El gran volumen de datos que las técnicas de minería Web generan sobre espacios Web puede llegar a ser muy difícil de entender, provocando la necesidad de desarrollar nuevas técnicas que permitan generar conocimiento sobre esos datos con el fin de facilitar la toma de decisiones. Esta tesis explora la utilización de técnicas de InfoVis/VA para ayudar en la exploración de espacios Web. Más concretamente, presentamos el desarrollo de un prototipo muy flexible que ha sido utilizado para analizar tres tipos distintos de espacios Web con distintas metas informacionales: el análisis de la usabilidad de páginas Web, la evaluación del comportamiento de los estudiantes en entornos virtuales de aprendizaje y la exploración de la estructura de grandes conversaciones asíncronas existentes en foros online. Esta tesis pretende aceptar el reto propuesto por la comunidad de InfoVis/VA de llevar a cabo investigaciones en condiciones más reales, introduciendo los problemas relacionados con el análisis de los espacios Web ya mencionados, y explorando las ventajas de utilizar las visualizaciones proporcionadas por nuestra herramienta con usuarios reales. / The vast amount of data that Web mining techniques generate from Web spaces is difficult to understand, suggesting the need to develop new techniques to gather insight into them in order to assist in decision making processes. This dissertation explores the usage of InfoVis/VA techniques to assist in the exploration of Web spaces. More specifically, we present the development of a customisable prototype that has been used to analyse three different types of Web spaces with different information goals: the analysis of the usability of a website, the assessment of the students in virtual learning environments, and the exploration of the structure of large asynchronous conversations existing in online forums. Echoing the call of the Infovis/VA community for the need for more research into realistic circumstances, we introduce the problems of the analysis of such Web spaces, and further explore the benefits of using the visualisations provided by our system with real users. / El gran volum de dades que les tècniques de mineria Web proporcionen sobre els espais Web és generalment molt difícil dʼentendre, provocant la necessitat de desenvolupar noves tècniques que permetin generar coneixement sobre les dades de manera que facilitin la presa de decissions. Aquesta tesi explora la utilizació de tècniques dʼInfovis/VA per ajudar en lʼexploració dʼespais Web. Més concretament, presentem el desenvolupament dʼun prototipus molt flexible que hem utilitzat per analitzar tres tipus diferents dʼespais Web amb diferents objectius informacionals: lʼanèlisi de la usabilitat de pàgines Web, lʼavaluació del comportament dels estudiants en entorns virtuals dʼaprenentatge i lʼexploració de lʼestructura de grans converses asíncrones existents en fòrums online. Aquesta tesi pretén acceptar el repte proposat per la comunitat dʼInfoVis/VA de fer recerca en condicions més reals, introduint els problemes relacionats en lʼanàlisi dels espais Web ja esmentats, i explorant els avantatges dʼutilizar les visualitzacions proporcionades per la nostra eina amb usuaris reals.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 78-80).

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.

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.

<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>

Software development is constantly evolving. This produces a lot of opportunities, but also confusion about what the best practices are. A rather unexplored area within software development is visual analytics for software testing. The goal of this thesis is to get an understanding of what role visual analytics can have within software testing. In this thesis, a literature review was used to gather information about analytical needs, tools, and other vital information about the subject. A survey towards practitioners was used to get information about the industry, the survey had questions about visual analytics, visualizations, and their potential roles.  We conclude that visual analytics of software testing results does have a role in software testing, mainly in a faster understanding of test results, the ability to produce big picture overviews, and supporting decision making.

In light of advancements made in data collection techniques over the past two decades, data mining has become common practice to summarize large, high dimensional datasets, in hopes of discovering noteworthy data structures. However, one concern is that most data mining approaches rely upon strict criteria that may mask information in data that analysts may find useful. We propose a new approach called Bayesian Visual Analytics (BaVA) which merges Bayesian Statistics with Visual Analytics to address this concern. The BaVA framework enables experts to interact with the data and the feature discovery tools by modeling the "sense-making" process using Bayesian Sequential Updating. In this paper, we use BaVA idea to enhance high dimensional visualization techniques such as Probabilistic PCA (PPCA). However, for real-world datasets, important structures can be arbitrarily complex and a single data projection such as PPCA technique may fail to provide useful insights. One way for visualizing such a dataset is to characterize it by a mixture of local models. For example, Tipping and Bishop [Tipping and Bishop, 1999] developed an algorithm called Mixture Probabilistic PCA (MPPCA) that extends PCA to visualize data via a mixture of projectors. Based on MPPCA, we developped a new visualization algorithm called Covariance-Guided MPPCA which group similar covariance structured clusters together to provide more meaningful and cleaner visualizations. Another way to visualize a very complex dataset is using nonlinear projection methods such as  the Generative Topographic Mapping algorithm(GTM). We developped an interactive version of GTM to discover interesting local data structures. We demonstrate the performance of our approaches using both synthetic and real dataset and compare our algorithms with existing ones. / Ph. D.

