Stereoscopic 3D graph visualization for assisted data exploration and discovery

Abstract

Data structures and relations are becoming increasingly complex and difficult to assess and manage. Although automated rules and algorithms can be used for many data-mining tasks, there are still situations where human attention and insight is required to identify unexpected circumstances or unanticipated patterns. Presentation of large quantities of data has always been a challenging task. In this paper a method for representing large graph-based data sets is proposed to help users navigate through large clusters of data. The proposed method is based on a stereoscopic 3D visualization with special enhancements for a large multi node graph visualization. The stereoscopic projection allows for utilization of techniques that can draw users’ attention to particular regions of the graph. The method uses specially established node-node relations to calculate attention drawing factor values for each graph node.