The advances in technology for creation, storage and dissemination of data have dramatically increased the need for tools that effectively provide users with means of identifying and understanding relevant information. Despite the great computing opportunities distributed frameworks such as Hadoop provide, it has only increased the need for means of identifying and understanding relevant information. Formal Concept Analysis (FCA) may play an important role in this context, by employing more intelligent means in the analysis process. FCA provides an intuitive understanding of generalization and specialization relationships among objects and their attributes in a structure known as a concept lattice. The present thesis addresses the problem of mining and visualising concepts over a data stream. The proposed approach is comprised of several distributed components that carry the computation of concepts from a basic transaction, filter and transforms data, stores and provides analytic features to visually explore data. The novelty of our work consists of: (i) a distributed processing and analysis architecture for mining concepts in real-time; (ii) the combination of FCA with visual analytics visualisation and exploration techniques, including association rules analytics; (iii) new algorithms for condensing and filtering conceptual data and (iv) a system that implements all proposed techniques, called Cubix, and its use cases in Biology, Complex System Design and Space Applications.

This dissertation focuses on developing imaging informatics algorithms for clinical decision support systems (CDSSs) based on histopathological whole-slide images (WSIs). Currently, histopathological analysis is a common clinical procedure for diagnosing cancer presence, type, and progression. While diagnosing patients using biopsy slides, pathologists manually assess nuclear morphology. However, making decisions manually from a slide with millions of nuclei can be time-consuming and subjective. Researchers have proposed CDSSs that help in decision making but they have limited reproducibility. The development of robust CDSSs for WSIs faces several informatics challenges: (1) Lack of robust segmentation methods for histopathological images, (2) Semantic gap between quantitative information and pathologist’s knowledge, (3) Lack of batch-invariant imaging informatics methods, (4) Lack of knowledge models for capturing informative patterns in large WSIs, and (5) Lack of guidelines for optimizing and validating diagnostic models. I conducted advanced imaging informatics research to overcome these challenges and developed novel methods to extract information from WSIs, to model knowledge embedded in large histopathological datasets, such as The Cancer Genome Atlas (TCGA), and to assist decision making with biological and clinical validation. I validated my methods for two applications: (1) diagnosis of histopathology-based endpoints such as subtype and grade and (2) prediction of clinical endpoints such as metastasis, stage, lymphnode spread, and survival. The statistically emergent feature subsets in the diagnostic models for histopathology-based endpoints were concordant with pathologists’ knowledge.

As estrategias de passes têm sido sempre de interesse para a pesquisa de futebol. Desde os inícios do futebol, os técnicos tem usado olheiros, gravações de vídeo, exercícios de treinamento e feeds de dados para coletar informações sobre as táticas e desempenho dos jogadores. No entanto, a natureza dinâmica das estratégias de passes são bastante complexas para refletir o que está acontecendo dentro do campo e torna difícil o entendimento do jogo. Além disso, existe uma demanda crecente pela deteção de padrões e analise de estrategias de passes popularizado pelo tiki-taka utilizado pelo FC. Barcelona. Neste trabalho, propomos uma abordagem para abstrair as sequências de pases e agrupálas baseadas na geometria da trajetória da bola. Para analizar as estratégias de passes, apresentamos um esquema de visualização interátiva para explorar a frequência de uso, a localização espacial e ocorrência temporal das sequências. A visualização Frequency Stripes fornece uma visão geral da frequencia dos grupos achados em tres regiões do campo: defesa, meio e ataque. O heatmap de trajetórias coordenado com a timeline de passes permite a exploração das formas mais recorrentes no espaço e tempo. Os resultados demostram oito trajetórias comunes da bola para sequências de três pases as quais dependem da posição dos jogadores e os ângulos de passe. Demonstramos o potencial da nossa abordagem com utilizando dados de várias partidas do Campeonato Brasileiro sob diferentes casos de estudo, e reportamos os comentários de especialistas em futebol. / Passing strategies analysis has always been of interest for soccer research. Since the beginning of soccer, managers have used scouting, video footage, training drills and data feeds to collect information about tactics and player performance. However, the dynamic nature of passing strategies is complex enough to reflect what is happening in the game and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing sequence analysis popularized by FC Barcelona’s tiki-taka. We propose an approach to abstract passing strategies and group them based on the geometry of the ball trajectory. To analyse passing sequences, we introduce a interactive visualization scheme to explore the frequency of usage, spatial location and time occurrence of the sequences. The frequency stripes visualization provide, an overview of passing groups frequency on three pitch regions: defense, middle, attack. A trajectory heatmap coordinated with a passing timeline allow, for the exploration of most recurrent passing shapes in temporal and spatial domains. Results show eight common ball trajectories for three-long passing sequences which depend on players positioning and on the angle of the pass. We demonstrate the potential of our approach with data from the Brazilian league under several case studies, and report feedback from a soccer expert.

