The increasing number of web services published over the Web highlights the need for an effective method for users to find appropriate web services. Existing web service discovery methods do not effectively aid a user in finding suitable web services. The current methods provide textual lists of web services that the user is required to explore and manually evaluate. Thus, these methods lead to time-consuming and ineffective web service discovery. The aim of this research was to investigate using information visualization (IV) techniques to effectively support web service discovery. The node-and-link network IV technique was selected as the most appropriate IV technique to visualize web service collections. A prototype, called SerViz, was developed as a tool for interactive visualization of web service collections incorporating the node-and-link IV technique and an alphabetical list-based technique. SerViz used the Programmable Web web service collection as the sample web service collection. A usability evaluation was conducted to compare these techniques. Ninety percent of participants preferred the network IV technique for visualizing web service collections. The network IV technique was also faster for browsing. Several usability problems were identified with the network IV technique. This motivated a need for implementing an alternative IV technique in SerViz. The node-and-link tree IV technique was selected as it was more structured than the network IV technique. A usability evaluation was conducted to compare the network and tree IV techniques. Participants slightly preferred the tree IV technique as the technique to visualize web service collections. The tree IV technique was faster for browsing the web service collection while the network IV technique was faster for searching and filtering. This research has determined that IV techniques can be used to effectively support web service discovery. Future work will involve using IV techniques to support collaborative web service discovery. Keywords: Web Service Discovery, Information Visualization, Web Service Collections, Information Visualization Techniques.

Técnicas de visualização de informaçoes têm sido usadas para a representação de ontologias visando permitir a compreensão de conceitos e propriedades em domínios específicos. A visualização de ontologias deve ser baseada em representaccões gráficas efetivas e téquinas de interação que auxiliem tarefas de usuários relacionadas a diferentes entidades e aspectos. Ontologias podem ser complexas devido tanto à grande quantidade de níveis da hierarquia de classes como também aos diferentes atributos. Neste trabalho, propo˜e-se uma abordagem baseada no uso de múltiplas e coordenadas visualizações para explorar ambos os níceis intensional e extensional de uma ontologia. Para tanto, são empregadas estruturas visuais baseadas em árvores que capturam a característica hierárquiva de partes da ontologia enquanto preservam as diferentes categorias de classes. Além desta contribuição, propõe-se um inovador emprego do conceito "Degree of Interest" de modo a reduzir a complexidade da representação da ontologia ao mesmo tempo que procura direcionar a atenção do usuádio para os principais conceitos de uma determinada tarefa. Através da análise automáfica dos diferentes aspectos da ontologia, o principal conceito é colocado em foco, distinguindo-o, assim, da informação desnecessária e facilitando a análise e o entendimento de dados correlatos. De modo a sincronizar as visualizações propostas, que se adaptam facilmente às tarefas de usuários, e implementar esta nova proposta de c´calculo baseado em "Degree of Interest", foi desenvolvida uma ferramenta de visualização de ontologias interativa chamada OntoViewer, cujo desenvolvimento seguiu um ciclo interativo baseado na coleta de requisitos e avaliações junto a usuários em potencial. Por fim, uma última contribuição deste trabalho é a proposta de um conjunto de "guidelines"visando auxiliar no projeto e na avaliação de téncimas de visualização para os níceis intensional e extensional de ontologias. / Visualization techniques have been used for the representation of ontologies to allow the comprehension of concepts and properties in specific domains. Techniques for visualizing ontologies should be based on effective graphical representations and interaction techniques that support users tasks related to different entities and aspects. Ontologies can be very large and complex due to many levels of classes’ hierarchy as well as diverse attributes. In this work we propose a multiple, coordinated views approach for exploring the intensional and extensional levels of an ontology. We use linked tree structures that capture the hierarchical feature of parts of the ontology while preserving the different categories of classes. We also present a novel use of the Degree of Interest notion in order to reduce the complexity of the representation itself while drawing the user attention to the main concepts for a given task. Through an automatic analysis of ontology aspects, we place the main concept in focus, distinguishing it from the unnecessary information and facilitating the analysis and understanding of correlated data. In order to synchronize the proposed views, which can be easily adapted to different user tasks, and implement this new Degree of Interest calculation, we developed an interactive ontology visualization tool called OntoViewer. OntoViewer was developed following an iterative cycle of refining designs and getting user feedback, and the final version was again evaluated by ten experts. As another contribution, we devised a set of guidelines to help the design and evaluation of visualization techniques for both the intensional and extensional levels of ontologies.

