Muitas aplicações tratam dados estruturados na forma de grafos, como, por exemplo, redes sociais, redes de computação e comunicação, redes epidemiológicas, entre outras. Essas aplicações são baseadas em grafos multivariados representando itens e relacionamentos caracterizados por múltiplos atributos. A maioria das técnicas descritas na literatura para lidar com grafos multivariados concentram-se em problemas associados com visualização da topologia ou em problemas associados com a visualização de múltiplos atributos de itens separados da topologia do grafo. Durante a exploração de grafos multivariados, os usuários podem se beneficiar da combinação de diversas técnicas de visualização. A fim de apoiar os usuários durante essa exploração, esta tese propõe uma abordagem que permite ao usuário combinar diversas técnicas de visualização, mantendo o controle da história das visualizações encadeando-as de uma maneira integrada. Os usuários são capazes de comparar os resultados fornecidos por diferentes técnicas de visualização, o que proporciona o sinergismo necessário para a compreensão mais completa do conjunto de dados. São propostas também três técnicas para a exploração de grafos multivariados. A primeira técnica (ClusterVis) fornece a visualização das relações entre atributos de nodos pertencentes a agrupamentos. A segunda, denominada GlyphMatrix, explora o uso de glifos e matriz de adjacência, para visualizar a relação entre atributos associados às arestas. E, finalmente, a terceira (Iris) permite a visualização de atributos associados às arestas de nodos adjacentes. / Many computing applications imply dealing with network data, for example, social networks, communications and computing networks, epidemiological networks, among others. These applications are based on multivariate graphs representing items and relationships characterized by multiple attributes. Most of the visualization techniques described in the literature for dealing with multivariate graphs focus either on problems associated with the visualization of topology or on problems associated with the visualization of multiple attributes of items, separated from the graph topology. During the exploration of multivariate graphs, users might get benefit of combining these diverse visualization techniques. In order to support users during that exploration, this thesis proposes an approach that allows users to combine diverse visualization techniques while keeping track of the history of chained visualizations in an integrated way. Users are able to compare results provided by different visualization techniques, and thus the tools provide the synergism one needs to fully comprehend the data set. Three techniques were embedded in the approach. The first one emphasizes the visualization of relations between the attributes of nodes belonging to clusters, and thus is called ClusterVis. The second one is named GlyphMatrix, and explores the use of glyphs and adjacency matrices as an alternative representation of the relation between the attributes of edges. Finally, a third technique (Iris) provides features for the visualization of attributes of edges of adjacent nodes.

Muitas aplicações tratam dados estruturados na forma de grafos, como, por exemplo, redes sociais, redes de computação e comunicação, redes epidemiológicas, entre outras. Essas aplicações são baseadas em grafos multivariados representando itens e relacionamentos caracterizados por múltiplos atributos. A maioria das técnicas descritas na literatura para lidar com grafos multivariados concentram-se em problemas associados com visualização da topologia ou em problemas associados com a visualização de múltiplos atributos de itens separados da topologia do grafo. Durante a exploração de grafos multivariados, os usuários podem se beneficiar da combinação de diversas técnicas de visualização. A fim de apoiar os usuários durante essa exploração, esta tese propõe uma abordagem que permite ao usuário combinar diversas técnicas de visualização, mantendo o controle da história das visualizações encadeando-as de uma maneira integrada. Os usuários são capazes de comparar os resultados fornecidos por diferentes técnicas de visualização, o que proporciona o sinergismo necessário para a compreensão mais completa do conjunto de dados. São propostas também três técnicas para a exploração de grafos multivariados. A primeira técnica (ClusterVis) fornece a visualização das relações entre atributos de nodos pertencentes a agrupamentos. A segunda, denominada GlyphMatrix, explora o uso de glifos e matriz de adjacência, para visualizar a relação entre atributos associados às arestas. E, finalmente, a terceira (Iris) permite a visualização de atributos associados às arestas de nodos adjacentes. / Many computing applications imply dealing with network data, for example, social networks, communications and computing networks, epidemiological networks, among others. These applications are based on multivariate graphs representing items and relationships characterized by multiple attributes. Most of the visualization techniques described in the literature for dealing with multivariate graphs focus either on problems associated with the visualization of topology or on problems associated with the visualization of multiple attributes of items, separated from the graph topology. During the exploration of multivariate graphs, users might get benefit of combining these diverse visualization techniques. In order to support users during that exploration, this thesis proposes an approach that allows users to combine diverse visualization techniques while keeping track of the history of chained visualizations in an integrated way. Users are able to compare results provided by different visualization techniques, and thus the tools provide the synergism one needs to fully comprehend the data set. Three techniques were embedded in the approach. The first one emphasizes the visualization of relations between the attributes of nodes belonging to clusters, and thus is called ClusterVis. The second one is named GlyphMatrix, and explores the use of glyphs and adjacency matrices as an alternative representation of the relation between the attributes of edges. Finally, a third technique (Iris) provides features for the visualization of attributes of edges of adjacent nodes.

