Many people in widely varied fields are exposed to categorical data describing myriad observations. The breadth of applications in which categorical data are used means that many of the people tasked to apply these data have not been trained in data analysis. Visualization of data is often used to alleviate this problem since visualization can convey relevant information in a non-mathematical manner. However, visualizations are frequently static and the tools to create them are largely geared towards quantitative data. It is the purpose of this thesis to demonstrate a method which expands on the parallel coordinates method of visualization and uses a 'Google Maps' style of interaction and view dependent data presentation for visualizing and exploring categorical data that is accessible by non-experts and promotes the use of domain specific knowledge. The parallel coordinates method has enjoyed increasing popularity in recent times, but has several shortcomings. This thesis seeks to address some of these problems in a manner which involves not just addressing the final static image which is generated, but the paradigm of interaction as well.

Traditional visualization techniques for multidimensional data sets, such as parallel coordinates, star glyphs, and scatterplot matrices, do not scale well to high dimensional data sets. A common approach to solve this problem is dimensionality reduction. Existing dimensionality reduction techniques, such as Principal Component Analysis, Multidimensional Scaling, and Self Organizing Maps, have serious drawbacks in that the generated low dimensional subspace has no intuitive meaning to users. In addition, little user interaction is allowed in those highly automatic processes. In this thesis, we propose a new methodology to dimensionality reduction that combines automation and user interaction for the generation of meaningful subspaces, called the visual hierarchical dimension reduction (VHDR) framework. Firstly, VHDR groups all dimensions of a data set into a dimension hierarchy. This hierarchy is then visualized using a radial space-filling hierarchy visualization tool called Sunburst. Thus users are allowed to interactively explore and modify the dimension hierarchy, and select clusters at different levels of detail for the data display. VHDR then assigns a representative dimension to each dimension cluster selected by the users. Finally, VHDR maps the high-dimensional data set into the subspace composed of these representative dimensions and displays the projected subspace. To accomplish the latter, we have designed several extensions to existing popular multidimensional display techniques, such as parallel coordinates, star glyphs, and scatterplot matrices. These displays have been enhanced to express semantics of the selected subspace, such as the context of the dimensions and dissimilarity among the individual dimensions in a cluster. We have implemented all these features and incorporated them into the XmdvTool software package, which will be released as XmdvTool Version 6.0. Lastly, we developed two case studies to show how we apply VHDR to visualize and interactively explore a high dimensional data set.

This report presents an application developed for evaluating the possible benefits of using audial support in a visualization application. A hypothesis is presented where the idea is that sonification might help users perceive densities in data-sets with large amounts of data points. The application presents a scatterplot and a parallel coordinates plot. To both plots audial support is added where the amplitude of the sound is used for representing the amount of points in different areas of the plots. The method is evaluated through user studies where ability to find maximum points, finding equal densities and appreciation of the sounds is investigated. Quantitative and qualitative results show improvements when finding maximum points in tight clusters both in parallel coordinates and scatter plots. This is a first step when investigating this area of visualization spurring further research.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2017-06-27T13:53:26Z No. of bitstreams: 1 Glauber Cini_.pdf: 2121004 bytes, checksum: c1f55ddc527cdaeb7ae3c224baea727a (MD5) / Made available in DSpace on 2017-06-27T13:53:26Z (GMT). No. of bitstreams: 1 Glauber Cini_.pdf: 2121004 bytes, checksum: c1f55ddc527cdaeb7ae3c224baea727a (MD5) Previous issue date: 2017-03-29 / Nenhuma / O Visual Analytics consiste na combinação de métodos inteligentes e automáticos com a capacidade de percepção visual do ser humano visando a extração do conhecimento de conjuntos de dados. Esta capacidade visual é apoiada por interfaces interativas como, sendo a de maior importância para este trabalho, a visualização por Coordenadas Paralelas. Todavia, ferramentas que disponham de ambos os métodos automáticos (KDD) e visuais (Coordenadas Paralelas) de forma genérica e integrada mostra-se primordial. Deste modo, este trabalho apresenta um modelo integrado entre o processo de KDD e o de Visualização de Informação utilizando as Coordenadas Paralelas com ênfase no make sense of data, ao ampliar a possibilidade de exploração dos dados ainda na etapa de pré-processamento. Para demonstrar o funcionamento deste modelo, um plugin foi desenvolvido sobre a ferramenta WEKA. Este módulo é responsável por ampliar as possibilidades de utilização da ferramenta escolhida ao expandir suas funcionalidades a ponto de conceitua-la como uma ferramenta Visual Analytics. Junto a visualização de Coordenadas Paralelas disponibilizada, também se viabiliza a interação por permutação das dimensões (eixos), interação por seleção de amostras (brushing) e possibilidade de detalhamento das mesmas na própria visualização. / Visual Analytics is the combination of intelligent and automatic methods with the ability of human visual perception aiming to extract knowledge from data sets. This visual capability is supported by interactive interfaces, considering the most important for this work, the Parallel Coordinates visualization. However, tools that have both automatic methods (KDD) and visual (Parallel Coordinates) in a generic and integrated way is inherent. Thus, this work presents an integrated model between the KDD process and the Information Visualization using the Parallel Coordinates with emphasis on the make sense of data, by increasing the possibility of data exploration in the preprocessing stage. To demonstrate the operation of this model, a plugin was developed on the WEKA tool. This module is responsible for expanding the possibilities of chosen tool by expanding its functionality to the point of conceptualizing it as a Visual Analytics tool. In addition to the delivered visualization of Parallel Coordinate, it is also possible to interact by permutation of the dimensions (axes), interaction by selection of samples (brushing) and possibility of detailing them in the visualization itself.

