Bauhaus University, Weimar

About: Bauhaus University, Weimar is a education organization based out in Weimar, Thüringen, Germany. It is known for research contribution in the topics: Finite element method & Isogeometric analysis. The organization has 1421 authors who have published 2998 publications receiving 104454 citations. The organization is also known as: Bauhaus-Universität Weimar & Hochschule für Architektur und Bauwesen.

Collaborative interaction in co-located two-user scenarios

Abstract: We investigated the utility of co-located collaborative interaction metaphors for assembly tasks in the automotive industry In a first expert review, we compared the usability of a regular single-user stereoscopic system to a twouser system Our experiments revealed that the two-user system greatly facilitates basic collaborative interactions, when users are standing next to each other However, it is unsuitable for scenarios wherein users are facing each other and more sophisticated collaborative interactions are required Our second study focused on these types of collaborative assembly tasks using head mounted displays instead of our projection-based setup In a virtual assembly task, two workers had to mount the windshield of a car by using two different interaction methods The first method employed tangible props and the other method relied solely on virtual interaction Our evaluation shows that the multi-user prop-based interaction results in significantly higher accuracy and was clearly preferred by our users These results are an important step towards the acceptance of such virtual techniques for reliable ergonomic evaluations of virtual assembly tasks

Tradeoffs in modeling performance of highly configurable software systems

Abstract: Modeling the performance of a highly configurable software system requires capturing the influences of its configuration options and their interactions on the system’s performance. Performance-influence models quantify these influences, explaining this way the performance behavior of a configurable system as a whole. To be useful in practice, a performance-influence model should have a low prediction error, small model size, and reasonable computation time. Because of the inherent tradeoffs among these properties, optimizing for one property may negatively influence the others. It is unclear, though, to what extent these tradeoffs manifest themselves in practice, that is, whether a large configuration space can be described accurately only with large models and significant resource investment. By means of 10 real-world highly configurable systems from different domains, we have systematically studied the tradeoffs between the three properties. Surprisingly, we found that the tradeoffs between prediction error and model size and between prediction error and computation time are rather marginal. That is, we can learn accurate and small models in reasonable time, so that one performance-influence model can fit different use cases, such as program comprehension and performance prediction. We further investigated the reasons for why the tradeoffs are marginal. We found that interactions among four or more configuration options have only a minor influence on the prediction error and that ignoring them when learning a performance-influence model can save a substantial amount of computation time, while keeping the model small without considerably increasing the prediction error. This is an important insight for new sampling and learning techniques as they can focus on specific regions of the configuration space and find a sweet spot between accuracy and effort. We further analyzed the causes for the configuration options and their interactions having the observed influences on the systems’ performance. We were able to identify several patterns across subject systems, such as dominant configuration options and data pipelines, that explain the influences of highly influential configuration options and interactions, and give further insights into the domain of highly configurable systems.