The current proliferation of connected displays and mobile devices from smart phones and tablets to wall-sized displays presents a number of exciting opportunities for information visualization and visual analytics. When a user employs heterogeneous displays collaboratively to achieve a goal, they form what is known as a display ecology. The display ecology enables multiple displays to function in concert within a broader technological environment to accomplish tasks and goals. However, since information and tasks are scattered and disconnected among separate displays, one of the inherent challenges associated with visual analysis in display ecologies is enabling users to seamlessly coordinate and subsequently connect and integrate information across displays. This research primarily addresses these challenges through the creation of interaction and visualization techniques and systems for display ecologies in order to support sensemaking with visual analysis. This dissertation explores essential visual analysis activities and design considerations for visual analysis in order to inform the new design of display ecologies for visual analysis. Based on identified design considerations, we then designed and developed two visual analysis systems. First, VisPorter supports intuitive gesture interactions for sharing and integrating information in a display ecology. Second, the Spatially Aware Visual Links (SAViL) presents a cross-display visual link technique capable of guiding the user's attention to relevant information across displays. It also enables the user to visually connect related information over displays in order to facilitate synthesizing information scattered over separate displays and devices. The various aspects associated with the techniques described herein help users to transform and empower the multiple displays in a display ecology for enhanced visual analysis and sensemaking. / Ph. D.

Exploring coordinated relationships is an important task in data analytics. For example, an intelligence analyst may want to find three suspicious people who all visited the same four cities. However, existing techniques that display individual relationships, such as between lists of entities, require repetitious manual selection and significant mental aggregation in cluttered visualizations to find coordinated relationships. This work presents a visual analytics approach that applies biclusters to support coordinated relationships exploration. Each computed bicluster aggregates individual relationships into coordinated sets. Thus, coordinated relationships can be formalized as biclusters. However, how to incorporate biclusters into a visual analytics tool to support sensemaking tasks is challenging. To address this, this work features three key contributions: 1) a five-level design framework for bicluster visualizations, 2) BiSet, highlighting bicluster-based edge bundling, seriation-based multiple lists ordering, and interactions for dynamic information foraging and management, and 3) an evaluation of BiSet. / Ph. D.

When exploring large, high-dimensional datasets, analysts often utilize two techniques for reducing the data to make exploration more tractable. The first technique, dimension reduction, reduces the high-dimensional dataset into a low-dimensional space while preserving high-dimensional structures. The second, clustering, groups similar observations while simultaneously separating dissimilar observations. Existing work presents a number of systems and approaches that utilize these techniques; however, these techniques can cooperate or conflict in unexpected ways. The core contribution of this work is the systematic examination of the design space at the intersection of dimension reduction and clustering when building intelligent, interactive tools in visual analytics. I survey existing techniques for dimension reduction and clustering algorithms in visual analytics tools, and I explore the design space for creating projections and interactions that include dimension reduction and clustering algorithms in the same visual interface. Further, I implement and evaluate three prototype tools that implement specific points within this design space. Finally, I run a cognitive study to understand how analysts perform dimension reduction (spatialization) and clustering (grouping) operations. Contributions of this work include surveys of existing techniques, three interactive tools and usage cases demonstrating their utility, design decisions for implementing future tools, and a presentation of complex human organizational behaviors. / Doctor of Philosophy / When an analyst is exploring a dataset, they seek to gain insight from the data. With data sets growing larger, analysts require techniques to help them reduce the size of the data while still maintaining its meaning. Two commonly-utilized techniques are dimension reduction and clustering. Dimension reduction seeks to eliminate unnecessary features from the data, reducing the number of columns to a smaller number. Clustering seeks to group similar objects together, reducing the number of rows to a smaller number. The contribution of this work is to explore how dimension reduction and clustering are currently being used in interactive visual analytics systems, as well as to explore how they could be used to address challenges faced by analysts in the future. To do so, I survey existing techniques and explore the design space for creating visualizations that incorporate both types of computations. I look at methods by which an analyst could interact with those projections in other to communicate their interests to the system, thereby producing visualizations that better match the needs of the analyst. I develop and evaluate three tools that incorporate both dimension reduction and clustering in separate computational pipelines. Finally, I conduct a cognitive study to better understand how users think about these operations, in order to create guidelines for better systems in the future.

