A framework and class library (GAV Flash) implemented in Adobe’s ActionScript is introduced, designed with the intention to significantly shorten the time and effort needed to develop customized web-enabled applications for geovisual analytics tasks. Through an atomic layered component architecture, GAV Flash provides a collection of interactive geo- and information visualization representations for exploring high dimensional spaces and extended with motion behavior. Versatile interaction methods are drawn from many data visualization research areas and optimized for dynamic web visualization of spatio-temporal and multivariate data. Based on layered component thinking and the use of programming interface mechanism the GAV Flash architecture is open and facilitates the creation of new or improved versions of existing components so that ideas can be tried out or optimized rapidly in a fully functional environment. Not only is GAV Flash a tool for interactive visualization but also supports storytelling around visual analytics in which visual representations not only serve as a discovery tool for individuals but also as a mean to share stories among users fostering a social style of collaborative data analysis. A mechanism “snapshot” for saving the explorative results of a reasoning process is introduced that aids collaboration and publication of gained insight and knowledge embedded as dynamic visualizations in blogs or web pages with associative metadata or “storytelling”.

In humans as well as animals, emotions greatly influence decision making on both a conscious and subconscious level. In this paper we use findings within psychology and neuroscience to create a model for modeling and using emotion maps for agent pathfinding.We propose a model that combines these emotion maps depending on the agent’s current emotions.We aim for a more varied and more natural agent behavior. Our hope is to create interesting behavior for games or game-like scenarios.

We use the theory of group representations to construct very fast image descriptors that split the vector space of local RGB distributions into small group-invariant subspaces. These descriptors are group theoretical generalizations of the Fourier Transform and can be computed with algorithms similar to the FFT. Because of their computational efficiency they are especially suitable for retrieval, recognition and classification in very large image datasets. We also show that the statistical properties of these descriptors are governed by the principles of the Extreme Value Theory (EVT). This enables us to work directly with parametric probability distribution models, which offer a much lower dimensionality and higher resolution and flexibility than explore the connection to EVT and analyse the characteristics of these descriptors from a probabilistic viewpoint with the help of large image databases.