Muitas aplicações tratam dados estruturados na forma de grafos, como, por exemplo, redes sociais, redes de computação e comunicação, redes epidemiológicas, entre outras. Essas aplicações são baseadas em grafos multivariados representando itens e relacionamentos caracterizados por múltiplos atributos. A maioria das técnicas descritas na literatura para lidar com grafos multivariados concentram-se em problemas associados com visualização da topologia ou em problemas associados com a visualização de múltiplos atributos de itens separados da topologia do grafo. Durante a exploração de grafos multivariados, os usuários podem se beneficiar da combinação de diversas técnicas de visualização. A fim de apoiar os usuários durante essa exploração, esta tese propõe uma abordagem que permite ao usuário combinar diversas técnicas de visualização, mantendo o controle da história das visualizações encadeando-as de uma maneira integrada. Os usuários são capazes de comparar os resultados fornecidos por diferentes técnicas de visualização, o que proporciona o sinergismo necessário para a compreensão mais completa do conjunto de dados. São propostas também três técnicas para a exploração de grafos multivariados. A primeira técnica (ClusterVis) fornece a visualização das relações entre atributos de nodos pertencentes a agrupamentos. A segunda, denominada GlyphMatrix, explora o uso de glifos e matriz de adjacência, para visualizar a relação entre atributos associados às arestas. E, finalmente, a terceira (Iris) permite a visualização de atributos associados às arestas de nodos adjacentes. / Many computing applications imply dealing with network data, for example, social networks, communications and computing networks, epidemiological networks, among others. These applications are based on multivariate graphs representing items and relationships characterized by multiple attributes. Most of the visualization techniques described in the literature for dealing with multivariate graphs focus either on problems associated with the visualization of topology or on problems associated with the visualization of multiple attributes of items, separated from the graph topology. During the exploration of multivariate graphs, users might get benefit of combining these diverse visualization techniques. In order to support users during that exploration, this thesis proposes an approach that allows users to combine diverse visualization techniques while keeping track of the history of chained visualizations in an integrated way. Users are able to compare results provided by different visualization techniques, and thus the tools provide the synergism one needs to fully comprehend the data set. Three techniques were embedded in the approach. The first one emphasizes the visualization of relations between the attributes of nodes belonging to clusters, and thus is called ClusterVis. The second one is named GlyphMatrix, and explores the use of glyphs and adjacency matrices as an alternative representation of the relation between the attributes of edges. Finally, a third technique (Iris) provides features for the visualization of attributes of edges of adjacent nodes.

The purpose of this study was to develop an interactive tool that enables identification of different types of users of an application based on clickstream data. A complex hierarchical clustering algorithm tool called Recursive Hierarchical Clustering (RHC) was used. RHC provides a visualisation of user types as clusters, where each cluster has its own distinguishing action pattern, i.e., one or several consecutive actions made by the user in the application. A case study was conducted on the mobile application Plick, which is an application for selling and buying second hand clothes. During the course of the project, the analysis and its result was discovered to be difficult to understand by the operators of the tool. The interactive tool had to be extended to visualise the complex analysis and its result in an intuitive way. A literature study of how humans interpret information, and how to present it to operators, was conducted and led to a redesign of the tool. More information was added to each cluster to enable further understanding of the clustering results. A clustering reconfiguration option was also created where operators of the tool got the possibility to interact with the analysis. In the reconfiguration, the operator could change the input file of the cluster analysis and thus the end result. Usability tests showed that the extra added information about the clusters served as an amplification and a verification of the original results presented by RHC. In some cases the original result presented by RHC was used as a verification to user group identification made by the operator solely based on the extra added information. The usability tests showed that the complex analysis with its results could be understood and configured without considerable comprehension of the algorithm. Instead it seemed like it could be successfully used in order to identify user types with help of visual clues in the interface and default settings in the reconfiguration. The visualisation tool is shown to be successful in identifying and visualising user groups in an intuitive way.

Visual analytics of large amounts of spatio-temporal data is challenging due to the overlap and clutter from movements of multiple objects. A common approach for analyzing such data is to consider how groups of items cluster and move together in space and time. However, most methods for showing Spatio-temporal Cluster (STC) properties, concentrate on a few dimensions of the cluster (e.g. the cluster movement direction or cluster density) and many other properties are not represented. Furthermore, while representing multiple attributes of clusters in a single view existing methods fail to preserve the original shape of the cluster or distort the actual spatial covering of the dataset. In this thesis, I propose a simple yet effective visualization, FlockViz, for showing multiple STC data dimensions in a single view by preserving the original cluster shape. To evaluate this method I develop a framework for categorizing the wide range of tasks involved in analyzing STCs. I conclude this work through a controlled user study comparing the performance of FlockViz with alternative visualization techniques that aid with cluster-based analytic tasks. Finally the exploration capability of FlockViz is demonstrated in some real life data sets such as fish movement, caribou movement, eagle migration, and hurricane movement. The results of the user studies and use cases confirm the advantage and novelty of the novel FlockViz design for visual analytic tasks.