The large amount of available data on a diverse range of human activities provides many opportunities for understanding, improving and revealing unknown patterns in them. Powerful automatic methods for extracting this knowledge from data are already available from machine learning and data mining. They, however, rely on the expertise of analysts to improve their results when those are not satisfactory. In this context, interactive multidimensional projections are a useful tool for the analysis of multidimensional data by revealing their underlying structure while allowing the user to manipulate the results to provide further insight into this structure. This manipulation, however, has received little attention regarding their influence on the mappings, as they can change the final layout in unpredictable ways. This is the main motivation for this research: understanding the effects caused by changes in these mappings. We approach this problem from two perspectives. First, the user perspective, we designed and developed visualizations that help reduce the trial and error in this process by providing the right piece of information for performing manipulations. Furthermore, these visualizations help explain the changes in the map caused by such manipulations. Second, we defined the effectiveness of manipulation in quantitative terms, then developed an experimental framework for assessing manipulations in multidimensional projections under this view. This framework is based on improving mappings using known evaluation measures for these techniques. Using the improvement of measures as different types of manipulations, we perform a series of experiments on five datasets, five measures, and four techniques. Our experimental results show that there are possible types of manipulations that can happen effectively, with some techniques being more susceptible to manipulations than others. / O grande volume de dados disponíveis em uma diversa gama de atividades humanas cria várias oportunidades para entendermos, melhorarmos e revelarmos padrões previamente desconhecidos em tais atividades. Métodos automáticos para extrair esses conhecimentos a partir de dados já existem em áreas como aprendizado de máquina e mineração de dados. Entretanto, eles dependem da perícia do analista para obter melhores resultados quando estes não são satisfatórios. Neste contexto, técnicas de projeção multidimensional interativas são uma ferramenta útil para a análise de dados multidimensionais, revelando sua estrutura subjacente ao mesmo tempo que permite ao analista manipular os resultados interativamente, estendendo o processo de exploração. Essa interação, entretanto, não foi estudada com profundidade com respeito à sua real influência nos mapeamentos, já que podem causar mudanças não esperadas no mapeamento final. Essa é a principal motivação desta pesquisa: entender os efeitos causados pelas mudanças em tais mapeamentos. Abordamos o problema de duas perspectivas. Primeiro, da perspectiva do usuário, desenvolvemos visualizações que ajudam a diminuir tentativas e erros neste processo provendo a informação necessária a cada passo da interação. Além disso, essas visualizações ajudam a explicar as mudanças causadas no mapeamento pela manipulação. A segunda perspectiva é a efetividade da manipulação. Definimos de forma quantitativa a efetividade da manipulação, e então desenvolvemos um arcabouço para avaliar manipulações sob a visão da efetividade. Este arcabouço é baseado em melhorias nos mapeamentos usando medidas de avaliação conhecidas para tais técnicas. Usando tais melhorias como diferentes formas de manipulação, realizamos uma série de experimentos em cinco bases de dados, cinco medidas e quatro técnicas. Nossos resultados experimentais nos dão evidências que existem certos tipos de manipulação que podem acontecer efetivamente, com algumas técnicas sendo mais suscetíveis a manipulações do que outras.

<div>Recent advancements in data-collecting technologies have posed new opportunities and challenges to making data-driven decisions. While visual analytics can be a powerful tool for exploring large datasets and extracting relevant insights to support data-driven decisions, many decision-makers lack the time or the technical expertise to utilize visual analytics effectively. It is more common for data analysts to explore data through visual analytics and report their findings to the decision-makers. However, the communication gap between data analysts and decision-makers limits the decision-maker's ability to make optimal data-driven decisions. I present a Three-Component Visual Summary to allow accurate and efficient extraction of insights relevant to the decisions and provide context to validate the insights retrieved. The Three-Component Visual Summary design creates visual summaries by combining visual representations of representative data, analytical highlights, and the data envelope. This design incorporates a high-level summary, the relevant analytical insights, and detailed explorations into one coherent visual representation which addresses the potential training gaps and limited available time for visual analytics. I demonstrate how the design can be applied to four major data types commonly used in commercial visual analytics tools. The evaluations prove the design allows more accurate and efficient knowledge retrieval and a more comprehensive understanding of the data and of the insights generated, making it more accessible to decision-makers that are casual experts. Finally, I summarize the insights gained from the design process and the feedback received, and provide a list of recommendations for designing a Three-Component Visual Summary.</div>

