Neurocirurgia guiada por imagem permite ao neurocirurgião navegar dentro do cérebro do paciente, usando imagens pré-operatórias como orientação, através do uso de sistemas de rastreamento, durante o procedimento cirúrgico. Muitos sistemas desenvolvidos para neurocirurgia guiada por imagem, empregam imagens pré-operatórias para fornecer orientação ao cirurgião, durante o procedimento cirúrgico. Seguindo um procedimento de calibração, a posição tridimensional e orientação dos instrumentos cirúrgicos podem ser transmitidas ao computador. Estas informações espaciais são usadas para acessar a região de interesse nas imagens pré-operatórias com a finalidade de apresentá-las ao cirurgião durante o procedimento. Contudo, quando ocorre a craniotomia, o movimento dos tecidos do cérebro pode ser fonte significativa de erro nestes sistemas. A arquitetura implementada neste trabalho visa o desenvolvimento de um sistema que permite planejamento e orientação cirúrgica. Para orientação cirúrgica foi desenvolvido um software que permite extrair fatias do volume de imagens de ressonância magnética (IRM), com orientação fornecida por um transdutor de posição magnético (Polhemus®). As fatias extraídas serão, futuramente, correlacionadas com imagens de ultra-som (IUS) intra-operatórias para detectar e corrigir a deformação do tecido cerebral durante a cirurgia. A ferramenta para navegação pré-cirúrgica foi desenvolvida para fornecer três fatias ortogonais obtidas através do volume de imagens. Na metodologia usada para a implementação do software, foi utilizada a linguagem de programação Python e a biblioteca gráfica Visualization Toolkit (VTK). Os resultados mostraram que o programa de planejamento pré-cirúrgico, gerou uma alta resolução na visualização dos planos ortogonais e oblíquos das IRM, além de ser rápido e interativo. O programa de extrair fatias do volume de IRM permitiu a aplicação de transformações ao volume, com base nos valores de coordenadas fornecidos pelo transdutor de posição. / Image guided neurosurgery enables the neurosurgeon to navigate inside the patient\'s brain using pre-operative images as a guide and a tracking system, during surgical procedure. Many image guided neurosurgery implementations employ pre-operative images as a guide to the surgeons throughout surgical procedure. Following a calibration procedure, three-dimensional position and orientation of surgical instruments may be transmitted to computer. The spatial information is used to access an interest region, in the pre-operative images, displaying them to the neurosurgeon during the surgical procedure. However, when a craniotomy is involved, movements of brain tissue can be a significant source of error in these systems. The architecture implemented in this work intends the development of a system to surgical planning and orientation. For surgical orientation, the software developed allows the extraction of slices from the volume of the magnetic resonance images (MRI) with orientation supplied by a magnetic position sensor (Polhemus®). In the future, the extracted slices will be correlated with intra-operative ultrasound images to detect and to correct the deformation of brain tissue during the surgery. Also, a tool for pre-operative navigation was developed, providing three orthogonal planes through the image volume. In the methodology used for the software implementation, the Python programming language and the Visualization Toolkit (VTK) graphics library were used. The results showed that the program of pre-operative navigation had high resolution in the visualization of orthogonal and oblique MRI planes. Furthermore, it was fast and interactive. The program to extract slices of the MRI volume allowed the application of transformations in the volume, using coordinates supplied by the position sensor.

