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Activity-Based Costing is presented in accounting text books as a costing system that can be used to make valuable managerial decisions. Accounting journals regularly report the successful implementations and benefits of activity-based costing systems for particular businesses. Little experimental or empirical evidence exists, however, that has demonstrated the benefits of activity-based costing under controlled conditions. Similarly, although case studies report conditions that may or may not favor activity-based costing decision making, controlled studies that measure the actual influence of those conditions on the usefulness of activity-based costing information are few. This study looked at the decision usefulness of activity-based costing information under controlled, laboratory settings. An interactive computer simulation tested the ability of 48 accounting majors to optimize profits with and without activity-based costing information and tested to see if presentation format or decision framing would influence their outcomes. The research showed that the activity-based costing information resulted in significantly better profitability decisions and required no additional time. Presentation in graphic (bar charts) or numeric (tabular reports) format did not influence profitability decisions but the graphs took longer for analysis and decision making. Decision framing influences were shown to beneficially affect profitability decisions but did not require additional time. Decision framing was especially helpful with the non-activity based costing information; it had no significant effect on activity-based costing performance. / Ph. D.

This thesis describes two problem solving environments that integrate visualization and computational tools into a high level user interface. The objective of a problem solving environment is to provide scientists with a complete, usable, and integrated set of high level facilities for solving problems in a specific domain. Integrating visualization tools with computation tools encourages scientists to think in terms of the overall task of solving a problem, not simply using the visualization to view the results of the computation. This increases their productivity by allowing them to focus on the problem at hand rather than on general computation issues. Two problem solving environments based on this philosophy, but intended for different problem domains, are presented: VizCraft and WBCSim. VizCraft provides an integrated environment for aircraft designers working with multidimensional design spaces. The design problem currently being faced by aircraft designers, some approaches that have been taken in the past towards solving it, and how VizCraft provides a unique approach in helping the designer visualize the problem, are presented. WBCSim provides a Web-based framework for wood scientists conducting research on wood-based composite materials. It integrates legacy simulation codes with a graphical front end, an optimization tool, and a visualization tool. WBCSim serves as a prototype for the design, construction, and evaluation of larger scale problem solving (computing) environments. Several different wood-based composite material simulations are supported. / Master of Science

Network packet traces, despite having a lot of noise, contain priceless information, especially for investigating security incidents or troubleshooting performance problems. However, given the gigabytes of flow crossing a typical medium sized enterprise network every day, spotting malicious activity and analyzing trends in network behavior becomes a tedious task. Further, computational mechanisms for analyzing such data usually take substantial time to reach interesting patterns and often mislead the analyst into reaching false positives, benign traffic being identified as malicious, or false negatives, where malicious activity goes undetected. Therefore, the appropriate representation of network traffic data to the human user has been an issue of concern recently. Much of the focus, however, has been on visualizing TCP traffic alone while adapting visualization techniques for the data fields that are relevant to this protocol's traffic, rather than on the multivariate nature of network security data in general, and the fact that forensic analysis, in order to be fast and effective, has to take into consideration different parameters for each protocol. In this thesis, we bring together two powerful tools from different areas of application: SiLK (System for Internet-Level Knowledge), for command-based network trace analysis; and ComVis, a generic information visualization tool. We integrate the power of both tools by aiding simplified interaction between them, using a simple GUI, for the purpose of visualizing network traces, characterizing interesting patterns, and fingerprinting related activity. To obtain realistic results, we applied the visualizations on anonymized packet traces from Lawrence Berkley National Laboratory, captured on selected hours across three months. We used a sliding window approach in visually examining traces for two transport-layer protocols: ICMP and UDP. The main contribution of this research is a protocol-specific framework of visualization for ICMP and UDP data. We explored relevant header fields and the visualizations that worked best for each of the two protocols separately. The resulting views led us to a number of guidelines that can be vital in the creation of "smart books" describing best practices in using visualization and interaction techniques to maintain network security; while creating visual fingerprints which were found unique for individual types of scanning activity. Our visualizations use a multiple-views approach that incorporates the power of two-dimensional scatter plots, histograms, parallel coordinates, and dynamic queries. / Master of Science

