Context: Agile/lean development has been successful situations where small teams collaborate over long periods of time with project stakeholders. Unclear is how such teams plan and coordinate their work in such situations where inter-dependencies with other projects exist. In large organizations, scattered teams and complex team structure makes it difficult for every stakeholder to have a clear understanding of project information. These factors make it difficult for large‐scale organizations to adopt the agile/lean development paradigm. Objectives: The goal of conducting this study is to find the information visualization techniques that ease or resolve the challenges of agile development in large-scale organizations. The study reports the challenges of agile development and information visualization techniques in literature and reported by industrial experts. Additionally, proposed a guideline that how information visualization technique can be used to ease or resolve related challenge of agile development. Methods: For this particular study, two research methodologies are used; Systematic Literature Review (SLR) and Industrial Survey. Two SLRs are performed for finding 1) challenges of agile development and 2) information visualization techniques in agile development. Data sources like Engineering Village (Inspec/ Compendex), IEEE Explore digital library, ACM digital library, Science Direct, ISI-Web of knowledge; Scopus were used to select primary study. Industrial survey was conducted in order to obtain empirical evidence to our findings. In survey, mainly questions were related to challenges of agile development and information visualization techniques practiced by industrial experts. Results: 84 different challenges of agile development found in literature and by applying grounded theory we found 9 distinct categories of challenges. There were 55 challenges reported by industrial experts in survey which later grouped into 10 distinct challenges. 45 information visualization techniques found in literature and grouped into 21 distinct technologies. There were 47 different information visualization techniques reported by industrial experts. When we grouped these techniques there were 9 distinct technologies found by applying open, axial and selective coding of grounded theory Conclusions: Systematic Literature Review and Industrial Survey confirmed that information visualization techniques can be used to ease or resolve challenges of agile development. Along with other visualization techniques, Data Flow Diagrams, UML, Use Case Diagrams, Burn Down Charts, Scrum Story Board, Kanban Boards and Gantt Chart are highly reported techniques found through systematic literature review and later confirmed by industrial experts. On the other hand, through survey we found that industrial experts mainly rely on informal and customized information visualization techniques to visualize information.

Eye-tracking devices can tell us where on the screen a person is looking. Researchers frequently analyze eye-tracking data manually, by examining every frame of a visual stimulus used in an eye-tracking experiment so as to match 2D screen-coordinates provided by the eye-tracker to related objects and content within the stimulus. Such task requires significant manual effort and is not feasible for analyzing data collected from many users, long experimental sessions, and heavily interactive and dynamic visual stimuli. In this dissertation, we present a novel analysis method. We would instrument visualizations that have open source code, and leverage real-time information about the layout of the rendered visual content, to automatically relate gaze-samples to visual objects drawn on the screen. Since such visual objects are shown in a visualization stand for data, the method would allow us to necessarily detect data that users focus on or Data of Interest (DOI). This dissertation has two contributions. First, we demonstrated the feasibility of collecting DOI data for real life visualization in a reliable way which is not self-evident. Second, we formalized the process of collecting and interpreting DOI data and test whether the automated DOI detection can lead to research workflows, and insights not possible with traditional, manual approaches.

Visualizations, web technologies and mobile devices are subjects which are trending nowadays in the world of technology and many research projects are tackling different issues. The originality of the thesis is in the aspect of bringing all these mentioned subjects together and providing findings that will help web designers when implementing visualizations for mobile phones. The thesis explores the use of web technologies for the visualization of complex data for mobile devices, both looking at the technical state of the art and capabilities, and at the difference in information needs for users in a mobile usage context. The first part deals with an in-depth research of the existing projects that deal with similar issues, analyzing the official documentation of the technologies and the community of developers. The other part of the research is focused on providing insights on the required changes for adapting to the needs of mobile device users by doing a usability testing on a specific visualization. The results provide information valuable for adapting visualizations, such as font sizes, color combinations, animation complexity and data simplicity. The use case that is used for feeding the visualizations with data belongs to the domain of eco visualizations, in particular dealing with sustainable food consumption.

This thesis documents the development of a web-application attacking the problem of visualization of co-authorship networks. The visualization encompasses several views.Each of them shows different aspects of the data which is loaded from Academic Archive Online (DiVa), a library system which holds all publications released in the Linnaeus University.  To detect relationships among authors, a new interactive layout for Node-Link Diagrams was developed which shows publications, authors and corresponding organizations (faculties, departments) in a radial manner. This Network-View is connected to another view showing the attributes (year, type) of the publications. In development, particular emphasis was placed on a rich support of user interaction in order to equip the user with a tool that allows graphical and explorative analysis of the underlying data.

