No description available.

This paper focuses on the importance of effective software development processes and a foundational understanding of success factors in the public sector. Although there has been significant investment in information technology, not all public sector software projects result in a successful return on investment. This study explores the crucial factors, referred to as Critical Success Factors (CSFs), that enable the success of public sector software development projects. Additionally, this study aims to discover if the CSFs, the Software Development Life Cycle (SDLC), or its methodologies are more impactful for the success of software projects. This study aims to also identify the challenges public sector developers face during their efforts. By analyzing empirical data collected from a US Department of Defense Research and Development organization, this research aims to provide essential information for improving the likelihood of successful software project outcomes. Ultimately, this research can enable decision-makers to create better training opportunities, standard operating procedures, and hiring processes for public sector software projects.

Sensemaking tasks, such as reading many news articles to determine the truthfulness of a given claim, are difficult. These tasks require a series of iterative steps to first forage for relevant information and then synthesize this information into a final hypothesis. To assist with such tasks, visual analytics systems provide interactive visualizations of data to enable faster, more accurate, or more thorough analyses. For example, semantic interaction techniques leverage natural or intuitive interactions, like highlighting text, to automatically update the visualization parameters using machine learning. However, this process of using machine learning based on user interaction is not yet well defined. We begin our research efforts by developing a computational pipeline that models and captures how a system processes semantic interactions. We then expanded this model to denote specifically how each component of the pipeline supports steps of the Sensemaking Process. Additionally, we recognized a cognitive symmetry in how analysts consider data items (like news articles) and their attributes (such as terms that appear within the articles). To support this symmetry, we also modeled how to visualize and interact with data items and their attributes simultaneously. We built a testbed system and conducted a user study to determine which analytic tasks are best supported by such symmetry. Then, we augmented the testbed system to scale up to large data using semantic interaction foraging, a method for automated foraging based on user interaction. This experience enabled our development of design challenges and a corresponding future research agenda centered on semantic interaction foraging. We began investigating this research agenda by conducting a second user study on when to apply semantic interaction foraging to better match the analyst's Sensemaking Process. / Doctor of Philosophy / Sensemaking tasks such as determining the truthfulness of a claim using news articles are complex, requiring a series of steps in which the relevance of each piece of information within the articles is first determined. Relevant pieces of information are then combined together until a conclusion may be reached regarding the truthfulness of the claim. To help with these tasks, interactive visualizations of data can make it easier or faster to find or combine information together. In this research, we focus on leveraging natural or intuitive interactions, such organizing documents in a 2-D space, which the system uses to perform machine learning to automatically adjust the visualization to better support the given task. We first model how systems perform such machine learning based on interaction as well as model how each component of the system supports the user's sensemaking task. Additionally, we developed a model and accompanying testbed system for simultaneously evaluating both data items (like news articles) and their attributes (such as terms within the articles) through symmetrical visualization and interaction methods. With this testbed system, we devised and conducted a user study to determine which types of tasks are supported or hindered by such symmetry. We then combined these models to build an additional testbed system that implemented a searching technique to automatically add previously unseen, relevant pieces of information to the visualization. Using our experience in implementing this automated searching technique, we defined design challenges to guide future implementations, along with a research agenda to refine the technique. We also devised and conducted another user study to determine when such automated searching should be triggered to best support the user's sensemaking task.