Scientific visualization is primarily concerned with the visual presentation of three-dimensional phenomena in domains like medicine, meteorology, astrophysics, etc. The emphasis in scientific visualization research has been on the efficient rendering of measured or simulated data points, surfaces, volumes, and a time component to convey the dynamic nature of the studied phenomena. With the explosive growth in the size of the data, interactive visualization of scientific data becomes a real challenge. In recent years, the graphics community has witnessed tremendous improvements in the performance capabilities of graphics processing units (GPUs), and advances in GPU-accelerated rendering have enabled data exploration at interactive rates. Nevertheless, the majority of techniques rely on the assumption that a true three-dimensional geometric model capturing physical phenomena of interest, is available and ready for visualization. Unfortunately, this assumption does not hold true in many scientific domains, in which measurements are obtained from a given scanning modality at sparsely located intervals in both space and time. This calls for the fusion of data collected from multiple sources in order to fill the gaps and tell the story behind the data. For years, data fusion has relied on machine learning techniques to combine data from multiple modalities, reconstruct missing information, and track features of interest through time. However, these techniques fall short in solving the problem for datasets with large spatio-temporal gaps. This realization has led researchers in the data fusion domain to acknowledge the importance of human-in-the-loop methods where human expertise plays a major role in data reconstruction. This PhD research focuses on developing visualization and interaction techniques aimed at addressing some of the challenges that experts are faced with when analyzing the spatio-temporal behavior of physical phenomena. Given a number of datasets obtained from different measurement modalities and from simulation, we propose a generalized framework that can guide research in the field of multi-sensor data fusion and visualization. We advocate the use of GPU parallelism in our developed techniques in order to emphasize interaction as a key component in the successful exploration and analysis of multi-sourced data sets. The goal is to allow the user to create a mental model that captures their understanding of the spatio-temporal behavior of features of interest; one which they can test against real data measurements to verify their model. This model creation and verification is an iterative process in which the user interacts with the visualization, explores and builds an understanding of what occurred in the data, then tests this understanding against real-world measurements and improves it. We developed a system as a reference implementation of the proposed framework. Reconstructed data is rendered in a way that completes the users' cognitive model, which encodes their understanding of the phenomena in question with a high degree of accuracy. We tested the usability of the system and evaluated its support for this cognitive model construction process. Once an acceptable model is constructed, it is fed back to the system in the form of a reference dataset, which our framework uses to guide the real-time tracking of measurement data. Our results show that interactive exploration tasks enable the construction of this cognitive model and reference set, and that real-time interaction is achievable during the exploration, reconstruction, and enhancement of multi-modal time-variant three-dimensional data, by designing and implementing advanced GPU-based visualization techniques. / Ph. D.

Interactive visual analysis has been a key component of gaining insights in information visualization area. However, the amount of data has increased exponentially in the past few years. Existing information visualization techniques lack scalability to deal with big data, such as graphs with millions of nodes, or millions of multidimensional data records. Recently, the remarkable development of Graphics Processing Unit (GPU) makes GPU useful for general-purpose computation. Researchers have proposed GPU based solutions for visualizing big data in graphics and scientific visualization areas. However, GPU based big data solutions in information visualization area are not well investigated. In this thesis, I concentrate on the visualization of big data in information visualization area. More specifically, I focus on visual exploration of large graphs and multidimensional datasets based on the GPU technology. My work demonstrates that GPU based methods are useful for sensemaking of big data in information visualization area. / Master of Science

Current research suggests that by actively involving students, you can increase pedagogical value of algorithm visualizations. We believe that a pedagogically successful visualization, besides actively engaging participants, also requires certain other key features. We compared several existing algorithm visualizations for the purpose of identifying features that we believe increase the pedagogical value of an algorithm visualization. To identify the most important features from this list, we conducted two experiments using a variety of the heapsort algorithm visualizations. The results of these experiments indicate that the single most important feature is the ability to control the pace of the visualization. Providing a good data set that covers all the special cases is important to help students comprehend an unfamiliar algorithm. An algorithm visualization having minimum features that focuses on the logical steps of an algorithm is sufficient for procedural understanding of the algorithm. To have better conceptual understanding, additional features (like an activity guide that makes students cover the algorithm in detail and analyze what they are doing, and pseudocode display of an algorithm) may prove to be helpful, but that is a much harder effect to detect. / Master of Science

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.

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

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.