Large data sets are difficult to analyze. Visualization has been proposed to assist exploratory data analysis (EDA) as our visual systems can process signals in parallel to quickly detect patterns. Nonetheless, designing an effective visual analytic tool remains a challenge. This challenge is partly due to our incomplete understanding of how common visualization techniques are used by human operators during analyses, either in laboratory settings or in the workplace. This thesis aims to further understand how visualizations can be used to support EDA. More specifically, we studied techniques that display multiple levels of visual information resolutions (VIRs) for analyses using a range of methods. The first study is a summary synthesis conducted to obtain a snapshot of knowledge in multiple-VIR use and to identify research questions for the thesis: (1) low-VIR use and creation; (2) spatial arrangements of VIRs. The next two studies are laboratory studies to investigate the visual memory cost of image transformations frequently used to create low-VIR displays and overview use with single-level data displayed in multiple-VIR interfaces. For a more well-rounded evaluation, we needed to study these techniques in ecologically-valid settings. We therefore selected the application domain of web session log analysis and applied our knowledge from our first three evaluations to build a tool called Session Viewer. Taking the multiple coordinated view and overview + detail approaches, Session Viewer displays multiple levels of web session log data and multiple views of session populations to facilitate data analysis from the high-level statistical to the low-level detailed session analysis approaches. Our fourth and last study for this thesis is a field evaluation conducted at Google Inc. with seven session analysts using Session Viewer to analyze their own data with their own tasks. Study observations suggested that displaying web session logs at multiple levels using the overview + detail technique helped bridge between high-level statistical and low-level detailed session analyses, and the simultaneous display of multiple session populations at all data levels using multiple views allowed quick comparisons between session populations. We also identified design and deployment considerations to meet the needs of diverse data sources and analysis styles.

Large data sets are difficult to analyze. Visualization has been proposed to assist exploratory data analysis (EDA) as our visual systems can process signals in parallel to quickly detect patterns. Nonetheless, designing an effective visual analytic tool remains a challenge. This challenge is partly due to our incomplete understanding of how common visualization techniques are used by human operators during analyses, either in laboratory settings or in the workplace. This thesis aims to further understand how visualizations can be used to support EDA. More specifically, we studied techniques that display multiple levels of visual information resolutions (VIRs) for analyses using a range of methods. The first study is a summary synthesis conducted to obtain a snapshot of knowledge in multiple-VIR use and to identify research questions for the thesis: (1) low-VIR use and creation; (2) spatial arrangements of VIRs. The next two studies are laboratory studies to investigate the visual memory cost of image transformations frequently used to create low-VIR displays and overview use with single-level data displayed in multiple-VIR interfaces. For a more well-rounded evaluation, we needed to study these techniques in ecologically-valid settings. We therefore selected the application domain of web session log analysis and applied our knowledge from our first three evaluations to build a tool called Session Viewer. Taking the multiple coordinated view and overview + detail approaches, Session Viewer displays multiple levels of web session log data and multiple views of session populations to facilitate data analysis from the high-level statistical to the low-level detailed session analysis approaches. Our fourth and last study for this thesis is a field evaluation conducted at Google Inc. with seven session analysts using Session Viewer to analyze their own data with their own tasks. Study observations suggested that displaying web session logs at multiple levels using the overview + detail technique helped bridge between high-level statistical and low-level detailed session analyses, and the simultaneous display of multiple session populations at all data levels using multiple views allowed quick comparisons between session populations. We also identified design and deployment considerations to meet the needs of diverse data sources and analysis styles.

This thesis presents a modification of existing entropy-based splitting criteria for classification and regression trees. Trees are typically grown using splitting criteria that choose optimal splits without taking future splits into account. This thesis examines localised splitting criteria that are based on local averaging in regression trees or local proportions in classification trees. The use of a localised criterion is motivated by the fact that future splits result in leaves that contain local observations, and hence local deviances provide a better approximation of the deviance of the fully grown tree. While most recent research has focussed on tree-averaging techniques that are aimed at taking a moderately successful splitting criterion and improving its predictive power, this thesis concentrates on improving the splitting criterion. Use of a localised splitting criterion captures local structures and enables later splits to capitalise on the placement of earlier splits when growing a tree. Using the localised splitting criterion results in much simpler trees for pure interaction data (data with no main effects) and can produce trees with fewer errors and lower residual mean deviances than those produced using a global splitting criterion when applied to real data sets with strong interaction effects. The superiority of the localised splitting criterion can persist when multiple trees are grown and averaged using simple methods. Although a single tree grown using the localised splitting criterion can outperform tree averaging using the global criterion, generally improvements in predictive performance are achieved by utilising the localised splitting criterion's property of detecting local discontinuities and averaging over sets of trees grown by placing splits where the deviance is locally minimal. Predictive performance improves further when the degree of localisation of the splitting criterion is randomly selected and weighted randomisation is used with locally minimal deviances to produce sets of trees to average over. Although state of the art methods quickly average very large numbers of trees, thus making the performance of the splitting criterion less critical, predictive performance when the localised criterion is used in bagging indicates that different splitting methods warrant investigation. The localised splitting criterion is most useful for growing one tree or a small number of trees to examine structure in the data. Structurally different trees can be obtained by simply splitting the data where the localised splitting criterion is locally optimal.