In this dissertation, I investigate interactive visual history for collaborative exploratory data analysis (EDA). In particular, I examine use of analysis history for improving the awareness of the dimension space coverage 1 2 3 to better support data exploration. Commonly, interactive history tools facilitate data analysis by capturing and representing information about the analysis process. These tools can support a wide range of use-cases from simple undo and redo to complete reconstructions of the visualization pipeline. In the con- text of exploratory collaborative Visual Analytics (VA), history tools are commonly used for reviewing and reusing past states/actions and do not efficiently support other use-cases such as understanding the past analysis from the angle of dimension space coverage. How- ever, such knowledge is essential for exploratory analysis which requires constant formulation of new questions about data. To carry out exploration, an analyst needs to understand “what has been done” versus “what is remaining” to explore. Lack of such insight can result in premature fixation on certain questions, compromising the coverage of the data set and breadth of exploration [80]. In addition, exploration of large data sets sometimes requires collaboration between a group of analysts who might be in different time/location settings. In this case, in addition to personal analysis history, each team member needs to understand what aspects of the problem his or her collaborators have explored. Such scenarios are common in domains such as science and business [34] where analysts explore large multi-dimensional data sets in search of relationships, patterns and trends. Currently, analysts typically rely on memory and/or externalization to keep track of investigated versus uninvestigated aspects of the problem. Although analysis history 4 mechanisms have the potential to assist analyst(s) with this problem, most common visual representations of history are geared towards reviewing & reusing the visualization pipeline or visualization states. I started this research with an observational user study to gain a better understanding of analysts’ history needs in the context of collaborative exploratory VA. This study showed that understanding the coverage of dimension space by using linear history 5 was cumbersome and inefficient. To address this problem, I investigated how alternate visual representations of analysis history could support this use-case. First, I designed and evaluated Footprint-I, a visual history tool that represented analysis from the angle of dimension space coverage (i.e. history of investigation of data dimensions; specifically, this approach revealed which dimensions had been previously investigated and in which combinations). I performed a user study that evaluated participants’ ability to recall the scope of past analysis using my proposed design versus a linear representation of analysis history. I measured participants’ task duration and accuracy in answering questions about a past exploratory VA session. Findings of this study showed that participants with access to dimension space coverage information were both faster and more accurate in understanding dimension space coverage information. Next, I studied the effects of providing coverage information on collaboration. To investigate this question, I designed and implemented Footprint-II, the next version of Footprint-I. In this version, I redesigned the representation of dimension space coverage to be more usable and scalable. I conducted a user study that measured the effects of presenting history from the angle of dimension space coverage on task coordination (tacit breakdown of a common task between collaborators). I asked each participant to assume the role of a business data analyst and continue a exploratory analysis work which was started by a collaborator. The results of this study showed that providing dimension space coverage information helped participants to focus on dimensions that were not investigated in the initial analysis, hence improving tacit task coordination. Finally, I investigated the effects of providing live dimension space coverage information on VA outcomes. To this end, I designed and implemented a standalone prototype VA tool with a visual history module. I used scented widgets [76] to incorporate real-time dimension space coverage information into the GUI widgets. Results of a user study showed that providing live dimension space coverage information increased the number of top-level findings. Moreover, it expanded the breadth of exploration (without compromising the depth) and helped analysts to formulate and ask more questions about their data. / Graduate / 0984 / ali.sarvghad@gmail.com