Abstract The topic of this work is the transformation of real-world human activity into virtual environments. More specifically, the topic is the process of identifying various aspects of visible human activity with sensor networks and studying the different ways how the identified activity can be visualized in a virtual environment. The transformation of human activities into virtual environments is a rather new research area. While there is existing research on sensing and visualizing human activity in virtual environments, the focus of the research is carried out usually within a specific type of human activity, such as basic actions and locomotion. However, different types of sensors can provide very different human activity data, as well as lend itself to very different use-cases. This work is among the first to study the transformation of human activities on a larger scale, comparing various types of transformations from multiple theoretical viewpoints. This work utilizes constructs built for use-cases that require the transformation of human activity for various purposes. Each construct is a mixed reality application that utilizes a different type of source data and visualizes human activity in a different way. The constructs are evaluated from practical as well as theoretical viewpoints. The results imply that different types of activity transformations have significantly different characteristics. The most distinct theoretical finding is that there is a relationship between the level of detail of the transformed activity, specificity of the sensors involved and the extent of world knowledge required to transform the activity. The results also provide novel insights into using human activity transformations for various practical purposes. Transformations are evaluated as control devices for virtual environments, as well as in the context of visualization and simulation tools in elderly home care and urban studies. / Tiivistelmä Tämän väitöskirjatyön aiheena on ihmistoiminnan muuntaminen todellisesta maailmasta virtuaalitodellisuuteen. Työssä käsitellään kuinka näkyvästä ihmistoiminnasta tunnistetaan sensoriverkkojen avulla erilaisia ominaisuuksia ja kuinka nämä ominaisuudet voidaan esittää eri tavoin virtuaaliympäristöissä. Ihmistoiminnan muuntaminen virtuaaliympäristöihin on kohtalaisen uusi tutkimusalue. Olemassa oleva tutkimus keskittyy yleensä kerrallaan vain tietyntyyppisen ihmistoiminnan, kuten perustoimintojen tai liikkumisen, tunnistamiseen ja visualisointiin. Erilaiset anturit ja muut datalähteet pystyvät kuitenkin tuottamaan hyvin erityyppistä dataa ja siten soveltuvat hyvin erilaisiin käyttötapauksiin. Tämä työ tutkii ensimmäisten joukossa ihmistoiminnan tunnistamista ja visualisointia virtuaaliympäristössä laajemmassa mittakaavassa ja useista teoreettisista näkökulmista tarkasteltuna. Työssä hyödynnetään konstrukteja jotka on kehitetty eri käyttötapauksia varten. Konstruktit ovat sekoitetun todellisuuden sovelluksia joissa hyödynnetään erityyppistä lähdedataa ja visualisoidaan ihmistoimintaa eri tavoin. Konstrukteja arvioidaan sekä niiden käytännön sovellusalueen, että erilaisten teoreettisten viitekehysten kannalta. Tulokset viittaavat siihen, että erilaisilla muunnoksilla on selkeästi erityyppiset ominaisuudet. Selkein teoreettinen löydös on, että mitä yksityiskohtaisemmasta toiminnasta on kyse, sitä vähemmän tunnistuksessa voidaan hyödyntää kontekstuaalista tietoa tai tavanomaisia datalähteitä. Tuloksissa tuodaan myös uusia näkökulmia ihmistoiminnan visualisoinnin hyödyntämisestä erilaisissa käytännön sovelluskohteissa. Sovelluskohteina toimivat ihmiskehon käyttäminen ohjauslaitteena sekä ihmistoiminnan visualisointi ja simulointi kotihoidon ja kaupunkisuunnittelun sovellusalueilla.

