As pessoas estão ficando cada vez mais interessadas no uso de monitores de atividade. A quantidade de dados de indivíduos disponível está ajudando na expansão e desenvolvimento de novas aplicações e projetos de visualizações para ser usados em casa, em ciência (e.g. para entender melhor o comportamento de populações) ou em governos interessados em desenvolver cidades inteligentes. Nesse trabalho é apresentada uma visualização simples e intuitiva que permite a exploração de dados pessoais por pessoas comuns. Com foco em ajudar as pessoas a compreenderem a si mesmas melhor e perceber coisas novas sobre seus dados. A visualização construída neste projeto é baseada em metáforas de calendários, relógios e mapas, além de utilizar gráficos de barra para explorar dados crus. A exploração desses dados se dá pela interação entre essas visualizações. Para avaliar o produto do trabalho são apresentados dois casos de uso onde alguns usuários tiveram a oportunidade de observar e discutir suas informações de dois pontos de vista diferente: exploração de dados pessoais para auto-aperfeiçoamento e o uso do Everyday Visualization por cientistas da saúde. Em nenhum dos casos houve treinamento. As visualizações resultantes agregam diversas fontes de dados, indo além de outros trabalhos de visualização casual e pessoal. Os resultados promissores demonstram a viabilidade de tais técnicas para visualização de dados pessoais. / People are becoming increasingly more interested in the use of activity monitors and selfimprovement. The availability of individuals’ data is also pushing the development of new applications and data visualization projects to be used at home, in science (e.g. to better understand the behavior of populations) or for governments interested in developing intelligent cities. In this work, we present an easy and intuitive set of visualizations to allow the exploration of personal data by common people. We focus on helping people to know themselves better and to make sense of their own data. Our visualizations are based on the metaphors of calendars, clocks, and maps, as well as on the use of bar charts to explore raw data. Data exploration is therefore guaranteed by the interaction between them. In order to evaluate our work we present two use cases, where few users observe and discuss the data from different points of view: the exploration of personal data for self-improvement purposes, and the use of Everyday Visualization by health scientists. Both use cases were ran without any training session. The resulting visualization aggregates several different data sources, going beyond many of the personal and casual visualization works. The promising results achieved demonstrated the viability of the use of such techniques for personal data visualizations and sense making.

abstract: In visualizing information hierarchies, icicle plots are efficient diagrams in that they provide the user a straightforward layout for different levels of data in a hierarchy and enable the user to compare items based on the item width. However, as the size of the hierarchy grows large, the items in an icicle plot end up being small and indistinguishable. In this thesis, by maintaining the positive characteristics of traditional icicle plots and incorporating new features such as dynamic diagram and active layer, we developed an interactive visualization that allows the user to selectively drill down or roll up to review different levels of data in a large hierarchy, to change the hierarchical structure to detect potential patterns, and to maintain an overall understanding of the current hierarchical structure. / Dissertation/Thesis / Masters Thesis Computer Science 2014

This work describes the design and implementation of space weather related phenomena within the interactive astro-visualization software OpenSpace. Data sets from the Community Coordinated Modelling Center (CCMC) at the National Aeronautics and Space Administration (NASA) were used to implement time-varying high-resolution solar imagery from space observatory spacecraft and time-varying field lines from the different models produced at the CCMC. The obtained results were used to take an audience on an interactive journey through the solar system, at the world’s first ever live planetarium show about space weather.

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In recent years, it is becoming more common for people to use applications or devices that keep track of their activities, such as fitness activities, places they visit, the music they listen to, and pictures they take. These data are used by the services for various purposes, but usually there are limitations for the users to explore or interact with them. This project investigates a new approach of visualizing such Quantified Self data, in a meaningful and enjoyable way that gives the users insights into their data. This thesis discusses the feasibility of creating a system that allows users to connect the activity tracking applications they already use, analyse the amount of activities, and then present the resulting information. The visualization of the information is done with an avatar that maps the different activities the user is engaged with, along with the activity levels, as graphical features. Within the scope of this work, several user studies were conducted and a system prototype was implemented to explore how to build, using web technologies, such a system that aggregates and analyses personal activity data, and also to determine what kind of data should and can be collected, to provide meaningful information to the users. Furthermore, it was investigated how a possible design for the avatar could look like, to be clearly understood by the users.