O tamanho dos conjuntos de dados se tornou um grande problema atualmente. À medida que o sensoriamento urbano ganha popularidade, os conjuntos de dados de natureza espacial e temporal se tornam ubíquos, e levantam uma série de questões relacionadas ao armazenamento e gerenciamento destes. Isso também cria uma mudança no paradigma de análise, uma vez que os conjuntos de dados que antes representavam uma única série de medições ordenadas no tempo, agora são compostos por centenas dessas séries, com uma taxa de amostragem que está aumentando constantemente. Além disso, uma vez que os dados urbanos normalmente apresentam disposição geográfica inerente, a maioria das das tarefas requerem o suporte de representações espaciais apropriadas. Este se torna outro problema, visto que as tecnologias de exibição de imagens não avançam na mesma velocidade das tecnologias de sensoriamento, de modo que consequentemente acaba-se tendo mais dados do que espaço visual para representa-los. Após conduzir uma pesquisa exaustiva a respeito de análise de dados temporais e visualização, nós melhoramos uma visualização compacta de series temporais para auxiliar a exploração de grandes conjuntos de dados espaçotemporais. Nossa proposta aproveita a compacticidade de tal representação para permitir o uso de um mapa para representar os atributos espaciais dos dados, de modo coordenado, enquanto representação, de forma compreensível, centenas de series simultaneamente, com total contexto temporal. Nós apresentamos nossa proposta como sendo capaz de auxiliar várias tarefas de caráter exploratório de forma intuitiva. Para defender essa afirmação, nós mostramos como essa ideia foi desenvolvida e melhorada ao longo do desenvolvimento de dois estudos de design visual em diferentes domínios de aplicação, e validamos com a implementação de protótipos que foram usados na análise exploratória de vários conjuntos de dados com 3 representações diferentes. Palavras- / The size of datasets became the major problem in data analysis today. As urban sensing becomes popular, datasets of spatial and temporal nature become ubiquitous, leading to several concerns regarding storage and management. It also creates a shift of paradigm in data analysis, as datasets that once represented a single series of measurements ordered in time are now composed of hundreds of series with ever increasing sampling rates. Also, as urban data usually presents inherent geographic disposition, most analysis tasks requires the support of proper spatial views. It becomes another problem, once that displaying technologies do not advance at the same of pace that sensing technologies do, and consequently, there is usually more data than visual space to represent it. After conducting exhaustive research on temporal data analysis and visualization, we improved a compact visual representation of time series to support the exploration of large spatio-temporal datasets. Our proposal exploits the compactness of such representation to allow the use of a map to represent the spatial properties of the data in a coordinate scheme while presenting, in a comprehensible manner, hundreds of series simultaneously, with full temporal context. We argue that such solution can effectively support many exploratory tasks in an intuitive manner. To support this claim, we show how the idea was conceived, and improved along the development of two design studies from different application domains, and validated by the implementation of prototypes used in the exploratory analysis of several datasets with 3 different data structures.

O tamanho dos conjuntos de dados se tornou um grande problema atualmente. À medida que o sensoriamento urbano ganha popularidade, os conjuntos de dados de natureza espacial e temporal se tornam ubíquos, e levantam uma série de questões relacionadas ao armazenamento e gerenciamento destes. Isso também cria uma mudança no paradigma de análise, uma vez que os conjuntos de dados que antes representavam uma única série de medições ordenadas no tempo, agora são compostos por centenas dessas séries, com uma taxa de amostragem que está aumentando constantemente. Além disso, uma vez que os dados urbanos normalmente apresentam disposição geográfica inerente, a maioria das das tarefas requerem o suporte de representações espaciais apropriadas. Este se torna outro problema, visto que as tecnologias de exibição de imagens não avançam na mesma velocidade das tecnologias de sensoriamento, de modo que consequentemente acaba-se tendo mais dados do que espaço visual para representa-los. Após conduzir uma pesquisa exaustiva a respeito de análise de dados temporais e visualização, nós melhoramos uma visualização compacta de series temporais para auxiliar a exploração de grandes conjuntos de dados espaçotemporais. Nossa proposta aproveita a compacticidade de tal representação para permitir o uso de um mapa para representar os atributos espaciais dos dados, de modo coordenado, enquanto representação, de forma compreensível, centenas de series simultaneamente, com total contexto temporal. Nós apresentamos nossa proposta como sendo capaz de auxiliar várias tarefas de caráter exploratório de forma intuitiva. Para defender essa afirmação, nós mostramos como essa ideia foi desenvolvida e melhorada ao longo do desenvolvimento de dois estudos de design visual em diferentes domínios de aplicação, e validamos com a implementação de protótipos que foram usados na análise exploratória de vários conjuntos de dados com 3 representações diferentes. Palavras- / The size of datasets became the major problem in data analysis today. As urban sensing becomes popular, datasets of spatial and temporal nature become ubiquitous, leading to several concerns regarding storage and management. It also creates a shift of paradigm in data analysis, as datasets that once represented a single series of measurements ordered in time are now composed of hundreds of series with ever increasing sampling rates. Also, as urban data usually presents inherent geographic disposition, most analysis tasks requires the support of proper spatial views. It becomes another problem, once that displaying technologies do not advance at the same of pace that sensing technologies do, and consequently, there is usually more data than visual space to represent it. After conducting exhaustive research on temporal data analysis and visualization, we improved a compact visual representation of time series to support the exploration of large spatio-temporal datasets. Our proposal exploits the compactness of such representation to allow the use of a map to represent the spatial properties of the data in a coordinate scheme while presenting, in a comprehensible manner, hundreds of series simultaneously, with full temporal context. We argue that such solution can effectively support many exploratory tasks in an intuitive manner. To support this claim, we show how the idea was conceived, and improved along the development of two design studies from different application domains, and validated by the implementation of prototypes used in the exploratory analysis of several datasets with 3 different data structures.

