Incremental Observer Relative Data Extraction
Publication: Research › Article in proceeding
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.
| Original language | English |
|---|---|
| Title | Key Technologies for Data Management : Lecture Notes in Computer Science |
| Editors | Howard Williams, Lachlan MacKinnon |
| Publisher | Springer Berlin / Heidelberg |
| Publication date | 2004 |
| Edition | 3112-2004 |
| Pages | 165 - 177 |
| ISBN (print) | 3540223827 |
| State | Published |
Conference
| Conference | 21st British National Conference on Databases |
|---|---|
| Nummer | 21 |
| Land | United Kingdom |
| By | Edingburgh |
| Periode | 07-07-04 → 09-07-04 |
Keywords
- data extraction, indexing visibility ranges
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