- GORDER: An Efficient Method for KNN Join Processing

https://doi.org/10.1016/B978-012088469-8.50067-XGet rights and content

Publisher Summary

This chapter introduces an efficient method for k-Nearest Neighbor (KNN) Join Processing—Gorder. KNN is an important but very expensive primitive operation of high-dimensional databases. Gorder is a block nested loop join method that exploits sorting, join scheduling, and distance computation filtering and reduction to reduce both I/O and CPU costs. It sorts input datasets into the G-order and applies the scheduled block nested loop join on the G-ordered data. The distance computation reduction is employed to further reduce CPU cost. It is simple and yet efficient, and handles high-dimensional data efficiently. The chapter conducts investigations on both synthetic cluster and real life datasets, and the results illustrate that Gorder is an efficient KNN-join method and outperforms existing methods by a wide margin.

References (0)

Cited by (54)

View all citing articles on Scopus
View full text