O tamanho dos conjuntos de dados se tornou um grande problema atualmente. À medida que o sensoriamento urbano ganha popularidade, os conjuntos de dados de natureza espacial e temporal se tornam ubíquos, e levantam uma série de questões relacionadas ao armazenamento e gerenciamento destes. Isso também cria uma mudança no paradigma de análise, uma vez que os conjuntos de dados que antes representavam uma única série de medições ordenadas no tempo, agora são compostos por centenas dessas séries, com uma taxa de amostragem que está aumentando constantemente. Além disso, uma vez que os dados urbanos normalmente apresentam disposição geográfica inerente, a maioria das das tarefas requerem o suporte de representações espaciais apropriadas. Este se torna outro problema, visto que as tecnologias de exibição de imagens não avançam na mesma velocidade das tecnologias de sensoriamento, de modo que consequentemente acaba-se tendo mais dados do que espaço visual para representa-los. Após conduzir uma pesquisa exaustiva a respeito de análise de dados temporais e visualização, nós melhoramos uma visualização compacta de series temporais para auxiliar a exploração de grandes conjuntos de dados espaçotemporais. Nossa proposta aproveita a compacticidade de tal representação para permitir o uso de um mapa para representar os atributos espaciais dos dados, de modo coordenado, enquanto representação, de forma compreensível, centenas de series simultaneamente, com total contexto temporal. Nós apresentamos nossa proposta como sendo capaz de auxiliar várias tarefas de caráter exploratório de forma intuitiva. Para defender essa afirmação, nós mostramos como essa ideia foi desenvolvida e melhorada ao longo do desenvolvimento de dois estudos de design visual em diferentes domínios de aplicação, e validamos com a implementação de protótipos que foram usados na análise exploratória de vários conjuntos de dados com 3 representações diferentes. Palavras- / The size of datasets became the major problem in data analysis today. As urban sensing becomes popular, datasets of spatial and temporal nature become ubiquitous, leading to several concerns regarding storage and management. It also creates a shift of paradigm in data analysis, as datasets that once represented a single series of measurements ordered in time are now composed of hundreds of series with ever increasing sampling rates. Also, as urban data usually presents inherent geographic disposition, most analysis tasks requires the support of proper spatial views. It becomes another problem, once that displaying technologies do not advance at the same of pace that sensing technologies do, and consequently, there is usually more data than visual space to represent it. After conducting exhaustive research on temporal data analysis and visualization, we improved a compact visual representation of time series to support the exploration of large spatio-temporal datasets. Our proposal exploits the compactness of such representation to allow the use of a map to represent the spatial properties of the data in a coordinate scheme while presenting, in a comprehensible manner, hundreds of series simultaneously, with full temporal context. We argue that such solution can effectively support many exploratory tasks in an intuitive manner. To support this claim, we show how the idea was conceived, and improved along the development of two design studies from different application domains, and validated by the implementation of prototypes used in the exploratory analysis of several datasets with 3 different data structures.