Phenology studies recurrent periodic phenomena of plants and their relationship to environmental conditions. Monitoring forest ecosystems using digital cameras allows the study of several phenological events, such as leaf expansion or leaf fall. Since phenological phenomena are cyclic, the comparative analysis of successive years is capable of identifying interesting variation on annual patterns. However, the number of images collected rapidly gets significant since the goal is to compare data from several years. Instead of performing the analysis over images, experts prefer to use derived statistics (such as average values). We propose PhenoVis, a visual analytics tool that provides insightful ways to analyze phenological data. The main idea behind PhenoVis is the Chronological Percentage Maps (CPMs), a visual mapping that offers a summary view of one year of phenological data. CPMs are highly customizable, encoding more information about the images using a pre-defined histogram, a mapping function that translates histogram values into colors, and a normalized stacked bar chart to display the results. PhenoVis supports different color encodings, visual pattern analysis over CPMs, and similarity searches that rank vegetation patterns found at various time periods. Results for datasets comprising data of up to nine consecutive years show that PhenoVis is capable of finding relevant phenological patterns along time. Fenologia estuda os fenômenos recorrentes e periódicos que ocorrem com as plantas. Estes podem vir a ser relacionados com as condições ambientais. O monitoramento de florestas, através de câmeras, permite o estudo de eventos fenológicos como o crescimento e queda de folhas. Uma vez que os fenômenos fenológicos são cíclicos, análises comparativas de anos sucessivos podem identificar variações interessantes no comportamento destes. No entanto, o número de imagens cresce rapidamente para que sejam comparadas lado a lado. PhenoVis é uma ferramenta para análise visual que apresenta formas para analisar dados fenológicos através de comparações estatísticas (preferência dos especialistas) derivadas dos valores dos pixels destas imagens. A principal ideia por trás de PhenoVis são os mapas percentuais cronológicos (CPMs), um mapeamento visual com uma visão resumida de um período de um ano de dados fenológicos. CPMs são personalizáveis e conseguem representar mais informações sobre as imagens do que um gráfico de linha comum. Isto é possível pois o processo envolve o uso de histogramas pré-definidos, um mapeamento que transforma valores em cores e um empilhamento dos mapas de percentagem que visa a criação da CPM. PhenoVis suporta diferentes codificações de cores e análises de padrão visual sobre as CPMs. Pesquisas de similaridade ranqueiam padrões parecidos encontrados nos diferentes anos. Dados de até nove anos consecutivos mostram que PhenoVis é capaz de encontrar padrões fenológicos relevantes ao longo do tempo.

abstract: Intelligence analysts’ work has become progressively complex due to increasing security threats and data availability. In order to study “big” data exploration within the intelligence domain the intelligence analyst task was abstracted and replicated in a laboratory (controlled environment). Participants used a computer interface and movie database to determine the opening weekend gross movie earnings of three pre-selected movies. Data consisted of Twitter tweets and predictive models. These data were displayed in various formats such as graphs, charts, and text. Participants used these data to make their predictions. It was expected that teams (a team is a group with members who have different specialties and who work interdependently) would outperform individuals and groups. That is, teams would be significantly better at predicting “Opening Weekend Gross” than individuals or groups. Results indicated that teams outperformed individuals and groups in the first prediction, under performed in the second prediction, and performed better than individuals in the third prediction (but not better than groups). Insights and future directions are discussed. / Dissertation/Thesis / Masters Thesis Engineering 2016

