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
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Time-parameterized queries in spatio-temporal databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
Out-of-core rendering of massive geometric environments
Proceedings of the conference on Visualization '02
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
The LSDh-Tree: An Access Structure for Feature Vectors
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Walking Through a Very Large Virtual Environment in Real-time
Proceedings of the 27th International Conference on Very Large Data Bases
Generalized Search Trees for Database Systems
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
3D visual data mining: goals and experiences
Computational Statistics & Data Analysis - Data visualization
Efficient approximate visibility query in large dynamic environments
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
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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, which is then used to render the visible data. For immersive data explorations, where an observer navigates in a potentially huge data space and explores selected data regions this approach is inadequate. A scalable approach is to make the database system observer-aware and exchange the data that is visible and most relevant to the observer.In this paper we present iTopN an incremental algorithm for extracting the most visible objects relative to the current position of the observer. We implement iTopN and compare it to an improved version of the R-tree that extends LRU with the caching of the top levels of the R-tree (LW-LRU). Our experiments show that iTopN is orders of magnitude faster than LW-LRU given the same amount of memory. Our experiments also show that for LW-LRU to perform as fast as iTopN it needs three times as much memory.