With the dramatic increase and continued growth of digital information, developing Visual Analytic systems that support human cognition and insight generation are more necessary than ever before, but there is currently no content-agnostic method for measuring or com- paring how well a system facilitates analysis. Researchers in industry and academia are developing advanced tools that offer automated data analysis combined with support for human sense-making; tools for a wide variety of sense-making tasks are freely available. Now, the pressing question is: which tool works best, and for what? We show that using Shannon's entropy and self-information measures will provide a measure of the complexity reduction that results from an analyst's actions while sorting the information. Further, we demonstrate that reduced complexity can be linked to the knowledge gained. This is important, because a metric for objectively evaluating the success of current systems in generating insights would establish a standard that future tools could build on. This work could help guide researchers and developers in making the next generation of analytic tools, and in the age of big data the effect of such tools could potentially impact everyone. / Master of Science

With todays flood of data coming from many types of sources, Machine Learning becomes increasingly important. Though, many times the use of Machine Learning is not enough to make sense of all this data. This makes visualization a very useful tool for Machine Learning practitioners and data analysts alike. Interactive visualization techniques can be very helpful by giving insight on the meaning of the output from classification tasks. In this work, the aim is to explore, implement and evaluate different visualization techniques with the explicit goal of directly relating these visualization to the Machine Learning process. The proposed approach is the development of visualization techniques for a posteriori analysis that combines data exploration and classification evaluation. Results include a modified version of the Radial Visualization technique, called Dual RadViz, and also the use of interactive multiclass Partial Dependence Plots as means of finding counterfactual explanations about Machine Learning classification. An account of some of the many ways Machine Learning and visualization are used together is also given. / Hoje em dia, com o enorme fluxo de dados provenientes de muitos tipos de fontes, Aprendizado de Máquina se torna cada vez mais importante. No entanto, muitas vezes o uso de Aprendizado de Máquina não é o suficiente para que seja possível enxergar o valor e o significado de todos estes dados. Isso faz com que visualização seja uma valiosa ferramenta tanto para analistas de dados quanto para aqueles que praticam tarefas relacionadas à Aprendizado de Máquina. Técnicas de visualização interativa podem ser de grande utilidade por possibilitarem insights sobre o significado do resultado de tarefas de classificação. Neste trabalho, o objetivo é explorar, implementar e avaliar diferentes técnicas de visualização, explicitamente focando em suas relações com o processo de Aprendizado de Máquina. A abordagem proposta se trata do desenvolvimento de técnicas de visualização para análise a posteriori dos resultados de tarefas de classificação, combinando avaliação da classificação e exploração visual de dados. Os resultados incluem uma versão modificada da técnica de Visualização Radial, chamada Dual RadViz, e também o uso de Gráficos de Dependência Parcial multiclasse interativos como meio de se chegar à explicações contrafatuais sobre resultados de classificação. É dado também um relato de algumas das muitas maneiras onde Aprendizado de Máquina e visualização são usados conjuntamente.

Military acquisition programs have long been criticized for the exponential growth in program costs required to generate modest improvements in capability. One of the most promising reform efforts to address this trend is the open system architecture initiative, which uses modular design principles and commercial interface standards as a means to reduce the cost and complexity of upgrading systems over time. While conceptually simple, this effort has proven to be exceptionally difficult to implement in practice. This difficulty stems, in large part, from the fact that open systems trade additional cost and risk in the early phases of development for the option to infuse technology at a later date, but the benefits provided by this option are inherently uncertain. Practical implementation therefore requires a decision support framework to determine when these uncertain, future benefits are worth the cost and risk assumed in the present. The objective of this research is to address this gap by developing a method to measure the expected costs, benefits and risks associated with open systems. This work is predicated on three assumptions: (1) the purpose of future technology infusions is to keep pace with the uncertain evolution of operational requirements, (2) successful designs must justify how future upgrades will be used to satisfy these requirements, and (3) program managers retain the flexibility to adapt prior decisions as new information is made available over time. The analytical method developed in this work is then applied to an example scenario for an aerial Intelligence, Surveillance, and Reconnaissance platform with the potential to upgrade its sensor suite in future increments. Final results demonstrate that the relative advantages and drawbacks between open and integrated system architectures can be presented in the context of a cost-effectiveness framework that is currently used by acquisition professionals to manage complex design decisions.