This thesis considers the tasks involved in exploratory analysis of temporal graph data, and the visual techniques which are able to support these tasks. There has been an enormous increase in the amount and availability of graph (network) data, and in particular, graph data that is changing over time. Understanding the mechanisms involved in temporal change in a graph is of interest to a wide range of disciplines. While the application domain may differ, many of the underlying questions regarding the properties of the graph and mechanism of change are the same. The research area of temporal graph visualisation seeks to address the challenges involved in visually representing change in a graph over time. While most graph visualisation tools focus on static networks, recent research has been directed toward the development of temporal visualisation systems. By representing data using computer-generated graphical forms, Information Visualisation techniques harness human perceptual capabilities to recognise patterns, spot anomalies and outliers, and find relationships within the data. Interacting with these graphical representations allow individuals to explore large datasets and gain further insightinto the relationships between different aspects of the data. Visual approaches are particularly relevant for Exploratory Data Analysis (EDA), where the person performing the analysis may be unfamiliar with the data set, and their goal is to make new discoveries and gain insight through its exploration. However, designing visual systems for EDA can be difficult, as the tasks which a person may wish to carry out during their analysis are not always known at outset. Identifying and understanding the tasks involved in such a process has given rise to a number of task taxonomies which seek to elucidate the tasks and structure them in a useful way. While task taxonomies for static graph analysis exist, no suitable temporal graph taxonomy has yet been developed. The first part of this thesis focusses on the development of such a taxonomy. Through the extension and instantiation of an existing formal task framework for general EDA, a task taxonomy and a task design space are developed specifically for exploration of temporal graph data. The resultant task framework is evaluated with respect to extant classifications and is shown to address a number of deficiencies in task coverage in existing works. Its usefulness in both the design and evaluation processes is also demonstrated. Much research currently surrounds the development of systems and techniques for visual exploration of temporal graphs, but little is known about how the different types of techniques relate to one another and which tasks they are able to support. The second part of this thesis focusses on the possibilities in this area: a design spaceof the possible visual encodings for temporal graph data is developed, and extant techniques are classified into this space, revealing potential combinations of encodings which have not yet been employed. These may prove interesting opportunities for further research and the development of novel techniques. The third part of this work addresses the need to understand the types of analysis the different visual techniques support, and indeed whether new techniques are required. The techniques which are able to support the different task dimensions are considered. This task-technique mapping reveals that visual exploration of temporalgraph data requires techniques not only from temporal graph visualisation, but also from static graph visualisation and comparison, and temporal visualisation. A number of tasks which are unsupported or less-well supported, which could prove interesting opportunities for future research, are identified. The taxonomies, design spaces, and mappings in this work bring order to the range of potential tasks of interest when exploring temporal graph data and the assortmentof techniques developed to visualise this type of data, and are designed to be of use in both the design and evaluation of temporal graph visualisation systems.