Les réseaux sont des modèles qui nous permettent de comprendre les relations entre éléments du monde réel. Une grande quantité de réseaux sont dynamiques, c'est-à-dire que leur connexité change au cours du temps. Comprendre les changements de connexité signifie comprendre les interactions entre les éléments de systèmes complexes: comment se forment les relations sociales et commerciales, comment sont transmis les signaux entre les régions du cerveau, comment s'organisent les réseaux trophiques après des catastrophes environnementales. Au-delà de ce que nous permettent la technologie et les algorithmes d'analyses, l'homme dispose d'une capacité unique pour comprendre et interpréter des informations : la vision et la cognition. Cette thèse développe et examine des moyens pour explorer les réseaux dynamiques d'une manière interactive et visuelle. Je propose des techniques pour déplier la complexité des réseaux, avec le but de les rendre compréhensibles, de les voir à partir de perspectives différentes, d'examiner leurs composantes. Déplier des réseaux est une métaphore, comme la création des cartes bidimensionelles d'objets tridimensionnels comme la Terre: chaque méthode de projection a comme résultat une carte différente qui permet de voir des relations différentes entre la taille des continents et des océans, des distances, etc. Je propose les techniques de dépliage suivantes, implémentées et évaluées dans des systèmes interactifs : (i) une navigation temporelle qui permet de naviguer plus efficacement entre des différents instants, ainsi qu'un feedback visuel qui permet de mieux comprendre les changements dans les réseaux entre deux instants arbitraires. (ii) Des designs permettant la comparaison directe de deux réseaux avec des liens pondérés. (iii) Un modèle de visualisation pour des réseaux denses avec des liens pondérés, ainsi que (iv) la génération de réseaux synthétiques utilisés pour l'évaluation des visualisations. Afin de mieux créer et évaluer des visualisations, nous (v) proposons une taxonomie de tâche pour décrire des tâches accomplies par des analystes des réseaux. Pour compléter, (vi) nous généralisons l'idée de dépliage pour décrire d'autres genres de données temporelles, représentable dans des cubes espace-temps. Cela concerne la visualisation de vidéos, des données multi-variées, ainsi que la géographique. Une telle généralisation a pour but de fournir une base commune pour échanger des techniques de visualisation et de mieux comprendre l'espace de design pour les réseaux dynamiques. Dans cette optique, nous proposons une taxonomie d'opérations génériques qui nous permet de transformer un cube espace-temps en visualisation bidimensionelle, ainsi qu'une description des formes évoquées par les données dans le cube espace-temps. / Networks are models that help us understanding and thinking about relationships between entities in the real world. Many of these networks are dynamic, i.e. connectivity changes over time. Understanding changes in connectivity means to understand interactions between elements of complex systems; how people create and break up friendship relations, how signals get passed in the brain, how business collaborations evolve, or how food-webs restructure after environmental changes. However, understanding static networks is already difficult, due to size, density, attributes and particular motifs; changes over time very much increase this complexity. Quantification of change is often insufficient, but beyond an analysis that is driven by technology and algorithms, humans dispose a unique capability of understanding and interpreting information in data, based on vision and cognition. This dissertation explores ways to interactively explore dynamic networks by means of visualization. I develop and evaluate techniques to unfold the complexity of dynamic networks, making them understandable by looking at them from different angles, decomposing them into their parts and relating the parts in novel ways. While most techniques for dynamic network visualization rely on one particular type of view on the data, complementary visualizations allow for higher-level exploration and analysis. Covering three aspects Tasks, Visualization Design and Evaluation, I develop and evaluate the following unfolding techniques: (i) temporal navigation between individual time steps of a network and improved animated transitions to better understand changes, (ii) designs for the comparison of weighted graphs, (iii) the Matrix Cube, a space-time cube based on adjacency matrices, allowing to visualize dense dynamic networks by, as well as GraphCuisine, a system to (iv) generate synthetic networks with the primary focus on evaluating visualizations in user studies. In order to inform the design and evaluation of visualizations, we (v) provide a task taxonomy capturing users' tasks when exploring dynamic networks. Finally, (vi) the idea of unfolding networks with Matrix Cubes is generalized to other data sets that can be represented in space-time cubes (videos, geographical data, etc.). Visualizations in these domains can inspire visualizations for dynamic networks, and vice-versa. We propose a taxonomy of operations, describing how 3D space-time cubes are decomposed into a large variety of 2D visualizations. These operations help us exploring the design space for visualizing and interactively unfolding dynamic networks and other spatio-temporal data, as well as may serve users as a mental model of the data.

