The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Efficient index support for view-dependent queries on CFD data
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
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Virtual reality techniques particularly in the field of CFD (computational fluid dynamics) are of growing importance due to their ability to offer comfortable means to interactively explore 3D data sets. The growing accuracy of the simulations brings modern main memory based visualization frameworks to their limits, inducing a limitation on CFD data sizes and an increase in query response times, which are obliged to be very low for efficient interactive exploration. We therefore developed "IndeGS", the index supported graphics data server, to offer efficient dynamic view dependent query processing on secondary storage indexes organized by "IndeGS" offering a high degree of interactivity and mobility in VR environments in the context of CFD postprocessing on arbitrarily sized data sets. Our demonstration setup presents "IndeGS" as an independent network component which can be addressed by arbitrary VR visualization hardware ranging from complex setups (e.g. CAVE, HoloBench) over standard PCs to mobile devices (e.g. PDAs). Our demonstration includes a 2D visualization prototype and a comfortable user interface to simulate view dependent CFD postprocessing performed by an interactive user freely roaming a fully immersive VR environment. Hereby, the effects of the use of different distance functions and query strategies integrated into "IndeGS" are visualized in a comprehensible way.