Keeping track of requirements from eliciting data to making decision needs an effective path from data to decision [43]. Visualization science helps to create this path by extracting insights from flood of data. Model helps to shape the transformation of data to visualization. Defect Detection and Prevention model was created to assess quality assurance activities. We selected DDP and started enhancing user interactivity with requirements visualization over basic DDP with implementing a visual requirements analytics framework. By applying GQM table to our framework, we added six visualization features to the existing visual requirements visualization approaches. We applied this framework to technical and non-technical stakeholder scenarios to gain the operational insights of requirements-driven risk mitigation in practice. The combination of the first and second scenarios' result presented the multiple stakeholders scenario result which was a small number of strategies from kept tradespase with common mitigations that must deploy to the system.

Illegal logging and timber trade presents a persistent threat to global biodiversity and national security due to its ties with illicit financial flows, and causes revenue loss. The scale of global commerce in timber and associated products, combined with the complexity and geographical spread of the supply chain entities present a non-trivial challenge in detecting such transactions. International shipment records, specifically those containing bill of lading is a key source of data which can be used to detect, investigate and act upon such transactions. The comprehensive problem can be described as building a framework that can perform automated discovery and facilitate actionability on detected transactions. A data driven machine learning based approach is necessitated due to the volume, velocity and complexity of international shipping data. Such an automated framework can immensely benefit our targeted end-users---specifically the enforcement agencies. This overall problem comprises of multiple connected sub-problems with associated research questions. We incorporate crucial domain knowledge---in terms of data as well as modeling---through employing expertise of collaborating domain specialists from ecological conservationist agencies. The collaborators provide formal and informal inputs spanning across the stages---from requirement specification to the design. Following the paradigm of similar problems such as fraud detection explored in prior literature, we formulate the core problem of discovering suspicious transactions as an anomaly detection task. The first sub-problem is to build a system that can be used find suspicious transactions in shipment data pertaining to imports and exports of multiple countries with different country specific schema. We present a novel anomaly detection approach---for multivariate categorical data, following constraints of data characteristics, combined with a data pipeline that incorporates domain knowledge. The focus of the second problem is U.S. specific imports, where data characteristics differ from the prior sub-problem---with heterogeneous attributes present. This problem is important since U.S. is a top consumer and there is scope of actionable enforcement. For this we present a contrastive learning based anomaly detection model for heterogeneous tabular data, with performance and scalability characteristics applicable to real world trade data. While the first two problems address the task of detecting suspicious trades through anomaly detection, a practical challenge with anomaly detection based systems is that of relevancy or scenario specific precision. The third sub-problem addresses this through a human-in-the-loop approach augmented by visual analytics, to re-rank anomalies in terms of relevance---providing explanations for cause of anomalies and soliciting feedback. The last sub-problem pertains to explainability and actionability towards suspicious records, through algorithmic recourse. Algorithmic recourse aims to provides meaningful alternatives towards flagged anomalous records, such that those counterfactual examples are not judged anomalous by the underlying anomaly detection system. This can help enforcement agencies advise verified trading entities in modifying their trading patterns to avoid false detection, thus streamlining the process. We present a novel formulation and metrics for this unexplored problem of algorithmic recourse in anomaly detection. and a deep learning based approach towards explaining anomalies and generating counterfactuals. Thus the overall research contributions presented in this dissertation addresses the requirements of the framework, and has general applicability in similar scenarios beyond the scope of this framework. / Doctor of Philosophy / Illegal timber trade presents multiple global challenges to ecological biodiversity, vulnerable ecosystems, national security and revenue collection. Enforcement agencies---the target end-users of this framework---face a myriad of challenges in discovering and acting upon shipments with illegal timber that violate national and transnational laws due to volume and complexity of shipment data, coupled with logistical hurdles. This necessitates an automated framework based upon shipment data that can address this task---through solving problems of discovery, analysis and actionability. The overall problem is decomposed into self contained sub-problems that address the associated specific research questions. These comprise of anomaly detection in multiple types of high dimensional tabular data, improving precision of anomaly detection through expert feedback and algorithmic recourse for anomaly detection. We present data mining and machine learning solutions to each of the sub-problems that overcome limitations and inapplicability of prior approaches. Further, we address two broader research questions. First is incorporation domain knowledge into the framework, which we accomplish through collaboration with domain experts from environmental conservation organizations. Secondly, we address the issue of explainability in anomaly detection for tabular data in multiple contexts. Such real world data presents with challenges of complexity and scalability, especially given the tabular format of the data that presents it's own set of challenges in terms of machine learning. The solutions presented to these machine learning problems associated with each of components of the framework provide an end-to-end solution to it's requirements. More importantly, the models and approaches presented in this dissertation have applicability beyond the application scenario with similar data and application specific challenges.

