This work has as its main focus the creation of resumes, how they are integrated into the hiring process, and how visual resumes play into the current world of recruitment and applications. On the side of applicants, there is a trend to create visualised resumes in the form of infographics whereas the recruiting side employs more and more automation technology that extracts the information contained in regular resumes and on social media sites in order to have to spend less time and therefore money on individualised resumes. Instead of viewing both these sides as opposites, this research tries to unify them and design a system that appeases both. The challenge is to find a way to create and use a new and innovative form of resume that benefits both recruiters and applicants. A new global specification created within a Community Group at the World Wide Web Consortium as well as a rudimentary implementation that can evolve into a rich ecosystem using that specification are introduced. Research for the topic had to be conducted from three different angles: the state of the art in research regarding recruitment and resumes, the state of the art in the industry, and currently existing resume standards, including the question whether they are suitable for the next-generation resume we are proposing. We are defining the core points of this next-generation resume that we labelled CV 2.0 and are proposing ways to reach a greater adoption of this resume in the future.
Visual analytics
Data wrangling generally denotes the cumbersome task of making data useful for analysis. Usually, this means applying hand-crafted scripts, requiring at least a certain degree of technical expertise. In order to make suchlike data preparation accessible for rather casual users as well, we have built a visual analytics prototype in the context of this thesis, easing related tasks. Our approach is an interdisciplinary one, combining contemporary concepts and ideas from various fields: human-computer interaction and usability engineering with information retrieval, data mining, machine learning, plus information visualization. In particular we focus on supporting transformations of time-oriented data since this kind of data exhibits unique characteristics which demand for special consideration. After analyzing related state of the art we identified open issues and derived requirements for such a system. We followed an iterative design process to develop a software prototype called TempMunger, in an agile manner. The process as well as corresponding artifacts are documented and presented in this thesis. Our prototype is a web-based application, tailored for desktop browser usage. More concretely, it offers interactive dashboard visualizations for preferably intuitive and exploratory transformation operations of time-oriented data. A qualitative evaluation of the prototype demonstrates its usefulness and reveals opportunities for future work. Concluding it is safe to say that data wrangling continues to be an exciting field of research where much is yet to be discovered.
As companies gather more and more data, we need to find a way to allow interested decision makers to access this data in an efficient way. In the context of sports practice, users could benefit from suggestions about new sports they could try out and the company could increase its sales. This work aims to support the analysts, simultaneously domain experts and IT laymen, in their data exploration and suggestion retrieval tasks through a user friendly interface, abstracting away the complexity of formulating expressive queries into the visual domain. We present a characterization and task analysis for this domain, and a prototype that meets the requirements emerging from them, based on an interdisciplinary literature research. The resulting prototype combines a visual query language with a collaborative filtering approach to render suggestions for new activities, and show multiple types of relationships in a visually compelling way. It has been implemented as a web application that handles the transformation of user input from a graphical pattern into a database query language and the results of this query into an easy to digest information representation. We conclude with an expert interview to validate the design for analysis and exploration.
More and more hospitals have been switching from paper-based patient records to electronic health records within the last years, which introduced new challenges for healthcare professionals. Each electronic record can hold a vast amount of diverse data, which can easily overwhelm a clinician in the stressful environment of a hospital. Consequently, important information can easily be missed or misinterpreted. These effects are especially critical for medication data, as medication errors can harm the treated patient directly. Proper information visualization is able to address these issues by providing cognitive support to healthcare professionals and facilitating insight into health records. The objectives of this thesis were to design a visualization of patient-specific medication histories and to implement an interactive prototype, which can be used in daily treatment settings. For this purpose, the characteristics and use cases of existing projects addressing
Currently, users who want to perform a complex analysis on graph data are dependent on knowledge about the data and complex command line interfaces. In order to simplify the complex analysis of graph data, appropriate tools have to be provided. Visual interaction increases the information content while at the same time it reduces the cognitive load on the user and supports him during query formulation. Numerous different approaches in literature make use of this fact. They reach from simple interactive visualization models to domain-specific visual query languages, but also reveal that there are some very similar developments to the proposed system. However, the combination of both aspects, i.e. the provision of an integrated system, allowing for graphically formulating complex queries and simultaneously supporting the user with visualizations could not be found in the literature. Hence the design of such a system makes a significant contribution to the research and development of graph databases. In this Thesis a design for such a system is developed with help of the query language for graph databases Gremlin and using a visual block metaphor. Based on this a prototype is implemented and evaluated.
Analyzing large, dynamic network data, such as the Database of Modern Exhibitions, presents significant challenges due to the dataset's scale and complexity, as it tracks a decade of European art exhibitions and encompasses thousands of artists with evolving relationships. Centrality measures help address this complexity by using algorithms to quantify the importance of each node. However, an important next step is to explore how these calculated importance metrics can be transformed into meaningful visual representations to extract insights and identify key actors.To achieve this, we conducted a state-of-the-art literature review to investigate how centrality measures are applied in data visualization, which informed the development of dome-insights, a visual analytics tool that integrates centrality measures into both its visualization and interaction design. Dome-insights serves as a prototype to demonstrate how incorporating centrality measures can enhance the exploration of large, complex networks, facilitating insight discovery and identification of key actors.The tool was assessed by art historians and delivered positive results in both quantitative and qualitative evaluations, demonstrating its ability to uncover meaningful insights. More broadly, the findings highlight the potential of centrality measures in dynamic network analysis, underscoring their role in enhancing visual analytics for complex network data.