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-09-19T16:19:16Z No. of bitstreams: 1 Richard Mateus Altmayer_.pdf: 11624194 bytes, checksum: 033148b21ac20bc09f084ae426e1e45f (MD5) / Made available in DSpace on 2018-09-19T16:19:16Z (GMT). No. of bitstreams: 1 Richard Mateus Altmayer_.pdf: 11624194 bytes, checksum: 033148b21ac20bc09f084ae426e1e45f (MD5) Previous issue date: 2018-04-12 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A utilização de informações sobre comportamento de navegação de usuários na web tem sido amplamente utilizada para traçar perfis comportamentais de usuários com o intuito de oferecer anúncios publicitários por segmentos ou categorias. Nesta mesma linha, hábitos de comportamento baseado em locais que um indivíduo frequenta no seu cotidiano também podem ser analisados. Este trabalho propõe um modelo de agrupamento de indivíduos de uma população para posterior análise de seus hábitos de frequência a locais (GeoSocial). Os padrões de frequência dos grupos formados representam características de comportamento da população e podem ajudar a identificar oportunidades mercadológicas ou auxiliar aos tomadores de decisão ligados ao governo proporem determinadas melhorias/mudanças na infra-estrutura de uma determinada cidade. As informações dos locais de interesse frequentados pelos usuários são capturadas por coordenadas GPS via aplicativo móvel desenvolvido. O aplicativo rastreia e armazena as localidades que o indivíduo frequenta, permite visualizar o seu tempo e locais de permanência e pode conectá-lo à uma rede social formada a partir das similaridades entre seus hábitos e de outros indivíduos. O modelo proposto engloba: i. um módulo de clusterização de usuários que utiliza a técnica Affinity Propagation; ii. um módulo de visualização interativa para análise dos grupos por meio da técnica de Coordenadas Paralelas. O GeoSocial é avaliado mediante a utilização de diferentes cenários, fazendo uso de dados artificiais gerados. A avaliação evidencia o potencial de adaptação do modelo à diferentes objetivos de análise. / Information about user navigation behavior on the web has been widely used to draw user behavioral profiles in order to offer advertisements segmented by categories. In this same line, behavior habits based on places that an individual attends in their daily life can also be analyzed. This paper proposes a clustering model of individuals for further analysis of their habits of frequency in places (GeoSocial). Patterns of the formed groups represent characteristics of population’s behavior and can help to identify market opportunities or to help decision makers linked to government to propose improvements/changes in the infrastructure of a city. Users information about their frequented interest places are captured by GPS coordinates by a mobile app developed. App tracks and storages places that are frequent individuals. It allows visualize their time permanency on places and connect they to a social network formed from the similarities between their habits and the others. The proposed model includes: i. a user clustering module based on Affinity Propagation technique; ii. an interactive visualization module to analyze individual data correlation of groups based on Parallel Coordinates technique. GeoSocial is evaluated by different scenarios, making use of artificial data generated. Evaluation indicates the possibility of the model to a multitude of objectives.

