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
Enhanced nearest neighbour search on the R-tree
ACM SIGMOD Record
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 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
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Location-based spatial queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Continuous K-nearest neighbor queries for continuously moving points with updates
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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The architecture named the GALIS is a cluster-based distributed computing system architecture which has been devised to efficiently handle a large volume of LBS application data. In this paper, we propose a distributed k-NN query processing scheme for moving objects on multiple computing nodes, each of which keeps records relevant to a different geographical zone. We also propose a hybrid k-NN scheme, which utilizes range queries instead of k-NN queries for the neighboring overlapped nodes, thus resulting in 30% reduction of query processing cost. Through some experiments, we show the efficiency of hybrid k-NN scheme over naïve k-NN scheme.