" More and more researchers are focusing on the management, querying and pattern mining of streaming data. The visualization of streaming data, however, is still a very new topic. Streaming data is very similar to time-series data since each datapoint has a time dimension. Although the latter has been well studied in the area of information visualization, a key characteristic of streaming data, unbounded and large-scale input, is rarely investigated. Moreover, most techniques for visualizing time-series data focus on univariate data and seldom convey multidimensional relationships, which is an important requirement in many application areas. Therefore, it is necessary to develop appropriate techniques for streaming data instead of directly applying time-series visualization techniques to it. As one of the main contributions of this dissertation, I introduce a user-driven approach for the visual analytics of multivariate data streams based on effective visualizations via a combination of windowing and sampling strategies. To help users identify and track how data patterns change over time, not only the current sliding window content but also abstractions of past data in which users are interested are displayed. Sampling is applied within each single time window to help reduce visual clutter as well as preserve data patterns. Sampling ratios scheduled for different windows reflect the degree of user interest in the content. A degree of interest (DOI) function is used to represent a user's interest in different windows of the data. Users can apply two types of pre-defined DOI functions, namely RC (recent change) and PP (periodic phenomena) functions. The developed tool also allows users to interactively adjust DOI functions, in a manner similar to transfer functions in volume visualization, to enable a trial-and-error exploration process. In order to visually convey the change of multidimensional correlations, four layout strategies were designed. User studies showed that three of these are effective techniques for conveying data pattern changes compared to traditional time-series data visualization techniques. Based on this evaluation, a guide for the selection of appropriate layout strategies was derived, considering the characteristics of the targeted datasets and data analysis tasks. Case studies were used to show the effectiveness of DOI functions and the various visualization techniques. A second contribution of this dissertation is a data-driven framework to merge and thus condense time windows having small or no changes and distort the time axis. Only significant changes are shown to users. Pattern vectors are introduced as a compact format for representing the discovered data model. Three views, juxtaposed views, pattern vector views, and pattern change views, were developed for conveying data pattern changes. The first shows more details of the data but needs more canvas space; the last two need much less canvas space via conveying only the pattern parameters, but lose many data details. The experiments showed that the proposed merge algorithms preserves more change information than an intuitive pattern-blind averaging. A user study was also conducted to confirm that the proposed techniques can help users find pattern changes more quickly than via a non-distorted time axis. A third contribution of this dissertation is the history views with related interaction techniques were developed to work under two modes: non-merge and merge. In the former mode, the framework can use natural hierarchical time units or one defined by domain experts to represent timelines. This can help users navigate across long time periods. Grid or virtual calendar views were designed to provide a compact overview for the history data. In addition, MDS pattern starfields, distance maps, and pattern brushes were developed to enable users to quickly investigate the degree of pattern similarity among different time periods. For the merge mode, merge algorithms were applied to selected time windows to generate a merge-based hierarchy. The contiguous time windows having similar patterns are merged first. Users can choose different levels of merging with the tradeoff between more details in the data and less visual clutter in the visualizations. The usability evaluation demonstrated that most participants could understand the concepts of the history views correctly and finished assigned tasks with a high accuracy and relatively fast response time. "

Email provides the average person with the ability to instantaneously communicate with people across the planet. However, the ease of this communication not only allows people to exchange information, but can also generate a large volume of messages. The methods of interacting with this repository currently in use are far from adequate. This thesis focuses on the development of a new interface for managing one's inbox and mail folders. The approach provides the user with a data visualization of an overview of the entire mailbox, or a chosen subset, at once. The tool includes interactions that allow a user to focus his or her attention on specific email and delve deeper into it in a simple intuitive fashion. A user study confirmed the usefulness of the resulting system.

Email has developed into one of the most extensively used computer applications. Email interfaces, on the other hand, have gone through very few transformations since their inception. As the growing volumes of email data accumulate in users' email boxes, these interfaces fail to provide effective message handling and browsing support. Saved email messages provide not only a vast and pulsating record of one's electronic past, but also a potential source of valuable insights into the structure and dynamics of one's social network. In this thesis, we introduce a visualization approach to email that draws upon email’s inherently personal character and uses intimacy as a key parameter. We have developed faMailiar, a novel email interface that visualizes email in a chronological manner through two alternative, calendar-like views that present email activity on different time granularity scales. Visual mappings of email data and support for filtering help the user see rhythms and patterns in her social interactions. Zooming, panning and implicit semantic zooming facilitate navigation across large email collections. This thesis also describes our iterative, human-centered design method. Two user studies have been performed at different stages of the process, and we explain their purpose, results and implications.

Geo-based Information Visualizations (GIV) allow people to analyze data points based on their related geographic locations. This approach is usually adopted where a large scale geo-referenced dataset is present and users are trying to find a way to examine hidden patterns within this data. One of the emerging trends in GIV is to visualize social media data to show how information flows between users of popular social networking sites. Due to its public nature and the large number of users, most of the visualizations in this area rely on conversational data from Twitter (Twitter.com). In this thesis, we design and implement a web-based interactive GIV system, AcademiaMap-GIV, to visualize online conversations among scholars on Twitter. A formal exploratory user study was also conducted on the target users. The study results demonstrated that most of the study respondents found the features of AcademiaMap-GIV effective in regards to visualizing information of their interests.