To guarantee the well-functioning of modern societies, it is essential to ensure thatcitizens are aware of their environment as well as the government's actions andperformance. Therefor citizens need to have access to data collected by publicbodies from the activities within their society. However, these data may not easilyand instantly be perceivable by people. To release the potential of these data,which lead to improving the lives of citizen, the data need to analyzed, enhancedand eventually visualized, so the outcome would be usable to all regardless theirage and education. In this project, we propose, design, implement, and evaluate aninteractive analytical visualization framework for open-data. To achieve our purpose,the recent open data-sets from Sundsvall Municipality has been used as bothour case-study and demonstration sample. The solution has been design and builtbased on a set of requirements and principles we suggested for an ideal solution.The offered solution in this project enables users to analyze data by interactingwith the visualization graphics. The solution let the user extract information andfacts from large data-sets, and correlate different property of data by applying filtersand categorizing data, and see the result instantly. Standard web technologieswere used to implement the solution, namely HTML5, JavaScript, and SVGgraphics, which made the system to be cross-platform and high-performing. Tohandle the big-data, we enhance the data-set before-hand by performing aggregateoperations using Map-Reduce and parallel computing algorithms (i.e. Java-8 parallelstreams), and the further computation is implemented in JavaScript, and processedat the client-side.

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.

As a result of the rapid evolution of smart mobile devices and the wide application of satellite-based positioning devices, the moving object database (MOD) has become a hot research topic in recent years. The moving objects generate a large amount of geo-referenced data in different types, such as videos, audios, images and sensor logs. In order to better analyze and utilize the data, it is useful and necessary to visualize the data on a map. With the rise of web mapping, visualizing the moving object and geo-referenced data has never been so easy. While displaying the trajectory of a moving object is a mature technology, there is little research on visualizing both the location and data of the moving objects in a synchronized manner. This dissertation proposes a general moving object visualization model to address the above problem. This model divides the spatial data visualization systems into four categories. Another contribution of this dissertation is to provide a framework, which deals with all these visualization tasks with synchronization control in mind. This platform relies on the TerraFly web mapping system. To evaluate the universality and effectiveness of the proposed framework, this dissertation presents four visualization systems to deal with a variety of situations and different data types.

Diploma thesis describes basics terms of data visualization elements in the theoretical part. There are the following main topics covered -- importance of data visualization in business management, description and application of main chart types, design and usage of complex visualizations (dashboards), key features of modern data visualization technology. Theoretical grounds are then applied on real examples from different companies in order to show how managers can benefit from good data visualization techniques. Examples from significantly different areas were chosen -- financial management, operations management, analysis of process efficiency.

Since there is limited research on the use of data dashboards in the evaluation field, this study explores the integration of a data dashboard in a knowledge network, the Centre for Research on Educational and Community Services (CRECS) as part of its program evaluation activities. The study used three phases of data collection and analysis. It investigates the process of designing a dashboard for a knowledge network and the different uses of a data dashboard in a program evaluation context through interviews and focus group discussions. Four members of the CRECS team participated in one focus group; two other members participated in individual interviews. Data were analyzed for thematic patterns. Results indicate that the process of designing a data dashboard consists of five steps that indicate the iterative process of design and the need for sufficient consultations with stakeholders. Moreover, the data dashboard has the potential to be used internally, within CRECS, and externally with other stakeholders. The data dashboard is also believed to be beneficial in program evaluation context as a monitoring tool, for evaluability assessment, and for evaluation capacity building. In addition, it can be used externally for accountability, reporting, and communication. The study sheds light on the potentials of data dashboards in organizations, yet prolonged and broader studies should take place to confirm these uses and their sustainability.