abstract: Predictive analytics embraces an extensive area of techniques from statistical modeling to machine learning to data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline under the underlying assumption that a human-in-the-loop can aid the analysis by integrating domain knowledge that might not be broadly captured by the system. Primary uses of visualization in the predictive analytics pipeline have focused on data cleaning, exploratory analysis, and diagnostics. More recently, numerous visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent-specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end-users to understand and engage with the modeling process. However, despite the numerous and promising applications of visual analytics to predictive analytics tasks, work to assess the effectiveness of predictive visual analytics is lacking. This thesis studies the current methodologies in predictive visual analytics. It first defines the scope of predictive analytics and presents a predictive visual analytics (PVA) pipeline. Following the proposed pipeline, a predictive visual analytics framework is developed to be used to explore under what circumstances a human-in-the-loop prediction process is most effective. This framework combines sentiment analysis, feature selection mechanisms, similarity comparisons and model cross-validation through a variety of interactive visualizations to support analysts in model building and prediction. To test the proposed framework, an instantiation for movie box-office prediction is developed and evaluated. Results from small-scale user studies are presented and discussed, and a generalized user study is carried out to assess the role of predictive visual analytics under a movie box-office prediction scenario. / Dissertation/Thesis / Doctoral Dissertation Engineering 2017

As estrategias de passes têm sido sempre de interesse para a pesquisa de futebol. Desde os inícios do futebol, os técnicos tem usado olheiros, gravações de vídeo, exercícios de treinamento e feeds de dados para coletar informações sobre as táticas e desempenho dos jogadores. No entanto, a natureza dinâmica das estratégias de passes são bastante complexas para refletir o que está acontecendo dentro do campo e torna difícil o entendimento do jogo. Além disso, existe uma demanda crecente pela deteção de padrões e analise de estrategias de passes popularizado pelo tiki-taka utilizado pelo FC. Barcelona. Neste trabalho, propomos uma abordagem para abstrair as sequências de pases e agrupálas baseadas na geometria da trajetória da bola. Para analizar as estratégias de passes, apresentamos um esquema de visualização interátiva para explorar a frequência de uso, a localização espacial e ocorrência temporal das sequências. A visualização Frequency Stripes fornece uma visão geral da frequencia dos grupos achados em tres regiões do campo: defesa, meio e ataque. O heatmap de trajetórias coordenado com a timeline de passes permite a exploração das formas mais recorrentes no espaço e tempo. Os resultados demostram oito trajetórias comunes da bola para sequências de três pases as quais dependem da posição dos jogadores e os ângulos de passe. Demonstramos o potencial da nossa abordagem com utilizando dados de várias partidas do Campeonato Brasileiro sob diferentes casos de estudo, e reportamos os comentários de especialistas em futebol. / Passing strategies analysis has always been of interest for soccer research. Since the beginning of soccer, managers have used scouting, video footage, training drills and data feeds to collect information about tactics and player performance. However, the dynamic nature of passing strategies is complex enough to reflect what is happening in the game and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing sequence analysis popularized by FC Barcelona’s tiki-taka. We propose an approach to abstract passing strategies and group them based on the geometry of the ball trajectory. To analyse passing sequences, we introduce a interactive visualization scheme to explore the frequency of usage, spatial location and time occurrence of the sequences. The frequency stripes visualization provide, an overview of passing groups frequency on three pitch regions: defense, middle, attack. A trajectory heatmap coordinated with a passing timeline allow, for the exploration of most recurrent passing shapes in temporal and spatial domains. Results show eight common ball trajectories for three-long passing sequences which depend on players positioning and on the angle of the pass. We demonstrate the potential of our approach with data from the Brazilian league under several case studies, and report feedback from a soccer expert.

