Exploring nonlinear data relationships in vr using the 3d visual data mining system

Abstract. A software system has been developed for the study of static and dynamic data visualization in the context of Visual Data Mining in Virtual Reality. We use a specific data set to illustrate how the visualization tools of the 3D Visual Data Mining (3DVDM) system can assist in detecting potentially interesting non-linear data relationships that are hard to discover using traditional statistical methods of analysis. These detected data structures can form a basis for specification of further explanatory statistical analysis. The visualization tools are shown to reveal many interesting patterns and in particular the dynamic data visualization appears to have a very promising potential. The results encourage further developments of the current and new tools in the context of practical applications.

M.: Using dynamic soundscapes to support visual data mining in vr

Abstract. Data Mining in Virtual Reality (VR) has, for good reasons, been focused on facilitating visual inspection and analysis. As the amount and complexity of data is overwhelming, it is worthwhile to consider a further exploration of the human perceptual faculties, and it seems natural to consider sound as a possible perceptual cue. Current technology enables us to create advanced real-time 3D soundscapes, which may prove useful since the human ears ’ field of hearing is larger than the eyes ’ field of view, and thus is able to inform us on events happening in areas that we do no see at a given time. This paper presents methods for generating 3D soundscapes from statistical information. Through a series of investigations, we present and discuss the effectiveness of these methods in situations where sound acts as support for visual cues, as well as the use of sound as a separate cue for analyzing data in VR.