This thesis addresses the problem of action recognition through the analysis of human motion and the benchmarking of its imitation by robotic systems. For our action recognition related approaches, we focus on presenting approaches that generalize well across different sensor modalities. We transform multivariate signal streams from various sensors to a common image representation. The action recognition problem on sequential multivariate signal streams can then be reduced to an image classification task for which we utilize recent advances in machine learning. We demonstrate the broad applicability of our approaches formulated as a supervised classification task for action recognition, a semi-supervised classification task for one-shot action recognition, modality fusion and temporal action segmentation. For action classification, we use an EfficientNet Convolutional Neural Network (CNN) model to classify the image representations of various data modalities. Further, we present approaches for filtering and the fusion of various modalities on a representation level. We extend the approach to be applicable for semi-supervised classification and train a metric-learning model that encodes action similarity. During training, the encoder optimizes the distances in embedding space for self-, positive- and negative-pair similarities. The resulting encoder allows estimating action similarity by calculating distances in embedding space. At training time, no action classes from the test set are used. Graph Convolutional Network (GCN) generalized the concept of CNNs to non-Euclidean data structures and showed great success for action recognition directly operating on spatio-temporal sequences like skeleton sequences. GCNs have recently shown state-of-the-art performance for skeleton-based action recognition but are currently widely neglected as the foundation for the fusion of various sensor modalities. We propose incorporating additional modalities, like inertial measurements or RGB features, into a skeleton-graph, by proposing fusion on two different dimensionality levels. On a channel dimension, modalities are fused by introducing additional node attributes. On a spatial dimension, additional nodes are incorporated into the skeleton-graph. Transformer models showed excellent performance in the analysis of sequential data. We formulate the temporal action segmentation task as an object detection task and use a detection transformer model on our proposed motion image representations. Experiments for our action recognition related approaches are executed on large-scale publicly available datasets. Our approaches for action recognition for various modalities, action recognition by fusion of various modalities, and one-shot action recognition demonstrate state-of-the-art results on some datasets. Finally, we present a hybrid imitation learning benchmark. The benchmark consists of a dataset, metrics, and a simulator integration. The dataset contains RGB-D image sequences of humans performing movements and executing manipulation tasks, as well as the corresponding ground truth. The RGB-D camera is calibrated against a motion-capturing system, and the resulting sequences serve as input for imitation learning approaches. The resulting policy is then executed in the simulated environment on different robots. We propose two metrics to assess the quality of the imitation. The trajectory metric gives insights into how close the execution was to the demonstration. The effect metric describes how close the final state was reached according to the demonstration. The Simitate benchmark can improve the comparability of imitation learning approaches.

In this thesis the possibilities for real-time visualization of OpenVDB files are investigated. The basics of OpenVDB, its possibilities, as well as NanoVDB and its GPU port, were studied. A system was developed using PNanoVDB, the graphics API port of OpenVDB. Techniques were explored to improve and accelerate a single ray approach of ray tracing. To prove real-time capability, two single scattering approaches were also implemented. One of these was selected, further investigated and optimized to achieve interactive real-time rendering. It is important to give artists immediate feedback on their adjustments, as well as the possibility to change all parameters to ensure a user friendly creation process. In addition to the optical rendering, corresponding benchmarks were collected to compare different improvement approaches and to prove their relevance. Attention was paid to the rendering times and memory consumption on the GPU to ensure optimal use. A special focus, when rendering OpenVDB files, was put on the integrability and extensibility of the program to allow easy integration into an existing real-time renderer like U-Render.

Currently, there are a variety of digital tools in the humanities, such as annotation, visualization, or analysis software, which support researchers in their work and offer them new opportunities to address different research questions. However, the use of these tools falls far short of expectations. In this thesis, twelve improvement measures are developed within the framework of a design science theory to counteract the lack of usage acceptance. By implementing the developed design science theory, software developers can increase the acceptance of their digital tools in the humanities context.