Monitoring volumes of malicious network data for across multiple sources can potentially be overwhelming. As a result, vital data is at a greater risk of being overlooked and the time span for analyzing it could be too lengthy. One way to address this issue is to employ network security visualization techniques to evaluate security risks and identify malicious activity to help mitigate compromised nodes on a network. The purpose of this thesis is to introduce a visualization framework to help reduce task-completion time, enhance situational awareness, and decrease user error of complex visualizations for network security applications. From the developed framework, three techniques are suggested as contributions using visualization and interaction: (1) Stereoscopic visualization technique aims to increase user awareness of vulnerabilities and malicious attacks, (2) the recommender system aims to ensure efficient navigation in complex 3D environments, and (3) an interaction system aims to assist in usability of visualization environments using Natural User Interfaces (NUIs). To investigate the aforementioned techniques, the following tools were created: 3D Stereoscopic Vulnerability Assessment Tool (3DSVAT), Parallel 3D Coordinate Visualization (P3D), NAVSEC recommender system, and Interaction System for Network Security (InterSec).

Information visualization can be valuable in a wide range of applications, it deals with abstract, non-spatial data and with the representation of data elements in a meaningful form irrespective of the size of the data, because sometimes visualization itself focuses on the certain key aspects of the data in the representation and thus it helps by providing ease for the goal oriented interpretation. Information visualization focuses on providing a spontaneous and deeper level of the understanding of the data. Research collaboration enhances sharing knowledge and also enhances an individual’s talent. New ideas are generated when knowledge is shared and transferred among each other. According to (He et al, 2009) Research collaboration has been considered as a phenomenon of growing importance for the researchers, also it should be encouraged and is considered to be a “good thing” among the researchers. The main purpose of this thesis work is to prepare a model for the competence profile visualization purpose. For this purpose the study of different visualization techniques that exist in the field of information visualization are discussed in this thesis work. The study and discussion about the visualization techniques motivates in selecting appropriate visualization techniques for the visualization of Ontology-based competence profiles for research collaboration purpose. A proof of concept is developed which shows how these visualization techniques are applied to visualize several components of competence profile.

Com base em métodos de extração de características de imagens e extração de vocabulários de textos, podemos empregar técnicas para posicionamento de dados multidimensionais no plano para mapear conjuntos de dados em espaços visuais, auxiliando usúarios na interpretação e análise dos dados. Alguns desses métodos constroem árvores de similaridade, impondo uma hierarquia sobre as relações entre as características extraídas dos dados. Em um ambiente de análise exploratória, é natural que se procurem métodos e técnicas capazes de manipular e interagir com os dados de forma rápida e eficiente. Nesse contexto, o trabalho visa implementar e aplicar técnicas conhecidas de navegação e interação em árvores no contexto de visualizações baseadas em posicionamento de pontos no plano. Em particular as técnicas NJ e MST, implementadas e utilizadas com sucesso na ferramenta PEx-Image, tornaram-se pontos chave para o auxílio na exploração dos dados através das apresentações radial e hiperbólica, implementadas juntamente com ferramentas de exploração. Este trabalho implementa e apresenta a capacidade exploratória dessas duas formas de apresentação de árvores sobre as visualizações NJ e MST. / Based on methods of feature extraction for images and vocabulary exploration for text, we can apply point placement techniques to multidimensional data in order to map data sets into visual spaces, assisting users on data analysis and interpretation. Some of these methods build similarity trees, imposing a hierarchy on the relationship between the characteristics extracted from data. In an exploratory analysis environment, it is natural to use methods and techniques capable of manipulating and interacting data quickly and eciently. In this context, this paper aims to implement and apply known techniques of tree navigation and interaction in the context of point placement visualizations. In particular the NJ and MST techniques, implemented and successfully used in the system PEx-Image, are the main focus for helping data exploration through Radial and Hyperbolic Layouts, implemented with exploration tools. This work implements Radial and Hyperbolic layouts to support exploration of NJ and MST views

