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With the dramatic increase of available data, the process of data processing should get higher and higher performance. Most researches on k-Nearest Neighbor (kNN) query algorithm are based on the regular partitioning method which is easy to cause the imbalance of load, even influence the overall performance of the kNN query algorithm. In addition, the traditional kNN query algorithm works on single process or single machine platforms, which cannot obtain high enough efficiency when dealing with big data. Aiming at these two issues, an irregular partitioning method based kNN algorithm is presented and being executed on the distributed parallel computing platform—MapReduce as of in this paper. Experiments show that the irregular partitioning method based kNN algorithm using MapReduce can obtain much higher performance and can guarantee a very efficient query when dealing with big data.

Irregular Partitioning Method Based k-Nearest Neighbor Query Algorithm Using MapReduce