In producing a user interface design to visualize search results for a digital library called Envision [Nowell, France, Hix, Heath, &amp; Fox, 1996] [Fox, Hix, Nowell, et al., 1993] [Nowell &amp; Hix, 1993], we found that choosing graphical devices and document attributes to be encoded with each graphical device is a surprisingly difficult task. By <i>graphical devices</i> we mean those visual display elements (e.g., color, shape, size, position, etc.) used to convey encoded, semantic information. Research in the areas of psychophysics of visual search and identification tasks, graphical perception, and graphical language development provides scientific guidance for design and evaluation of graphical encodings which might otherwise be reduced to opinion and personal taste. However, literature offers inconclusive and often conflicting viewpoints, suggesting a need for further research. The goal of this research was to determine empirically the effectiveness of graphical devices for encoding nominal and quantitative information in complex visualization displays. Using the Envision Graphic View, we conducted a within-subjects empirical investigation of the effectiveness of three graphical devices - <i>icon color, icon shape,</i> and <i>icon size</i> - in communicating nominal (document type) and quantitative (document relevance) data. Our study provides empirical evidence regarding the relative effectiveness of icon color, shape, and size for conveying both nominal and quantitative data. While our studies consistently rank color as most effective, the rankings differ for shape and size. For nominal data, icon shape ranks ahead of icon size by all measures except time for task completion, which places shape behind size. For quantitative data, we found, by all measures, that encodings with icon shape are more effective than with icon size. We conclude that the <i>nature of tasks</i> performed and the relative <i>importance of measures of effectiveness</i> are more significant than the type of data represented for designers choosing among rankings. / Ph. D.

The purpose of this study is to explore the feasibility and effectiveness of a scalable concept map based navigation system for a digital textbook. A literature review has been conducted to identify possible methods to promote conceptual understanding in the context of a digital textbook, and these hypothesized solutions will be evaluated through a prototype tool. The primary method that has been selected for this study to promote conceptual understanding in textbooks is the concept map. When concept maps are used as advance organizers or navigation aids for hypermedia documents, they have been shown to promote conceptual understanding. Issues with scalability exist, however. When maps become too large or complicated, a phenomenon labeled "map-shock" occurs. Map-shock is a result of cognitive overload that nullifies the positive effects the concept map has on learning. In order to eliminate map-shock, one needs to manage the cognitive load imposed on the learner. This project proposes using information visualization techniques leveraged from the computer science domain to develop an interactive concept map based navigation system that will retain the positive effects of concept maps, and also present the visuals in a way that does not cognitively overload the user. This study seeks to answer the research question: "How can a large-scale concept map visualization tools be realized in a way that promotes conceptual understanding and manages cognitive load?" To answer the research question, a prototype tool, labeled the "Adaptive Map tool", was developed and populated with engineering statics content. This prototype contains content that is similar to the material in a traditional statics textbook, but the information is accessed through the proposed adaptive concept map visualization. The tool was then given to students in engineering statics class to be used as a supplemental textbook. The effects of the Adaptive Map tool were evaluated through a multiple case study approach that allowed researchers to understand how this tool fit into the larger learning context of a class. Results indicated that some students did integrate the Adaptive Map tool into the learning process, and furthermore that the tool did promote meaningful learning behaviors that lead to better conceptual understanding of the material. / Ph. D.