Effect mapping is a method often used by companies in order to try to tie together the business goals with the users and their needs. The results of the effect mapping is often presented in long reports, with an additional effect map attached to it. Unfortunately, few clients tend to read these reports that they later are supposed to base future decisions on. This thesis aims to examine the efficiency in readers' perception of information, specifically the information from effect mapping. It evaluates if the understanding and perception, as well as the participation, could be improved by developing a digital web based version of the information. The objective of this study is to determine if a visualized web version of the effect mapping is more efficient than the original paper report version. The purpose is to help consulting firms improve the communication with their clients, that the clients will take part of and consume the information provided by the effect mappings that are delivered in a more efficient way. The process started out with workshops that were a foundation for a prototype. The prototype was developed based on the theoretical framework of this thesis, and created from a specific example of a traditional effect mapping report. The prototype was tested and evaluated before compared to, and evaluated with, the original paper report version of the effect mapping. The findings are that a web based, visualized presentation of the effect mapping is more efficient than the original paper report version. Not only did the participants of the study find the correct information in a shorter amount of time, but the user experience was also enhanced. The findings in this study are useful for firms that want to present results of effect mapping to customers who do not tend to spend time reading the full reports, but would rather get a quicker overview. This study can be used as a point of departure for future research, by testing it on other reports or firms.

The continuously advent of new technologies have made a rich and growing type of information sources available to analyses and investigation. In this context, multidimensional data analysis is considerably important when dealing with such large and complex datasets. Among the possibilities when analyzing such kind of data, applying visualization techniques can help the user find and understand patters, trends and establish new goals. Some applications examples of visualization of multidimensional data analysis goes from image classification, semantic word clouds, cluster analysis of document collection to exploration of multimedia content. This thesis presents several visualization methods to interactively explore multidimensional datasets aimed from specialized to casual users, by making use of both static and dynamic representations created by multidimensional projections. Firstly, we present a multidimen- sional projection technique which faithfully preserves distance and can handle any type of high-dimensional data, demonstrating applications scenarios in both multimedia and text docu- ments collections. Next, we address the task of interpreting projections in 2D, by calculating neighborhood errors. Hereafter, we present a set of interactive visualizations that aim to help users with these tasks by revealing the quality of a projection in 3D, applied in different high dimensional scenarios. In the final part, we address two different approaches to get insight into multimedia data, in special soccer sport videos. While the first make use of multidimensional projections, the second uses efficient visual metaphor to help non-specialist users in browsing and getting insights in soccer matches. / O advento contínuo de novas tecnologias tem criado um tipo rico e crescente de fontes de informação disponíveis para análise e investigação. Neste contexto, a análise de dados multidi- mensional é consideravelmente importante quando se lida com grandes e complexos conjuntos de dados. Dentre as possibilidades ao analisar esses tipos de dados, a aplicação de técnicas de visualização pode auxiliar o usuário a encontrar e entender os padrões, tendências e estabelecer novas metas. Alguns exemplos de aplicações de visualização de análise de dados multidimen- sionais vão de classificação de imagens, nuvens semântica de palavras, e análise de grupos de coleção de documentos, à exploração de conteúdo multimídia. Esta tese apresenta vários métodos de visualização para explorar de forma interativa conjuntos de dados multidimensionais que visam de usuários especializados aos casuais, fazendo uso de ambas representações estáticas e dinâmicas criadas por projeções multidimensionais. Primeiramente, apresentamos uma técnica de projeção multidimensional que preserva fielmente distância e que pode lidar com qualquer tipo de dados com alta-dimensionalidade, demonstrando cenários de aplicações em ambos os casos de multimídia e coleções de documentos de texto. Em seguida, abordamos a tarefa de interpretar as projeções em 2D, calculando erros de vizinhança. Posteriormente, apresentamos um conjunto de visualizações interativas que visam ajudar os usuários com essas tarefas, revelando a qualidade de uma projeção em 3D, aplicadas em diferentes cenários de alta dimensionalidade. Na parte final, discutimos duas abordagens diferentes para obter percepções sobre dados multimídia, em particular vídeos de futebol. Enquanto a primeira abordagem utiliza projeções multidimensionais, a segunda faz uso de uma eficiente metáfora visual para auxiliar usuários não especialistas em navegar e obter conhecimento em partidas de futebol.