The Web is an essential component of moving our society to the digital age. We use it for communication, shopping, and doing our work. Most user interaction in the Web happens with Web page interfaces. Thus, the usability and accessibility of Web page interfaces are relevant areas of research to make the Web more useful. Eye tracking is a tool that can be helpful in both areas, performing usability testing and improving accessibility. It can be used to understand users' attention on Web pages and to support usability experts in their decision-making process. Moreover, eye tracking can be used as an input method to control an interface. This is especially useful for people with motor impairment, who cannot use traditional input devices like mouse and keyboard. However, interfaces on Web pages become more and more complex due to dynamics, i.e., changing contents like animated menus and photo carousels. We need general approaches to comprehend dynamics on Web pages, allowing for efficient usability analysis and enjoyable interaction with eye tracking. In the first part of this thesis, we report our work on improving gaze-based analysis of dynamic Web pages. Eye tracking can be used to collect the gaze signals of users, who browse a Web site and its pages. The gaze signals show a usability expert what parts in the Web page interface have been read, glanced at, or skipped. The aggregation of gaze signals allows a usability expert insight into the users' attention on a high-level, before looking into individual behavior. For this, all gaze signals must be aligned to the interface as experienced by the users. However, the user experience is heavily influenced by changing contents, as these may cover a substantial portion of the screen. We delineate unique states in Web page interfaces including changing contents, such that gaze signals from multiple users can be aggregated correctly. In the second part of this thesis, we report our work on improving the gaze-based interaction with dynamic Web pages. Eye tracking can be used to retrieve gaze signals while a user operates a computer. The gaze signals may be interpreted as input controlling an interface. Nowadays, eye tracking as an input method is mostly used to emulate mouse and keyboard functionality, hindering an enjoyable user experience. There exist a few Web browser prototypes that directly interpret gaze signals for control, but they do not work on dynamic Web pages. We have developed a method to extract interaction elements like hyperlinks and text inputs efficiently on Web pages, including changing contents. We adapt the interaction with those elements for eye tracking as the input method, such that a user can conveniently browse the Web hands-free. Both parts of this thesis conclude with user-centered evaluations of our methods, assessing the improvements in the user experience for usability experts and people with motor impairment, respectively.

The flexible integration of information from distributed and complex information systems poses a major challenge for organisations. The ontology-based information integration concept SoNBO (Social Network of Business Objects) developed and presented in this dissertation addresses these challenges. In an ontology-based concept, the data structure in the source systems (e.g. operational application systems) is described with the help of a schema (= ontology). The ontology and the data from the source systems can be used to create a (virtualised or materialised) knowledge graph, which is used for information access. The schema can be flexibly adapted to the changing needs of a company regarding their information integration. SoNBO differs from existing concepts known from the Semantic Web (OBDA = Ontology-based Data Access, EKG = Enterprise Knowledge Graph) both in the structure of the company-specific ontology (= Social Network of Concepts) as well as in the structure of the user-specific knowledge graph (= Social Network of Business Objects) and makes use of social principles (known from Enterprise Social Software). Following a Design Science Research approach, the SoNBO framework was developed and the findings documented in this dissertation. The framework provides guidance for the introduction of SoNBO in a company and the knowledge gained from the evaluation (in the company KOSMOS Verlag) is used to demonstrate its viability. The results (SoNBO concept and SoNBO framework) are based on the synthesis of the findings from a structured literature review and the investigation of the status quo of ontology-based information integration in practice: For the status quo in practice, the basic idea of SoNBO is demonstrated in an in-depth case study about the engineering office Vössing, which has been using a self-developed SoNBO application for a few years. The status quo in the academic literature is presented in the form of a structured literature analysis on ontology-based information integration approaches. This dissertation adds to theory in the field of ontology-based information integration approaches (e. g. by an evaluated artefact) and provides an evaluated artefact (the SoNBO Framework) for practice.