Data can be found everywhere, for example in the form of price, size, weight and colour of all products sold by a company, or as time series of daily observations of temperature, precipitation, wind and visibility from thousands of stations. Due to their size and complexity it is intrinsically hard to form a global overview and understanding of them. Information visualization aims at overcoming these difficulties by transforming data into representations that can be more easily interpreted. This thesis presents work on the development of methods to enable efficient information visualization of multivariate and time-varying data sets by conveying information in a clear and interpretable way, and in a reasonable time. The work presented is primarily based on a popular multivariate visualization technique called parallel coordinates but many of the methods can be generalized to apply to other information visualization techniques. A three-dimensional, multi-relational version of parallel coordinates is presented that enables a simultaneous analysis of all pairwise relationships between a single focus variable and all other variables included in the display. This approach permits a more rapid analysis of highly multivariate data sets. Through a number of user studies the multi-relational parallel coordinates technique has been evaluated against standard, two-dimensional parallel coordinates and been found to better support a number of different types of task. High precision density maps and transfer functions are presented as a means to reveal structure in large data displayed in parallel coordinates. These two approaches make it possible to interactively analyse arbitrary regions in a parallel coordinates display without risking the loss of significant structure. Another focus of this thesis relates to the visualization of time-varying, multivariate data. This has been studied both in the specific application area of system identification using volumetric representations, as well as in the general case by the introduction of temporal parallel coordinates. The methods described in this thesis have all been implemented using modern computer graphics hardware which enables the display and manipulation of very large data sets in real time. A wide range of data sets, both synthetically generated and taken from real applications, have been used to test these methods. It is expected that, as long as the data have multivariate properties, they could be employed efficiently.

Presenting large amount of Multivariate Data is not a simple problem. When there are multiple correlated variables involved, it becomes difficult to comprehend data using traditional ways. Information Visualization techniques provide an interactive way to present and analyze such data. This thesis has been carried out at ABB Corporate Research, Västerås, Sweden. Use of Parallel Coordinates and Multiple Coordinated Views was has been suggested to realize interactive reporting and trending of Multivariate Data for ABB’s Network Manager SCADA system. A prototype was developed and an empirical study was conducted to evaluate the suggested design and test it for usability from an actual industry perspective. With the help of this prototype and the evaluations carried out, we are able to achieve stronger results regarding the effectiveness and efficiency of the visualization techniques used. The results confirm that such interfaces are more effective, efficient and intuitive for filtering and analyzing Multivariate Data.

The performance of hard disks has become increasingly important as the volume of data storage increases. At the bottom level of large-scale storage networks is the hard disk. Despite the importance of hard drives in a storage network, it is often difficult to analyze the performance of hard disks due to the sheer size of the datasets seen by hard disks. Additionally, hard drive workloads can have several multi-dimensional characteristics, such as access time, queue depth and block-address space. The result is that hard drive workloads are extremely diverse and large, making extracting meaningful information from hard drive workloads very difficult. This is one reason why there are several inefficiencies in storage networks. In this paper, we develop a tool that assists in communicating valuable insights into these datasets, resulting in an approach that utilizes parallel coordinates to model data storage workloads captured with bus analyzers. Users are presented with an effective visualization of workload captures with this implementation, along with methods to interact with and manipulate the model in order to more clearly analyze the lowest level of their storage systems. Design decisions regarding the feature set of this tool are based on the analysis needs of domain experts and feedback from a conducted user study. Results from our user study evaluations demonstrate the efficacy of our tool to observe valuable insights, which can potentially assist in future storage system design and deployment decisions. Using this tool, domain experts were able to model storage system datasets with various features to manipulate the visualization to make observations and discoveries, such as detecting logical block address banding and observe various dataset trends which were not readily noticeable using conventional analysis methods.

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