Efficient Parallel Processing of High-Dimensional Spatial KNN Queries
Some efficient top-k algorithms, i.e., Fagin's Algorithm, threshold algorithm (TA), and best position algorithm (BPA), can be used to answer k nearest neighbor (kNN) queries. However, extending the existing algorithms without further changes to the algorithms themselves would not be efficient since there are the different characteristics between the kNN queries and top-k queries. For example, the kNN queries are more distance-sensitive rather than the position of data points. Second, it is necessary to add some novel parallel heuristics and pruning policies for the kNN queries. Third, there are still many redundant random accesses among FA, TA, and BPA. In this paper, we address aforementioned these problems and take these algorithms to answer parallel kNN (PkNN) queries in spatial databases. We integrate the advantages of the B + -tree and Open MP programming and propose three efficient parallel kNN query algorithms, namely distance priority-based PkNN, optimized PkNN, and partition-based PkNN. Our performance evaluation shows that our proposed algorithms achieve significant improvement in comparison with existing algorithms, i.e., BPA and BPA2. In addition, our approaches are also capable of returning kNN results incrementally which greatly shorten the query response time and enhance user experience.
T. Jiang et al., "Efficient Parallel Processing of High-Dimensional Spatial KNN Queries," Soft Computing, Springer Verlag, Jan 2022.
The definitive version is available at https://doi.org/10.1007/s00500-022-07081-0
Keywords and Phrases
Algorithm; Nearest Neighbor Queries; Parallel; Performance; Spatial Database
International Standard Serial Number (ISSN)
Article - Journal
© 2022 Springer, All rights reserved.
01 Jan 2022
This work was supported in part by the ZJNSF Grant LY16F020026, NSFC Grants. 61522208 and 61379033.