Repositórios com volumes de dados cada vez maiores foram viabilizados pelo desenvolvimento tecnológico, criando importantes fontes de informação em diversas áreas da atividade humana. Esses repositórios freqüentemente incluem informação sobre o comportamento temporal e o posicionamento espacial dos itens neles representados, os quais são extremamente relevantes para a análise dos dados. O processo de descoberta de conhecimento a partir de grandes volumes de dados tem sido objeto de estudo em diversas disciplinas, dentre elas a Visualização de Informação, cujas técnicas podem apoiar diversas etapas desse processo. Esta tese versa sobre o uso da Visualização Exploratória em conjuntos de dados com atributos temporais e espaciais, empregando a estratégia de múltiplas visualizações coordenadas para apoiar o tratamento de dados em estágios iniciais de processos de descoberta de conhecimento. São propostas duas novas representações visuais temporais – denominadas ‘Variação Temporal Uni-escala’ e ‘Variação Temporal Multi-escala’ – para apoiar a análise exploratória de dados temporais. Adicionalmente, é proposto um modelo de arquitetura de software – AdaptaVis, que permite a integração dessas e outras representações visuais em uma plataforma de visualização de informação flexível, extensível e adaptável às necessidades de diferentes usuários, tarefas e domínios de aplicação – a plataforma InfoVis. Sessões de uso realizadas com dados e usuários reais dos domínios de Climatologia e Negócios permitiram validar empiricamente as representações visuais e o modelo. O modelo AdaptaVis e a plataforma InfoVis estabelecem bases para a continuidade de diversas pesquisas em Visualização de Informação, particularmente o estudo de aspectos relacionados ao uso coordenado de múltiplas visualizações, à modelagem do processo de coordenação, e à integração entre múltiplas técnicas visuais e analíticas. / Data repositories with ever increasing volumes have been made possible by the evolution in data collection technologies, creating important sources of information in several fields of human activity. Such data repositories often include information about both the temporal behavior and the spatial positioning of data items that will be relevant in future data analysis tasks. The process of discovering knowledge embedded in great volumes of data is a topic of study in several disciplines, including Information Visualization, which offers a range of techniques to support different stages of a discovery process. This thesis addresses the application of Exploratory Visualization techniques on datasets with temporal and spatial attributes, using the strategy of coordinating multiple data views, to assist data treatment on early stages of knowledge discovery processes. Two temporal visual representations are proposed – ‘Uni-scale Temporal Behavior’ and ‘Multi-scale Temporal Behavior’ – that support the exploratory analysis of temporal data. Moreover, a software architecture model is introduced – AdaptaVis, that allows the integration of these and other visualization techniques into a flexible, extensible and adaptable information visualization platform – called InfoVis – that may be tailored to meet the requirements of different users, tasks and application domains. Sessions conducted with real data and users from the Climatology and Business application domains allowed an empirical validation of both the visual representations and the model. The AdaptaVis model and the InfoVis platform establish the basis for further research on issues related to the coordinated use of multiple data views, the modeling of the coordination process and the integration amongst multiple visual and analytical techniques.

Na maioria dos processos de análise de imagens há a necessidade de um pré-processamento, no qual são extraídos e calculados vetores de características que representem as imagens são utilizados no cálculo de similaridade. Uma dificuldade nessas tarefas é o grande número de características que definem um espaço de alta dimensionalidade, afetando fortemente o desempenho das tarefas que seguem, que podem envolver uma análise visual, um agrupamento ou uma classificação de dados, por exemplo. Lidar com esse problema normalmente exige técnicas de redução de dimensionalidade ou seleção de características. O presente trabalho dá sequência a trabalhos que utilizam técnicas de visualização como suporte para avaliar espaços de características gerados a partir de coleções de imagens. Nele, objetiva-se aprimorar um método baseado na análise visual de conjuntos de imagens empregando a árvore de similaridade Neighbor-Joining que apoia o usuário a selecionar um subespaço de características que mantenha ou melhore os resultados das visualizações do conjunto de imagens. A partir da metodologia proposta, a avaliação e a seleção de características representativas é realizada usando a visualização NJ. A maior parte dos experimentos responde positivamente para diferentes conjuntos de imagens representados por vários extratores, obtendo-se processos de seleção personalizados mais precisos e eficazes, em termos de agrupamento, do que abordagens automáticas reportadas na literatura / In the majority of the image analysis processes there is need for a pre-processing step, in which feature vectors representative of the images are extracted and similarity methods are calculates. A difficult step in the process is to choose amongst the large number of features available, that will define a feature space of high dimensionality, impacting the cost of the subsequent processing tasks, such as visual analysis, clustering and classification. This problem is usually handled by dimension reduction of feature selection techniques. This work extends and improves previous work that employs visualization and visual analysis techniques to support evaluation of feature spaces created from image collections. The goal is to improve a previous method of feature selection through visualization to employ similarity trees via the Neighbor Joining (NJ) algorithm as the basis for the visual layout, as well as to improve the choices of the analyst regarding tools for visual selection of features. The same process can be employed to support evaluation of feature spaces using the NJ visualization. The majorities of experiments results in improvement of spaces generated by various extractors, yielding personalized selection process that are more precisely related to user\'s perspective of the data set and are perform similarly or better than automatic approaches available in the literature. Keywords: information visualization, mining, visual images, visual analysis of the feature space, similarity trees

