Visualization

Time is a complex dimension, especially when trying to visualize it. In the last ten years a lot of approaches to display and interact with temporal data have been published. They range from linear timeline visualizations to novel ideas employing visual metaphors and even clustering techniques to support the user in exploring large-scale data sets. The diversity of the proposed methods has raised the awareness that a common categorization needs to be defined to efficiently evaluate the usability and interactivity of information visualization tools.

Natural and cultural cycles, like day and night, weekday and weekend, years and seasons, define our life and become perceivable in every analysis of human behavior. This work documents reimplementation and extension of Groove (granular overview overlay), a visualization technique to gain insights on several levels of detail in complex, time-oriented data at a single glance. A powerful framework was implemented, dealing with common data-related tasks and providing an extensible visualization and interaction pipeline. Based on that framework, a visualization adapting the calendar-analogy was implemented to show the frameworks benefits and resulting easements for future studies. In the conclusion we sketch possible future extensions and the usability of the Groove-Framework.

This master thesis describes the evaluation of an interactive information visualization technique that is capable of displaying quantitative attributes (numeric values) of multivariate data over time and corresponding qualitative abstractions (interpretations) of the quantitative values (SemTimeZoom). The integration of interpretations and a-priori knowledge in the form of qualitative abstractions is especially useful in the medical domain. Vital parameters of patients can be analyzed using predefined domain knowledge and the resulting interpretations can be visualized together with raw numerical measurements.

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

With the increase of remote work due to COVID-19 and the overall movement towards open source projects, distributed version control system, like Git gained popularity overthe last years. The publicly available data on platforms (e.g., GitHub) therefore becomes richer and attracts sociologists and software analysts for further analysis.This master thesis aims to visualize GitHub trends using Visual Analytics. The data used originates from the GitHub API as well as GitHub Archive, is multivariate and contains different types of information containing repositories, users and events. This data will be extended by the temporal dimension to identify potential trends. For the problem definition and further methodology, the design triangle as described by Mikschet. al is being used.The outcome of the thesis is a prototype, that not only enables domain experts to fulfill common tasks related to identifying GitHub anomalies and trends but also allows foruser interaction to focus on more granular analysis. While many trends can potentially be visualized, this thesis will focus on a small subset of trends to introduce a generic approach and evaluate it on given scenarios and tasks. The general group of potential user groups is broad, but there is a strong emphasis on analysts in technology industries.The prototype was evaluated with domain experts in different fields of expertise that were asked to perform given tasks that can be fulfilled using the developed prototype. The results of the evaluation showed, that there is a strong interest in the analysis of GitHub data and that the right encodings and visualization methods can help find patterns and trends significantly.

In art historical research, the study of social networks can provide insights into complex interactions between artists. However, despite the successful use of network visualizations in the field of art history, there are still many open questions as well as challenges that need to be solved in order to support art historians in the best possible way for their research. For example, existing approaches offer only limited possibilities to visually explore art historical networks with regard to their geographical context as well as their temporal development. Furthermore, so-called node-link diagrams are usually used for the visual representation of networks, which in the past revealed weaknesses in comparison with other representations in various studies. Finally, it is difficult to evaluate applications with respect to their suitability in the art history domain, since established evaluation approaches are often not conducted with domain experts. We present Exhibitions Explorer, a solution which enables the interactive, visual exploration of art historical networks. The solution integrates different visualization approaches from research into a new visualization concept that focuses on the requirements of the domain. Using an insight-based evaluation approach, we also demonstrate that, provided a suitable visualization concept, network visualizations can lead to a high quantity and quality of domain-relevant insights.