An AkNN Algorithm for High-Dimensional Big Data - Zhongwei Wang; Yefang Chen; Siyou Xiao; Jiangbo Qian (Dianxin kexue)
A new variant of k nearest neighbor queries，which called as all k-nearest neighbor queries（AkNN），is a process to search the k nearest neighbors of each object in a data set.An AkNN query algorithm for high-dimensional big data on the Hadoop system was proposed.Using the banding technique and the p-stable LSH algorithm，dimensionality reduction was performed，then the data was embeded in a Z-order curve.The preprocessed data were continued to be treated on a MapReduce framework in a distributed parallel manner.Experimental results show that the proposed algorithm can efficiently handle AkNN queries for large-scale high-dimensional data.