Phenology studies recurrent periodic phenomena of plants and their relationship to environmental conditions. Monitoring forest ecosystems using digital cameras allows the study of several phenological events, such as leaf expansion or leaf fall. Since phenological phenomena are cyclic, the comparative analysis of successive years is capable of identifying interesting variation on annual patterns. However, the number of images collected rapidly gets significant since the goal is to compare data from several years. Instead of performing the analysis over images, experts prefer to use derived statistics (such as average values). We propose PhenoVis, a visual analytics tool that provides insightful ways to analyze phenological data. The main idea behind PhenoVis is the Chronological Percentage Maps (CPMs), a visual mapping that offers a summary view of one year of phenological data. CPMs are highly customizable, encoding more information about the images using a pre-defined histogram, a mapping function that translates histogram values into colors, and a normalized stacked bar chart to display the results. PhenoVis supports different color encodings, visual pattern analysis over CPMs, and similarity searches that rank vegetation patterns found at various time periods. Results for datasets comprising data of up to nine consecutive years show that PhenoVis is capable of finding relevant phenological patterns along time. Fenologia estuda os fenômenos recorrentes e periódicos que ocorrem com as plantas. Estes podem vir a ser relacionados com as condições ambientais. O monitoramento de florestas, através de câmeras, permite o estudo de eventos fenológicos como o crescimento e queda de folhas. Uma vez que os fenômenos fenológicos são cíclicos, análises comparativas de anos sucessivos podem identificar variações interessantes no comportamento destes. No entanto, o número de imagens cresce rapidamente para que sejam comparadas lado a lado. PhenoVis é uma ferramenta para análise visual que apresenta formas para analisar dados fenológicos através de comparações estatísticas (preferência dos especialistas) derivadas dos valores dos pixels destas imagens. A principal ideia por trás de PhenoVis são os mapas percentuais cronológicos (CPMs), um mapeamento visual com uma visão resumida de um período de um ano de dados fenológicos. CPMs são personalizáveis e conseguem representar mais informações sobre as imagens do que um gráfico de linha comum. Isto é possível pois o processo envolve o uso de histogramas pré-definidos, um mapeamento que transforma valores em cores e um empilhamento dos mapas de percentagem que visa a criação da CPM. PhenoVis suporta diferentes codificações de cores e análises de padrão visual sobre as CPMs. Pesquisas de similaridade ranqueiam padrões parecidos encontrados nos diferentes anos. Dados de até nove anos consecutivos mostram que PhenoVis é capaz de encontrar padrões fenológicos relevantes ao longo do tempo.

abstract: The ability to design high performance buildings has acquired great importance in recent years due to numerous federal, societal and environmental initiatives. However, this endeavor is much more demanding in terms of designer expertise and time. It requires a whole new level of synergy between automated performance prediction with the human capabilities to perceive, evaluate and ultimately select a suitable solution. While performance prediction can be highly automated through the use of computers, performance evaluation cannot, unless it is with respect to a single criterion. The need to address multi-criteria requirements makes it more valuable for a designer to know the "latitude" or "degrees of freedom" he has in changing certain design variables while achieving preset criteria such as energy performance, life cycle cost, environmental impacts etc. This requirement can be met by a decision support framework based on near-optimal "satisficing" as opposed to purely optimal decision making techniques. Currently, such a comprehensive design framework is lacking, which is the basis for undertaking this research. The primary objective of this research is to facilitate a complementary relationship between designers and computers for Multi-Criterion Decision Making (MCDM) during high performance building design. It is based on the application of Monte Carlo approaches to create a database of solutions using deterministic whole building energy simulations, along with data mining methods to rank variable importance and reduce the multi-dimensionality of the problem. A novel interactive visualization approach is then proposed which uses regression based models to create dynamic interplays of how varying these important variables affect the multiple criteria, while providing a visual range or band of variation of the different design parameters. The MCDM process has been incorporated into an alternative methodology for high performance building design referred to as Visual Analytics based Decision Support Methodology [VADSM]. VADSM is envisioned to be most useful during the conceptual and early design performance modeling stages by providing a set of potential solutions that can be analyzed further for final design selection. The proposed methodology can be used for new building design synthesis as well as evaluation of retrofits and operational deficiencies in existing buildings. / Dissertation/Thesis / M.S. Architecture 2013

