Visual analytics

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.

The visualization and analysis of large graphs plays an essential role in various application fields. Since the size of graphs grew exponentially in the past few years, it became a challenge to reduce the visual clutter of dense and occluded graphs. By abstracting the structure of a node-link diagram, containing thousands of nodes and edges, visual clutter is reduced drastically, supporting the analysis of underlying patterns in an interactive approach. Additional visual techniques are used to overcome the challenge of representing the evolution of structural diagram changes and relationships between entities in dynamic graph visualization. The recent publications of large static and dynamic graph visualization techniques are using rich clients based on fast processing GPU algorithms, as well as distributed approaches for cluster-computing frameworks. Even though these techniques are capable of processing large-scale graphs interactively, they are also restricted by the user’s hardware or are more complex and expensive than simple client-server solutions. This thesis aims to provide an alternative approach, at providing a distributed, cross-platform, server-client application, able to visualize large node-link graphs, consisting of thousands of elements, interactively in a standard web-browser. We describe an aggregation strategy based on meta-elements, that provides an adjustable level of detail interface and visualizes the hierarchy of cumulative elements throughout multiple abstraction layers. By highlighting structural changes over time in dynamic graphs in combination with tools, such as panning and zooming and overview and detail, our system allows for dynamic graph exploration. We will demonstrate the usability of our technique by providing a complete prototype and present benchmarks on different graphs. Furthermore, we evaluate technical aspects of our approach as well as its applicability to large real-world graphs.

The digitization of our world provides us with a vast amount of data. This data allows us to construct accurate models of real world situations which are explored and analyzed to get a deeper understanding and eventually draw conclusions for our further actions. Multivariate networks are a particularly complex construct which are ubiquitous in many different subject areas, like social media, telecommunication, transport, finance, and demographics. These networks often have a spatial context attached to them and usually evolve over time. This fact makes it even harder to efficiently visualize the many aspects of such a network. This thesis aims to define and build a visualization of a multivariate network which changes over time and space. The underlying data network is composed of real-world movement data of citizens of Vienna from 2007 to 2018, provided by the city of Vienna, MA23. This data represents the change of residencies of people moving to, from, or within Vienna. To tackle the complexity of the many dimensions of this data such as time, space, and other attributes, like the country of birth of the moving people, we follow a user-centered design approach proposed by Miksch et al. The implemented prototype of the visualization focuses on two different user groups, which are people from the department for urban development on the one hand and the public on the other hand. Both groups may take interest in the relations between the districts and in understanding the migration flow over the years. In the design process, we focus on strengths and weaknesses of different visualization techniques to amplify the visual expressiveness of the key aspects of the data. Spatial information is encoded in a geographic map on which flows depict movements between areas. The design choices of these flows are essential to sustain readability. The temporal aspects are depicted with different time-series visualizations. Each of them focuses on the data from a different angle. Interactivity and interoperability between these visualizations ensure determined navigation through the various aspects of the migration data. We evaluated the visualization prototype with five experts in the field of Visual Analytics and one non-expert. The evaluation showed that the right combination of different visualization and interaction techniques results in an effective and appropriate visualization from which users can draw the desired insight.

The research area "Knowledge-Assisted Visual Analytics" (KAVA) deals with the integration of domain knowledge into Visual Analytics approaches which offers many advantages for research as well as for the analysis of data since analysts do not need to rely on their domain knowledge and can concentrate more on the analysis task itself. Especially in the health care sector, KAVA has great potential which is currently not fully exploited since there are only a few approaches that deal with KAVA in combination with health care data. To fill this gap, we propose a new KAVA approach dealing with a dataset that resulted from a clinical trial of a medication for treating the eye disease Uveitis to provide the possibility of exploring and analyzing the dataset. For designing and developing the approach a user-centered design process, involving a domain expert, in combination with problem-driven visualization research is applied. The final approach is validated using a qualitative task-oriented user study with five visualization experts. The results suggest that the approach is able to support the analysis as well as exploration of the dataset.