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
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Fast parallel similarity search in multimedia databases
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Parallel processing of nearest neighbor queries in declustered spatial data
GIS '96 Proceedings of the 4th ACM international workshop on Advances in geographic information systems
Similarity query processing using disk arrays
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Enhanced nearest neighbour search on the R-tree
ACM SIGMOD Record
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
On a Nearest-Neighbor Problem Under Minkowski and Power Metrics for Large Data Sets
The Journal of Supercomputing - Special issue on computational issues in fluid dynamics optimization and simulation
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Declustering Spatial Databases on a Multi-Computer Architecture
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
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Even though the problem of k nearest neighbor (kNN) query is well-studied in serial environment, there is little prior work on parallel kNN search processing in parallel one. In this paper, we present the first Best-First based Parallel kNN (BFPkNN) query algorithm in a multi-disk setting, for efficient handling of kNN retrieval with arbitrary values of k by parallelization. The core of our method is to access more entries from multiple disks simultaneously and enable several effective pruning heuristics to discard non-qualifying entries. Extensive experiments with real and synthetic datasets confirm that BFPkNN significantly outperforms its competitors in both efficiency and scalability.