Although virtual reality hardware is now widely available, the uptake of real walking is hindered by the fact that it requires often impractically large amounts of physical space. To address this, we present VirtualSpace, a novel system that allows overloading multiple users immersed in different VR experiences into the same physical space. VirtualSpace accomplishes this by containing each user in a subset of the physical space at all times, which we call tiles; app-invoked maneuvers then shuffle tiles and users across the entire physical space. This allows apps to move their users to where their narrative requires them to be while hiding from users that they are confined to a tile. We show how this enables VirtualSpace to pack four users into 16m2. In our study we found that VirtualSpace allowed participants to use more space and to feel less confined than in a control condition with static, pre-allocated space.

We present TrussFab, an integrated end-to-end system that allows users to fabricate large scale structures that are sturdy enough to carry human weight. TrussFab achieves the large scale by complementing 3D print with plastic bottles. It does not use these bottles as "bricks" though, but as beams that form structurally sound node-link structures, also known as trusses, allowing it to handle the forces resulting from scale and load. TrussFab embodies the required engineering knowledge, allowing non-engineers to design such structures and to validate their design using integrated structural analysis. We have used TrussFab to design and fabricate tables and chairs, a 2.5 m long bridge strong enough to carry a human, a functional boat that seats two, and a 5 m diameter dome.

The blogosphere allows analysts to track opinions and sentiments of individuals, groups or the general public with large sample sizes regarding many topics. Essential for the sentiment analysis are visualizations. The visual understanding of large corpora's sentiment is far more effective than relying on textual representations of the analyzed content. Users are very interested in changes in the public opinion. Thus, the identification of patterns is of high interest. In this paper, we propose a cluster heat map visualization for sentiment visualization that displays the sentiment development of various related terms over time intervals. As we want to encourage the discovery of patterns over multiple related topics, we apply an ordering algorithm based on dimensionality reduction to the cluster heat map and improve upon the ordering algorithm to enable fast pattern recognition.