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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.

In high-tech companies, user experience has become an important part of data-driven product development, but unfortunately the artifacts created by user experience work are often stored in disparate documents and databases, and it is difficult to get a big picture of all the artifacts that have been created and how they relate to each other and to other product development data. Data visualization is one way to approach solving these issues, but how can this data be presented to users in a meaningful way such that both the artifacts and the relationships between them are understood? Three hierarchical data visualizations - partition, sunburst, and zoomable treemap - were built around some early product development data. These visualizations were tested in a comparative exploratory usability test with six users to find how well users understood the data and the relationships between the data. The most significant results were that most users did not know how to interact with the partition and sunburst visualizations until prompted to do so, users had a difference in understanding the data between the sunburst and partition visualization, and some techniques worked very well to keep users oriented while others did not. Future work should first focus on adding a creative element to the tool, where data can be added and relationships can be built in the visualization itself, and then focus on testing the tool again with a more specific audience of strategic planners, UX designers, and requirements engineers.

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.

This thesis solves the area of data visualization in Business Intelligence. We live in a time when the amount of data grows turbulently. These data need to be processed into a form with added value for management and business objectives of the company. The first task is to adjust the data into the required format, the second task is to choose the appropriate visualization so that the data are providing the highest value. This work deals with data visualization and its main aim is to introduce a different approach to data visualization, which has a higher added value than conventional visualization methods used in dashboards and reports. First of all, theoretical part is describing data area (including the trend of Big Data) and reporting (chapter 3). Followed by chapter 4, which is enriching the reader with the information about the need to deal with data visualization and also with the design theory of visualization components. The theoretical part is finished with fifth chapter. It deals with current trends in the field of data visualization and Business Intelligence and also introduces D3.js library as a tool for creating interactive and efficient visualization components. The practical part is offering suggestions for interactive and dynamic visualization of input data (chapter 6). It presents a convenient way to choose a visualization component with greater value than the traditional static component in the form of a graph or table. Proposals contained in D3.js library are presented to the reader. Chosen components according to the author are interesting from the view of design and, if properly used, offer an innovative approach to data visualization. For purposes of practical examples of visualization, author is using D3.js library and open source tool for Business Intelligence from Pentaho. Practical example is part of the final chapter 7.

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.

<p>Visualizing multidimensional spatial data is an essential visual analysis strategy, it helps us interpret and communicate how different variables correlate to geographical information. In this study, we proposed an abstract contextual visualization that encodes data on the boundaries of spatial distributions and developed a new algorithm, AuroraMap. AuroraMap projects the spatial data to the boundaries of the distributions and color-encodes the densities continuously. We further conducted the user experiments, and the results show users can detect the relative locations and scopes of the clusters. Furthermore, users can quantitatively determine the peak value of each cluster’s density. The method provides three contributions: (1) freeing up and saving the graphical visualization space; (2) assisting the users to quantitatively estimate the clusters inside distributions; (3) facilitating the visual comparisons for multiple and multivariate spatial distributions. In the end, we demonstrated two applications with real-world religious infrastructural data by AuroraMap to visualize geospatial data within complex boundaries and compare multiple variables in one graph.</p><p> </p>