Ferramentas e técnicas de visualização promovem uma análise de dados mais efetiva pelo fato de explorar a capacidade humana na percepção de padrões, principalmente em representações gráficas. Muitos fenômenos são associados a algum tipo de referência, temporal ou geográfica, que pode oferecer informação importante quando são submetidos a processos de análise. Este trabalho aborda representações visuais de dados geradas por técnicas de projeção multidimensional, e propõe uma estratégia para o tratamento diferenciado das referências temporais ou geográficas presentes em conjuntos de dados, no processo de gerar uma projeção multidimensional. Foi proposta e implementada uma variação da técnica Least Square Projection (LSP) que evidencia a informação das referências e permite ao usuário interagir com os mapas visuais gerados, bem como diversas funcionalidades que auxiliam no processo de análise exploratória. A nova abordagem é ilustrada por meio de estudos de caso envolvendo bases de dados temporais e com referências geográficas, em que foi possível observar o comportamento global dos elementos, bem como comportamentos de elementos ou grupos de elementos de interesse. Limitações da estratégia proposta também são discutidas / Visualization tools and techniques promote more effective data analysis by exploiting the human visual perception capabilities in detecting patterns in graphical representations. Many phenomena generate data that include temporal or geographical references, which are likely to provide important information in data analysis procedures. This work addresses data visualizations generated with multidimensional projections, proposing a strategy to handle temporal and geographical references present in multidimensional data sets, when generating multidimensional projections. The Least Squares Projection (LSP) technique was extended to explicitly handle the reference information and represent it in the visual maps, and a set of supporting analysis functions have been implemented. The proposed approach is illustrated through case studies on multidimensional data sets, in which it was possible to observe the global behavior of the elements, as well as individual behavior of elements or groups of elements of interest

O uso da computação em uma variedade cada vez maior de aplicações fez com que os Sistemas de Gerenciamento de Bases de Dados (SGBD) passassem a ser utilizados para armazenar os mais diversos tipos de dados complexos, como imagens, sons e cadeias de DNA entre outros. Consultas baseadas em relações de ordem total ou igualdade não podem ser aplicadas ou tem aplicações limitadas quando executadas nestes conjuntos de dados. Logo, efetua-se consultas por similaridade baseadas no conteúdo de dados desses tipos. Se tais conjuntos de dados podem ser representados em um espaço métrico, é possível utilizar os Métodos de Acesso Métricos (MAM), como a Slim-Tree, a M-Tree e a DBM-Tree, para otimizar as consultas por similaridade. Porém, os MAM são muito difíceis de compreender e analisar devido à complexidade de suas estruturas. Esta dissertação apresenta um sistema de visualização que permite a inspeção visual da organização e do comportamento de MAM, provendo aos desenvolvedores e administradores de SGBD uma forma rápida e fácil para obter informações essenciais sobre estas estruturas que podem levar a melhorias no desempenho de consultas e outras operações. / The use of computers by an increasing variety of applications led the Database Management Systems (DBMS) to be used to store a wide range of complex data types, such as images, sounds, DNA chains, etc. Queries based on the total order relationship and/or equality can not be applied or have a limited range of applications when performed over these datasets. It is necessary to use similarity queries based on the contents of the data. If these datasets can be represented as metric spaces, it is possible to use the Metric Access Methods (MAM), such as the Slim-Tree, the M-Tree and the DBM-Tree, to optimize similarity queries. However, MAM are very hard to understand and analyze due to their complex structures. This work presents a visualization system that allows the visual inspection of the organization and the behavior of MAM. The usage of this system provides to MAM developers and database administrators, an easy and fast way to acquire information about key aspects of these structures, which can lead to improvements on the performance of queries and other operations.