The mortality is historically one of the most important demographic indicator and definitely reflects the maturity of each country. The objective of this diploma thesis is the comparison of mortality rates in analyzed countries around the world over time and among each other using the principle component analysis that allows assessing data different way. The big advantage of this method is minimal loss of information and quite understandable interpretation of mortality in each country. This thesis offers several interesting graphical outputs, that for example confirm higher mortality rate in Eastern European countries compared to Western European countries and show that Czech republic is country where mortality has fallen most in context of post-communist countries between 1990 and 2010. Source of the data is Human Mortality Database and all data were processed in statistical tool SPSS.

No description available.

[pt] As visualizações de dados são cada vez mais comuns na mídia tradicional e nas redes sociais. No entanto, a alfabetização visual da população não acompanhou essa crescente popularidade. É necessário para quem cria os gráficos montar uma comunicação visual que contenha as informações necessárias de forma atrativa e de fácil compreensão. Em contrapartida, é necessário para quem os consome, captar as informações representadas pelos gráficos e extrair as análises do que vê. A importância da alfabetização visual é a capacidade de ler um gráfico, ou seja, olhar para um gráfico e identificar informações relevantes, tendências e discrepâncias em um determinado cenário. Neste trabalho, realizamos quatro estudos para explorar os fatores que influenciam o sucesso da análise de dados visuais. No primeiro estudo descobrimos como as pessoas tentam dar sentido a visualizações de dados específicas, através de perguntas que elas fazem ao encontrar uma visualização pela primeira vez. No segundo estudo exploramos como as distribuições de dados podem afetar a eficácia e eficiência das visualizações de dados. No terceiro estudo investigamos quando não especialistas identificam que uma visualização não é adequada para responder uma pergunta de análise específica, quando eles fazem boas sugestões de alteração para tornar essas visualizações adequadas e quando avaliam bem a adequação de algumas sugestões oferecidas a eles. No quarto estudo, criamos um teste para avaliar a compreensão das pessoas sobre os aspectos aplicados (responder perguntas de análise com o apoio de uma visualização) e conceituais (questões sobre a função e estrutura) da visualização de dados. Nossos resultados fornecem recursos para o desenvolvimento de material didático e ferramentas para recomendação de visualizações de dados relacionadas a perguntas que se visa responder. Uma contribuição adicional deste trabalho aos resultados dos estudos foi a estruturação de uma lista unificada de diferentes tarefas de visualização que encontramos na literatura. / [en] Data visualizations are increasingly common in traditional media and social networks. However, the visualization literacy of the population did not follow this growing popularity. It is necessary for those who create the charts to assemble a visual communication that contains the necessary information in an attractive and easy-to-understand way. By contrast, it is necessary for those who consume them to capture information represented by the charts and extract the analyses of what they see. The importance of visual literacy is the ability to read a chart, i.e., look at a chart and identify relevant information, trends, and outliers in a given scenario. In this work, we conducted four studies to explore factors related to the success of visual data analysis. We identified issues ranging from data distribution to formulating good questions to enrich exploration. The first study discovered how people try to make sense of specific data visualizations through questions they ask when they first encounter a visualization. In the second study, we explored how data distributions can affect the effectiveness and efficiency of data visualizations. In the third study, we investigated when non-experts identify that particular visualization is not adequate to answer a specific analysis question, when they make good suggestions for changes to make these visualizations adequate, and when they evaluated well the adequacy of some suggestions offered to them. In the fourth study, we created a test to assess people s understanding of the applied (answering analysis questions supported by a visualization) and conceptual (questions about function and structure) aspects of data visualization. Our results provide resources for developing of educational material and tools for recommending data visualizations to answer specific data-relation questions. An additional contribution of this work to the results of the studies was the structuring of a unified list of different visualization tasks that we found in the literature.