perception of groups of elements showing similar behavior and track their temporal evolution. One of the strategies introduced summarizes, in a single visual representation, the temporal behavior of the multidimensional data space, thus allowing analysts to identify and analyze the entities with similar behavior along the simulation

L'analyse de données de plus en plus complexes, volumineuses et issues de différentes sources (e.g. internet, médias sociaux, etc.) est une tâche difficile. Elle reste cependant cruciale dans de très nombreux domaines d'application. Elle implique, pour pouvoir en extraire des connaissances, de mieux comprendre la nature des données, leur évolution ou les nombreuses relations complexes qu'elles peuvent contenir. La visualisation d'informations s'intéresse aux méthodes de représentations visuelles et interactives permettant d'aider un utilisateur à extraire des connaissances. C'est dans ce contexte que se situe le travail présenté dans ce mémoire. Dans un premier temps, nous nous intéressons à la visualisation de longues séries temporelles hiérarchiques. Après avoir analysé les différentes approches existantes, nous présentons le système MultiStream permettant de visualiser, explorer et comparer l'évolution de séries organisées dans une structure hiérarchique. Nous illustrons son utilisation par deux exemples d'utilisation : émotions exprimées dans des médias sociaux et évolution des genres musicaux. Dans un second temps nous abordons la problématique de données complexes modélisées sous la forme de graphes multicouches (différentes types d'arêtes peuvent relier les n÷uds). Plus particulièrement nous nous intéressons au requêtage visuel de graphes volumineux en présentant VERTIGo un système qui permet de construire des requêtes, d'interroger un moteur spécifique, de visualiser/explorer les résultats à différentes niveaux de détail et de suggérer de nouvelles extensions de requêtes. Nous illustrons son utilisation à l'aide d'un graphe d'auteurs provenant de différentes communautés. / The analysis of data that is increasingly complex, large and from different sources (e.g. internet, social medias, etc.) is a dificult task. However, it remains crucial for many fields of application. It implies, in order to extract knowledge, to better understand the nature of the data, its evolution or the many complex relationships it may contain. Information visualization is about visual and interactive representation methods to help a user to extract knowledge. The work presented in this document takes place in this context. At first, we are interested in the visualization of large hierarchical time series. After analyzing the different existing approaches, we present the MultiStream system for visualizing, exploring and comparing the evolution of the series organized into a hierarchical structure. We illustrate its use by two examples: emotions expressed in social media and the evolution of musical genres. In a second time, we tackle the problem of complex data modeled in the form of multilayer graphs (different types of edges can connect the nodes). More specifically, we are interested in the visual querying of large graphs and we present VERTIGo, a system which makes it possible to build queries, to launch them on a specific engine, to visualize/explore the results at different levels of details and to suggest new query extensions. We illustrate its use with a graph of co-authors from different communities.

Técnicas de Visualização Exploratória acopladas a estratégias de interação podem ser muito úteis para apoiar processos de descoberta de conhecimento a partir de dados. Múltiplas visualizações de um mesmo conjunto de dados permitem observá-lo sob várias perspectivas, bem como explorar os pontos fortes e minimizar o efeito dos pontos fracos de técnicas específicas. Nesse contexto, é interessante que as múltiplas visualizações estejam coordenadas de forma que ações de interação executadas pelo usuário sobre uma delas possam ser propagadas para as demais durante as etapas exploratórias. Um aspecto complexo do projeto de ferramentas de visualização é justamente como acoplar representações visuais e controles interativos de maneira flexível. Esse projeto de mestrado abordou a concepção e implementação de um modelo de coordenação de ações capaz de tratar a coordenação entre múltiplas visualizações de forma genérica e flexível. Para isso, foram estudados alguns modelos de coordenação descritos na literatura e utilizados em Sistemas de Visualização de uso geral. O modelo proposto foi implementado no InfoVis, uma plataforma de software extensível que incorpora múltiplas técnicas de visualização exploratória, em desenvolvimento no ICMC-USP / Exploratory Visualization techniques in association with interaction techniques provide useful tools to support knowledge discovery processes from raw data. Multiple visualizations of the same data set allows users to observe the data from multiple perspectives, so as to make the most of the strengths of each technique, while minimizing their weaknesses. In this context, in exploratory processes it is interesting to coordinate the multiple visualizations, so that user interaction actions on one visualization are reflected in the remaining ones, thus reducing the cognitive load on the user. A complex aspect in the project of visualization tools is how to connect visual representations and interactive controls in a flexible way. This work approaches the conception and implementation of a coordination model capable of handling multiple visualizations in a generic and flexible manner. In order to accomplish this, some coordination models described in recent works in the literature and used in multi-purpose Visualization Systems were studied. The proposed model was implemented in InfoVis, an extensible software framework that incorporates multiple exploratory visualization techniques under development at the ICMC-USP

Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-05-30T17:58:54Z No. of bitstreams: 1 JULIA_MARA _COLLEONI_COUTO_DIS.pdf: 14083152 bytes, checksum: 6cf58ce4aafa66a5dee5876f3807b23f (MD5) / Approved for entry into archive by Sheila Dias (sheila.dias@pucrs.br) on 2018-06-11T11:15:59Z (GMT) No. of bitstreams: 1 JULIA_MARA _COLLEONI_COUTO_DIS.pdf: 14083152 bytes, checksum: 6cf58ce4aafa66a5dee5876f3807b23f (MD5) / Made available in DSpace on 2018-06-11T11:24:23Z (GMT). No. of bitstreams: 1 JULIA_MARA _COLLEONI_COUTO_DIS.pdf: 14083152 bytes, checksum: 6cf58ce4aafa66a5dee5876f3807b23f (MD5) Previous issue date: 2018-03-27 / Although human brain stores image more easily than text, most of the tools we use when it comes to project management are based on textual reports, such as Microsoft Office files. The number of projects that fail is still large due to several reasons, among which is the lack of understanding of the project by the stakeholders. When it comes to software development, the uncertainties, ambiguities, and complexities inherent in these projects can amplify the chances of failures. Data visualization tools and techniques can help clarify understanding of the context and project details for stakeholders, reducing the risk of project failure and facilitating communication processes. The objective of this work is to identify what is used of visual management applied to project management, in general, and by projects of various types, and verify, among the results, what can be applied in software development project management, and how it can be done. The method designed for the study is composed of 4 phases: foundation, evaluation, compilation, and proposal. Throughout the research, a systematic literature mapping was developed, followed by a survey and a focus group, resulting in the main contribution of this work: the PMBoK (PMI project management practice guide) extension proposal, containing a process, data visualization tools and techniques that can be used as support by the manager and stakeholders in a software development project. / Apesar de o c?rebro humano armazenar imagens com maior facilidade do que texto, grande parte das ferramentas utilizadas quando se trata de gest?o de projetos ? baseada em relat?rios textuais, como arquivos do pacote Microsoft Office. Atualmente, ainda ? grande a quantidade de projetos que falham, devido a motivos diversos, dentre os quais est? o n?o entendimento do projeto por parte das partes envolvidas. Quando se trata de desenvolvimento de software, as incertezas, ambiguidades e complexidades inerentes a estes projetos podem amplificar as chances de falhas. As ferramentas e t?cnicas de visualiza??o de dados podem ajudar a esclarecer o entendimento do contexto e de detalhes do projeto para todas as partes envolvidas, reduzindo o risco de insucesso no projeto e facilitando os processos de comunica??o. O objetivo deste trabalho ? identificar o que ? utilizado de gest?o visual aplicada ao gerenciamento de projetos, de maneira geral e por projetos de diversos tipos, e verificar, dentre os resultados, o que pode ser aplicado no gerenciamento de projetos de desenvolvimento de software, e de que maneira isso pode ser feito. A metodologia desenhada para o estudo ? composta por 4 fases: fundamenta??o, compila??o, avalia??o e proposta. Ao longo da pesquisa foram desenvolvidos um mapeamento sistem?tico da literatura, seguido por uma survey e um focus group, resultando na principal contribui??o deste trabalho: a proposta de extens?o do PMBoK (o guia de pr?ticas de gerenciamento de projetos do PMI), contendo um processo, t?cnicas e ferramentas de visualiza??o de dados que podem ser utilizadas como apoio pelo gerente e demais partes envolvidas em um projeto de desenvolvimento de software.