Incremental Observer Relative Data Extraction

摘要

The visual exploration of large databases calls for a tight coupling of database and visualization systems. Current visualization systems typically fetch all the data and organize it in a scene tree that is then used to render the visible data. For immersive data explorations in a Cave or a Panorama, where an observer is data space this approach is far from optimal. A more scalable approach is to make the observer-aware database system and to restrict the communication between the database and visualization systems to the relevant data. In this paper VR-tree, an extension of the R-tree, is used to index visibility ranges of objects. We introduce a new operator for incremental Observer Relative data Extraction (iORDE). We propose the Volatile Access STructure (VAST), a lightweight main memory structure that is created on the fly and is maintained during visual data explorations. VAST complements VR-tree and is used to quickly determine objects that enter and leave the visibility area of an observer. We provide a detailed algorithm and we also present experimental results that illustrate the benefits of VAST.