Large data sets are difficult to analyze. Visualization has been proposed to assist exploratory data analysis (EDA) as our visual systems can process signals in parallel to quickly detect patterns. Nonetheless, designing an effective visual analytic tool remains a challenge. This challenge is partly due to our incomplete understanding of how common visualization techniques are used by human operators during analyses, either in laboratory settings or in the workplace. This thesis aims to further understand how visualizations can be used to support EDA. More specifically, we studied techniques that display multiple levels of visual information resolutions (VIRs) for analyses using a range of methods. The first study is a summary synthesis conducted to obtain a snapshot of knowledge in multiple-VIR use and to identify research questions for the thesis: (1) low-VIR use and creation; (2) spatial arrangements of VIRs. The next two studies are laboratory studies to investigate the visual memory cost of image transformations frequently used to create low-VIR displays and overview use with single-level data displayed in multiple-VIR interfaces. For a more well-rounded evaluation, we needed to study these techniques in ecologically-valid settings. We therefore selected the application domain of web session log analysis and applied our knowledge from our first three evaluations to build a tool called Session Viewer. Taking the multiple coordinated view and overview + detail approaches, Session Viewer displays multiple levels of web session log data and multiple views of session populations to facilitate data analysis from the high-level statistical to the low-level detailed session analysis approaches. Our fourth and last study for this thesis is a field evaluation conducted at Google Inc. with seven session analysts using Session Viewer to analyze their own data with their own tasks. Study observations suggested that displaying web session logs at multiple levels using the overview + detail technique helped bridge between high-level statistical and low-level detailed session analyses, and the simultaneous display of multiple session populations at all data levels using multiple views allowed quick comparisons between session populations. We also identified design and deployment considerations to meet the needs of diverse data sources and analysis styles. / Science, Faculty of / Computer Science, Department of / Graduate

Proximity scaling methods (e.g., multidimensional scaling) represent objects in a low dimensional configuration so that fitted distances between objects optimally approximate multivariate proximities. Next to finding the optimal configuration the goal is often also to assess groups of objects from the configuration. This can be difficult if the optimal configuration lacks clusteredness (coined c-clusteredness). We present Cluster Optimized Proximity Scaling (COPS), which attempts to solve this problem by finding a configuration that exhibts c-clusteredness. In COPS, a flexible scaling loss function (p-stress) is combined with an index that quantifies c-clusteredness in the solution, the OPTICS Cordillera. We present two variants of combining p-stress and Cordillera, one for finding the configuration directly and one for metaparameter selection for p-stress. The first variant is illustrated by scaling Californian counties with respect to climate change related natural hazards. We identify groups of counties with similar risk profiles and find that counties that are in high risk of drought are socially vulnerable. The second variant is illustrated by finding a clustered nonlinear representation of countries according to their history of banking crises from 1800 to 2010. (authors' abstract) / Series: Discussion Paper Series / Center for Empirical Research Methods

Infecções hospitalares, causadas por fungos e bactérias, são responsáveis por milhares de mortes anuais e disseminam-se cada vez mais rápido em ambiente hospitalar. Estas infecções afetam principalmente pacientes que são submetidos a procedimentos invasivos ou que apresentam baixa imunidade. Este quadro tende a agravar-se devido ao surgimento de micro-organismos resistentes aos tratamentos atualmente disponíveis, decorrentes, principalmente, de excessivas prescrições, automedicação, pela utilização errônea de antibióticos bem como pela aplicação de métodos de profilaxia inadequados determinando, desta forma, a necessidade de pesquisar e de identificar novos medicamentos para esta finalidade. Neste contexto, ressalta-se que os nitrocompostos que vêm demonstrando bons resultados como agentes antimicrobianos e, sendo assim, este trabalho se propõe a planejar, sintetizar e avaliar duas séries de compostos análogos à nifuroxazida frente a bactérias Gram-positivas, Gram-negativas e fungos de importância em infecções hospitalares com caráter de multirresistência. O planejamento de modificações moleculares na estrutura da série de análogos à nifuroxazida foi auxiliado pelo diagrama de Craig, que se aplica à seleção dos grupos substituintes. A identificação dos compostos obtidos foi realizada por análise espectrofotométrica RMN 1H e RMN 13C e, como critério de pureza, foi realizado análise elementar de CHN. A melhor atividade dos análogos foi frente à S. aureus, onde 16 dos 23 compostos planejados apresentaram atividade superior aos fármacos de referência, estas quais, e composto protótipo. Vale salientar também que os compostos mais ativos foram os análogos 4-butil-N\'-[(5-nitrofuran-2-il) metileno] benzidrazida e 3-acetil-2-[5-nitro-furan-2-il]-5-[4-ciano-fenil]-2,3-diidro-1,3,4-oxadiazolínico com IC90 = 1,8 ± 0,04 µM e 3,89 ± 0,07 µM, respectivamente. Nos estudos de relações estrutura-atividade, a propriedade eletrônica se mostrou com importância fundamental para a atividade dos compostos frente aos micro-organismos considerados. Os resultados encontrados são bastantes promissores e sinalizam para a possibilidade de identificação de um novo candidato para fármaco antimicrobiano frente à infecções hospitalares com caráter de multirresistência. / Nosocomial infections are caused by bacteria and fungi and are the main reason for thousands of deaths every year in the hospital environment. The most susceptible individuals to these type of infection are the immunocompromised, highlighting old-aged and immunodeficiency people, as also patients who passed by invasive procedures. Not only do this situation is very serious, but also a huge number of multidrug-resistant microorganisms are reported worldwide, basically due to excess of prescription and wrong use of antibiotics. If this situation doesn\'t be maintained today, there will not be a bright sight in the future. In this context, there is the necessity for research and development of new antimicrobial agents, and the nitrocompounds, highlighting nifuroxazide\'s analogs, are showing excellent activity against several pathogens related to multidrug resistant nosocomial infections. Therefore, this work aims to design, to synthesize and to evaluate two series of nifuroxazide\'s analogs against strains of gram-positive and gram-negative bacteria, also fungi strains. The sets of analogs were designed based on the replacement of hydroxyl group of nifuroxazide by different substituent groups according to Craig\'s diagram, also a modification in the structure core; identification of these compounds was carried out through 1H and 13C NMR. Melting point and elementary analysis were analyzed for purity criteria. Until the moment, about 23 compounds were evaluated and the best observed activity was against S. aureus strains, which 16 analogs showed better activity than the lead compound and several other compounds, used as referential drug. It is noteworthy that the best activity were analogs 4-butyl-N\'-((5-nitrofuran-2-yl) methylene) benzohydrazide and 3-acetyl-2-[5-nitro-furan-2-yl]-5-[4-cyano-phenil]-2,3-dihydro-1,3,4 oxadiazole with IC90 = 1,8 ± 0,04 µM and 3,89 ± 0,07 µM, respectively. The chemometrics studies suggest that electronic properties are most related to the biological activity on these microorganisms. All the available results shows the potential of nitrofurans to be a new candidate for an antimicrobial drug against multidrug resistant nosocomial infections.