Recent advancements in data acquisition, storage, and computing power have enabled scientists from various scientific and engineering domains to simulate more complex and longer phenomena. Scientists are usually interested in understanding the behavior of a phenomenon in different conditions. To do so, they run multiple simulations with different configurations (i.e., parameter settings, boundary/initial conditions, or computational models), resulting in an ensemble dataset. An ensemble empowers scientists to quantify the uncertainty in the simulated phenomenon in terms of the variability between ensemble members, the parameter sensitivity and optimization, and the characteristics and outliers within the ensemble members, which could lead to valuable insight(s) about the simulated model. The size, complexity, and high dimensionality (e.g., simulation input and output parameters) of simulation ensembles pose a great challenge in their analysis and exploration. Ensemble visualization provides a convenient way to convey the main characteristics of the ensemble for enhanced understanding of the simulated model. The majority of the current ensemble visualization techniques are mainly focused on analyzing either the ensemble space or the parameter space. Most of the parameter space visualizations are not designed for high-dimensional data sets or did not show the intrinsic structures in the ensemble. Conversely, ensemble space has been visualized either as a comparative visualization of a limited number of ensemble members or as an aggregation of multiple ensemble members omitting potential details of the original ensemble. Thus, to unfold the full potential of simulation ensembles, we designed and developed an approach to the visual analysis of high-dimensional simulation ensembles that merges sensemaking, human expertise, and intuition with machine learning and statistics. In this work, we explore how semantic interaction and sensemaking could be used for building interactive and intelligent visual analysis tools for simulation ensembles. Specifically, we focus on the complex processes that derive meaningful insights from exploring and iteratively refining the analysis of high dimensional simulation ensembles when prior knowledge about ensemble features and correlations is limited or/and unavailable. We first developed GLEE (Graphically-Linked Ensemble Explorer), an exploratory visualization tool that enables scientists to analyze and explore correlations and relationships between non-spatial ensembles and their parameters. Then, we developed Spatial GLEE, an extension to GLEE that explores spatial data while simultaneously considering spatial characteristics (i.e., autocorrelation and spatial variability) and dimensionality of the ensemble. Finally, we developed Image-based GLEE to explore exascale simulation ensembles produced from in-situ visualization. We collaborated with domain experts to evaluate the effectiveness of GLEE using real-world case studies and experiments from different domains. The core contribution of this work is a visual approach that enables the exploration of parameter and ensemble spaces for 2D/3D high dimensional ensembles simultaneously, three interactive visualization tools that explore search, filter, and make sense of non-spatial, spatial, and image-based ensembles, and usage of real-world cases from different domains to demonstrate the effectiveness of the proposed approach. The aim of the proposed approach is to help scientists gain insights by answering questions or testing hypotheses about the different aspects of the simulated phenomenon or/and facilitate knowledge discovery of complex datasets. / Doctor of Philosophy / Scientists run simulations to understand complex phenomena and processes that are expensive, difficult, or even impossible to reproduce in the real world. Current advancements in high-performance computing have enabled scientists from various domains, such as climate, computational fluid dynamics, and aerodynamics to run more complex simulations than before. However, a single simulation run would not be enough to capture all features in a simulated phenomenon. Therefore, scientists run multiple simulations using perturbed input parameters, initial and boundary conditions, or different models resulting in what is known as an ensemble. An ensemble empowers scientists to understand the model's behavior by studying relationships between and among ensemble members, the optimal parameter settings, and the influence of input parameters on the simulation output, which could lead to useful knowledge and insights about the simulated phenomenon. To effectively analyze and explore simulation ensembles, visualization techniques play a significant role in facilitating knowledge discoveries through graphical representations. Ensemble visualization offers scientists a better way to understand the simulated model. Most of the current ensemble visualization techniques are designed to analyze or/and explore either the ensemble space or the parameter space. Therefore, we designed and developed a visual analysis approach for exploring and analyzing high-dimensional parameter and ensemble spaces simultaneously by integrating machine learning and statistics with sensemaking and human expertise. The contribution of this work is to explore how to use semantic interaction and sensemaking to explore and analyze high-dimensional simulation ensembles. To do so, we designed and developed a visual analysis approach that manifested in an exploratory visualization tool, GLEE (Graphically-Linked Ensemble Explorer), that allowed scientists to explore, search, filter, and make sense of high dimensional 2D/3D simulations ensemble. GLEE's visualization pipeline and interaction techniques used deep learning, feature extraction, spatial regression, and Semantic Interaction (SI) techniques to support the exploration of non-spatial, spatial, and image-based simulation ensembles. GLEE different visualization tools were evaluated with domain experts from different fields using real-world case studies and experiments.