Visualization systems traditionally focus on graphical representation of information. They tend not to provide integrated analytical services that could aid users in tackling complex knowledge discovery tasks. Users¡¯ exploration in such environments is usually impeded due to several problems: 1) Valuable information is hard to discover, when too much data is visualized on the screen. 2) They have to manage and organize their discoveries off line, because no systematic discovery management mechanism exists. 3) Their discoveries based on visual exploration alone may lack accuracy. 4) They have no convenient access to the important knowledge learned by other users. To tackle these problems, it has been recognized that analytical tools must be introduced into visualization systems. In this paper, we present a novel analysis-guided exploration system, called the Nugget Management System (NMS). It leverages the collaborative effort of human comprehensibility and machine computations to facilitate users¡¯ visual exploration process. Specifically, NMS first extracts the valuable information (nuggets) hidden in datasets based on the interests of users. Given that similar nuggets may be re-discovered by different users, NMS consolidates the nugget candidate set by clustering based on their semantic similarity. To solve the problem of inaccurate discoveries, data mining techniques are applied to refine the nuggets to best represent the patterns existing in datasets. Lastly, the resulting well-organized nugget pool is used to guide users¡¯ exploration. To evaluate the effectiveness of NMS, we integrated NMS into XmdvTool, a freeware multivariate visualization system. User studies were performed to compare the users¡¯ efficiency and accuracy of finishing tasks on real datasets, with and without the help of NMS. Our user studies confirmed the effectiveness of NMS. Keywords: Visual Analytics, Visual Knowledge

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.

Statistical flow data such as commuting, migration, trade and money flows has gained manyinterests from policy makers, city planners, researchers and ordinary citizens as well. Therehave appeared numerous statistical data visualisations; however, there is a shortage of applicationsfor visualising flow data. Moreover, among these rare applications, some are standaloneand only for expert usages, some do not support interactive functionalities, and somecan only provide an overview of data. Therefore, in this thesis, I develop a web-enabled,highly interactive and analysis support statistical flow data visualisation application that addressesall those challenges.My application is implemented based on GAV Flash, a powerful interactive visualisationcomponent framework, thus it is inherently web-enabled with basic interactive features. Theapplication uses visual analytics approach that combines both data analysis and interactivevisualisation to solve cluttering issue, the problem of overlapping flows on the display. A varietyof analysis means are provided to analyse flow data efficiently including analysing bothflow directions simultaneously, visualising time-series flow data, finding most attracting regionsand figuring out the reason behind derived patterns. The application also supportssharing knowledge between colleagues by providing story-telling mechanism which allowsusers to create and share their findings as a visualisation story. Last but not least, the applicationenables users to embed the visualisation based on the story into an ordinary web-pageso that public stand a golden chance to derive an insight into officially statistical flow data.

As more and more data is created each day, researchers from different science domains are trying to make sense of it. A lot of this data, for example our connections to friends on different social networking websites, can be modeled as graphs, where the nodes are actors and the edges are relationships between them. Researchers analyze this data to find new forms of communication, to explore different social groups or subgroups, to detect illegal activities or to seek for different communication patterns that could help companies in their marketing campaigns. Another example are huge networks in system biology. Their visualization is crucial for the understanding of living beings. The topological structure of a network on its own could give insight into the existence or distribution of interesting actors in the network. However, this is often not enough to understand complex network systems in real-world applications. The reason for this is that all the network elements (nodes or edges) are not simple one-dimensional data. For instance in biology, experiments can be performed on biological networks. These experiments and network analysis approaches produce additional data that are often important to be analyzed with respect to the underlying network structure. Therefore, it is crucial to visualize the additional attributes of the network while preserving the network structure as much as possible. The problem is not trivial as these so-called multivariate networks could have a high number of attributes that are related to their nodes, edges, different groups, or clusters of nodes and/or edges. The aim of this thesis is to contribute to the development of different visualization and interaction techniques for the visual analysis of multivariate networks. Two research goals are defined in this thesis: first, a deeper understanding of existing approaches for visualizing multivariate networks should be acquired in order to classify them into categories and to identify disadvantages or unsolved visualization challenges. The second goal is to develop visualization and interaction techniques that will overcome various issues of these approaches. Initially, a brief survey on techniques to visualize multivariate networks is presented in this thesis. Afterwards, a small task-based user study investigating the usefulness of two main approaches for multivariate network visualization is discussed. Then, various visualization and interaction techniques for multivariate network visualization are presented. Three different software tools were implemented to demonstrate our research efforts. All features of our systems are highlighted, including a description of visualization and interaction techniques as well as disadvantages and scalability issues if present.