Infecções hospitalares, causadas por fungos e bactérias, são responsáveis por milhares de mortes anuais e disseminam-se cada vez mais rápido em ambiente hospitalar. Estas infecções afetam principalmente pacientes que são submetidos a procedimentos invasivos ou que apresentam baixa imunidade. Este quadro tende a agravar-se devido ao surgimento de micro-organismos resistentes aos tratamentos atualmente disponíveis, decorrentes, principalmente, de excessivas prescrições, automedicação, pela utilização errônea de antibióticos bem como pela aplicação de métodos de profilaxia inadequados determinando, desta forma, a necessidade de pesquisar e de identificar novos medicamentos para esta finalidade. Neste contexto, ressalta-se que os nitrocompostos que vêm demonstrando bons resultados como agentes antimicrobianos e, sendo assim, este trabalho se propõe a planejar, sintetizar e avaliar duas séries de compostos análogos à nifuroxazida frente a bactérias Gram-positivas, Gram-negativas e fungos de importância em infecções hospitalares com caráter de multirresistência. O planejamento de modificações moleculares na estrutura da série de análogos à nifuroxazida foi auxiliado pelo diagrama de Craig, que se aplica à seleção dos grupos substituintes. A identificação dos compostos obtidos foi realizada por análise espectrofotométrica RMN 1H e RMN 13C e, como critério de pureza, foi realizado análise elementar de CHN. A melhor atividade dos análogos foi frente à S. aureus, onde 16 dos 23 compostos planejados apresentaram atividade superior aos fármacos de referência, estas quais, e composto protótipo. Vale salientar também que os compostos mais ativos foram os análogos 4-butil-N\'-[(5-nitrofuran-2-il) metileno] benzidrazida e 3-acetil-2-[5-nitro-furan-2-il]-5-[4-ciano-fenil]-2,3-diidro-1,3,4-oxadiazolínico com IC90 = 1,8 ± 0,04 µM e 3,89 ± 0,07 µM, respectivamente. Nos estudos de relações estrutura-atividade, a propriedade eletrônica se mostrou com importância fundamental para a atividade dos compostos frente aos micro-organismos considerados. Os resultados encontrados são bastantes promissores e sinalizam para a possibilidade de identificação de um novo candidato para fármaco antimicrobiano frente à infecções hospitalares com caráter de multirresistência. / Nosocomial infections are caused by bacteria and fungi and are the main reason for thousands of deaths every year in the hospital environment. The most susceptible individuals to these type of infection are the immunocompromised, highlighting old-aged and immunodeficiency people, as also patients who passed by invasive procedures. Not only do this situation is very serious, but also a huge number of multidrug-resistant microorganisms are reported worldwide, basically due to excess of prescription and wrong use of antibiotics. If this situation doesn\'t be maintained today, there will not be a bright sight in the future. In this context, there is the necessity for research and development of new antimicrobial agents, and the nitrocompounds, highlighting nifuroxazide\'s analogs, are showing excellent activity against several pathogens related to multidrug resistant nosocomial infections. Therefore, this work aims to design, to synthesize and to evaluate two series of nifuroxazide\'s analogs against strains of gram-positive and gram-negative bacteria, also fungi strains. The sets of analogs were designed based on the replacement of hydroxyl group of nifuroxazide by different substituent groups according to Craig\'s diagram, also a modification in the structure core; identification of these compounds was carried out through 1H and 13C NMR. Melting point and elementary analysis were analyzed for purity criteria. Until the moment, about 23 compounds were evaluated and the best observed activity was against S. aureus strains, which 16 analogs showed better activity than the lead compound and several other compounds, used as referential drug. It is noteworthy that the best activity were analogs 4-butyl-N\'-((5-nitrofuran-2-yl) methylene) benzohydrazide and 3-acetyl-2-[5-nitro-furan-2-yl]-5-[4-cyano-phenil]-2,3-dihydro-1,3,4 oxadiazole with IC90 = 1,8 ± 0,04 µM and 3,89 ± 0,07 µM, respectively. The chemometrics studies suggest that electronic properties are most related to the biological activity on these microorganisms. All the available results shows the potential of nitrofurans to be a new candidate for an antimicrobial drug against multidrug resistant nosocomial infections.