Research in automatic control has enabled trucks to use adaptive cruise control to drive very close to each other and form platoons. This reduces drag and improves efficiency by lowering fuel consumption. A central challenge to understanding the formation of these platoons is that not all trucks are emerging from the same origin or reaching the same destination; they only share parts of their joint trip. This study uses participatory design methodologies to create a design for an interactive visualization system to enable researchers to study the formation of platoons in simulated scenarios. Three transport researchers participated in interviews and a set of two workshops to establish their needs and formulate tasks that would improve their understanding of the simulations. The main research-through-design question was “when do platoons form and how large are they?” To forward and ground the discussion, I developed a prototype with increasing fidelity after each round of participatory design. The interface consists four panels: 1) a spatial panel that contains a map view; 2) a temporal panel with context and focus timelines: 3) an adaptation panel with details on inter-truck relationships; and 4) a filtering panel with a parallel coordinate system. The results indicate a need for a flexible interactive visualization system that enables researchers to study how trucks are affected by plan recalculations and how they adapt to their partners influencing the costs and benefits of platooning. / Forskning inom reglerteknik och fordonsstyrning har gett lastbilar och andra tunga fordon möjlighet använda adaptiv farthållning till att köra med ett litet mellanrum och bilda vägkolonner. De kan då utnyttja vindsuget från fordonet framför och på så vis sänka bränsleförbrukningen. En central utmaning i skapandet av dessa kolonner är att fordonen inte har gemensamma startpunkter och destinationer. De delar i de flesta fall endast stycken av sin rutt med andra fordon, och turerna behöver då sammanfalla i tid. Denna studie använder deltagande designmetodik för att designa ett interaktivt visualiseringsverktyg som kan hjälpa forskare att studera skapandet av lastbilskolonner i simulerade scenarion. Tre transportforskare deltog i intervjuer och två cykler av workshops för att synliggöra och formulera arbetsuppgifter som kunde förbättra deras förståelse av simulationerna. Den primära deltagande design-frågan var “när bildas kolonner och hur stora är de?” För att förankra och driva diskussionen kring designen framåt utvecklades en prototyp som viderutvecklades efter varje deltagande designcykel. Interfacet i den resulterande prototypen och består av fyra paneler: 1) en geografisk panel som innehåller en kartvy; 2) en panel med tidslinjer för både fokus och kontext; 3) en anpassningspanel med detaljer på fordonens relationer; och 4) en filtreringspanel med ett parallellt koordinatsystem. Resultatet av studien indikerar ett behov ett flexibel visuellt analysverktyg som tillåter forskare att studera hur fordonen påverkas av förändringar i resplaner och vilken anpassning som krävs för att möta upp andra fordon för kolonnbildning.

Society generates more and more data every day, and with competent ways to visualize it, we can learn new things about the world we live in. While traditional visualizations try to stay clear of 3D graphs because they are hard for a user to process without proper depth cues, VR technology allows us to better perceive 3D structures, but what happens to our overview of the data when we perceive and interact with it in 3D? 6 participants were subject to a 3D VR visualization of customer data with filtering possibilities, where they were to perform a series of short tasks as well as a more open-ended free form task. Qualitative data was gathered through extensive semi-structured interviews. Quantitative supportive data was gathered as well in the form of interaction logs, time to complete tasks and the Presence Questionnaire. After this first round of tests, feedback and data was compiled into a new version of the visualization that was then tested on seven new participants. Results showed that seeing the graph from different perspectives was important to gaining an overview of the 3D graph, but also to actively interact with the data helped participants gain an overview by digging deeper into the data. In order to dig into the data in the 3D graph, participants expressed that they would’ve liked to be able to compare subsets of the data. / Vi genererar ofantliga mängder data varje dag, men utan att visualisera denna data är det inte säkert att vi lär oss någonting av den. Med detta följer även ett behov av att visualisera denna data i flera dimensioner, något som varit svårt i informationsvisualiseringar tidigare då det är svårt att representera djup på en platt skärm så att människor förstår. Med VR-teknik kan vi däremot använda detta djupseende i informationsvisualiseringar, men vad händer med vår överblick av informationen när vi ser och interagerar med den i 3D?   6 testpersoner fick interagera med en 3D-visualisering av kunddata med filtreringsmöjligheter i VR och fick i denna utföra en serie mindre uppgifter samt en större, fri uppgift. Under testet samlades kvalitativ data in i form av utförliga semi-strukturerade intervjuer och kvantitativ supportdata i form av loggar av interaktioner, tid att slutföra uppgifter samt svar från Presence Questionnaire för alla testpersoner. Åsikterna från denna testrunda användes sedan för att skapa en ny version av 3Dvisualiseringen som sedan testades på 7 nya personer enligt samma studieformat.   Resultaten visade att det var viktigt att kunna se grafen från olika perspektiv för att få en överblick, såväl som att kunna aktivt filtrera och interagera med visualiseringen för att kunna gräva djupare i datan. För att kunna gräva djupare kände många testpersoner att de behövde ett sätt att enkelt kunna jämföra delmängder av datan med varandra.

No description available.

No description available.