Multidimensional datasetsare increasingly more prominent and important in data science and many application domains. Such datasets typically consist of a large set of observations, or data points, each which is described by several measurements, or dimensions. During the design of techniques and tools to process such datasets, a key component is to gather insights into their structure and patterns, a goal which is targeted by multidimensional visualization methods. Structures and patterns of high-dimensional data can be described, at a core level, by the notion of similarity of observations. Hence, to visualize such patterns, we need effective and efficient ways to depict similarity relations between a large number of observations, each having a potentially large number of dimensions. Within the realm of multidimensional visualization methods, two classes of techniques exist projections and similarity trees which effectively capture similarity patterns and also scale well to the number of observations and dimensions of the data. However, while such techniques show similarity patterns, understanding and interpreting these patterns in terms of the original data dimensions is still hard. This thesis addresses the development of visual explanatory techniques for the easy interpretation of similarity patterns present in multidimensional projections and similarity trees, by several contributions. First, we proposemethodsthat make the computation of similarity treesefficient for large datasets, and also allow their visual explanation on a multiscale, or several levels of detail. We also propose ways to construct simplified representations of similarity trees, thereby extending their visual scalability even further. Secondly, we propose methods for the visual explanation of multidimensional projections in terms of automatically detected groups of related observations which are also automatically annotated in terms of their similarity in the high-dimensional data space. We show next how these explanatory mechanismscan be adapted to handle both static and time-dependent multidimensional datasets. Our proposed techniques are designed to be easy to use, work nearly automatically, handle any typesof quantitativemultidimensional datasets and multidimensional projection techniques, and are demonstrated on a variety of real-world large datasets obtained from image collections, text archives, scientific measurements, and software engineeering. / Conjuntos de dados multidimensionais são cada vez mais proeminentes e importantes em data science e muitos domínios de aplicação. Esses conjuntos de dados são tipicamente constituídos de um grande número de observações, ou objetos, cada qual descrito por várias medidas, ou dimensões. Durante o projeto de técnicas e ferramentas para processar tais dados, um dos focos principais é prover meios para análise e levantamento de hipóteses a partir das principais estruturas e padrões. Esse objetivo é perseguido por métodos de visualização multidimensional. Estruturas e padrões em dados multidimensionais podem ser descritos, em linhas gerais, pela noção de similaridade das observações. Portanto, para visualizar esses padrões, precisamos de meios efetivos e eficientes para retratar relações de similaridade dentre um grande número de observações, que potencialmente possuem um grande número de dimensões cada. No contexto dos métodos de visualização multidimensional, existem duas categorias de técnicas projeções e árvores de similaridade que efetivamente capturam padrões de similaridade e oferecem boa escalabilidade, tanto para o número de observações e quanto de dimensões. No entanto, embora essas técnicas exibam padrões de similaridade, o entendimento e interpretação desses padrões, em termos das dimensões originais dos dados, ainda é difícil. O trabalho desenvolvido nessa tese visa o desenvolvimento de técnicas explicativas para a fácil interpretação de padrões de similaridade presentes em projeções multidimensionais e árvores de similaridade. Primeiro, propomos métodos que possibilitam a computação eficiente de árvores de similaridade para grandes conjuntos de dados, e também a sua explicação visual em multiescala, ou seja, em vários níveis de detalhe. Também propomos modos de construir representações simplificadas de árvores de similaridade, e desse modo estender ainda mais a sua escalabilidade visual. Segundo, propomos métodos para explicar visualmente projeções multidimensionais em termos de grupos de observações relacionadas, detectadas e anotadas automaticamente para explicitar aspectos de sua similaridade no espaço de alta dimensionalidade. Mostramos em seguida como esses mecanismos explicativos podem ser adaptados para lidar com dados de natureza estática e dependentes no tempo. Nossas técnicas sã construídas visando fácil utilização, funcionamento semi automático, aplicação em quaisquer tipos de dados multidimensionais quantitativos e quaisquer técnicas de projeção multidimensional. Demonstramos a sua utilização em uma variedade de conjuntos de dados reais, obtidos a partir de coleções de imagens, arquivos textuais, medições científicas e de engenharia de software.