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Parallel Optimization of KNN Query Strategy based on Road Network

Volume 14, Number 10, October 2018, pp. 2366-2373
DOI: 10.23940/ijpe.18.10.p12.23662373

Boqi Hua, Hailong Suna,b, Fangsong Lib, Chao Jiangb, and Weitao Zoua

aCollege of Information and Computer Engineer, Northeast Forestry University, Harbin, 150040, China
bComputing Center of Heilongjiang Province, Harbin, 150026, China

(Submitted on July 12, 2018; Revised on August 15, 2018; Accepted on September 10, 2018)


K-nearest neighbor (KNN) query is one of the most important query types in spatial databases and have been widely used in intelligent transportation, roadside assistance, and other fields. In order to improve the query efficiency, in this paper we adopted the MapReduce parallel computing framework of the Hadoop large data processing platform and completed the query of K neighbor moving objects by designing Map, Reduce, Combiner, and other functions. Before the start of the query, the road network was divided into pieces, and each fragment was calculated. The final K-nearest neighbor moving objects were obtained by aggregating the calculated results of each slice to realize the parallel optimization of the KNN algorithm based on road network. The experimental results showed that the performance of the parallel KNN algorithm based on MapReduce was better than that of the serial KNN query algorithm in a large-scale road network environment and a larger K value of query requests.


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