Across a wide variety of digital devices, users create, consume, and disseminate large quantities of information. While data sometimes look like a spreadsheet or network diagram, more often for everyday users their data look more like an Amazon search page, the line-up for a fantasy football team, or a set of Yelp reviews. However, interpreting these kinds of data remains a difficult task even for experts since they often feature soft or unknown constraints (e.g. ”I want some Thai food, but I also want a good bargain”) across highly multidimensional data (i.e. rating, reviews, popularity, proximity). Existing technology is largely optimized for users with hard criteria and satisfiable constraints, and consumer systems often use representations better suited for browsing than sensemaking. In this thesis I explore ways to support soft constraint decision-making and exploratory data analysis by giving users tools that show fine-grained features of the data while at the same time displaying useful contextual information. I describe approaches for representing collaborative content history and working behavior that reveal both individual and group/dataset level features. Using these approaches, I investigate general visualizations that utilize physics to help even inexperienced users find small and large trends in multivariate data. I describe the transition of physicsbased visualization from the research space into the commercial space through a startup company, and the insights that emerged both from interviews with experts in a wide variety of industries during commercialization and from a comparative lab study. Taking one core use case from commercialization, consumer search, I develop a prototype, Fractal, which helps users explore and apply constraints to Yelp data at a variety of scales by curating and representing individual-, group-, and dataset-level features. Through a user study and theoretical model I consider how the prototype can best aide users throughout the sensemaking process. My dissertation further investigates physics-based approaches for represent multivariate data, and explores how the user’s exploration process itself can help dynamically to refine the search process and visual representation. I demonstrate that selectively representing points using clusters can extend physics-based visualizations across a variety of data scales, and help users make sense of data at scales that might otherwise overload them. My model provides a framework for stitching together a model of user interest and data features, unsupervised clustering, and visual representations for exploratory data visualization. The implications from commercialization are more broad, giving insight into why research in the visualization space is/isn’t adopted by industry, a variety of real-world use cases for multivariate exploratory data analysis, and an index of common data visualization needs in industry.

The aim of the thesis is to compare methods of cluster analysis correctly classify objects in the dataset into groups, which are known. In the theoretical section first describes the steps needed to prepare a data file for cluster analysis. The next theoretical section is dedicated to the cluster analysis, which describes ways of measuring similarity of objects and clusters, and dedicated to description the methods of cluster analysis used in practical part of this thesis. In practical part are described and analyzed 20 files. Each file contains only quantitative variables and sort characters by which objects are sorted. In each file is calculated success rate of object segmentation into groups for each cluster method. At the end of the practical part is a summary description of the results of cluster methods. The main contribution of this thesis is to evaluate the success of cluster methods for classification objects into known groups.