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

DOI

10.2991/isci-15.2015.237How to use a DOI?

Keywords

big data; k-Nearest Neighbor (kNN) query algorithm; irregular partitioning method; MapReduce

Abstract

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.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Qingqing Zhang
AU  - Changyun Li
AU  - Pinjie He
AU  - Xu Li
AU  - Haojie Zou
PY  - 2015/01
DA  - 2015/01
TI  - Irregular Partitioning Method Based k-Nearest Neighbor Query Algorithm Using MapReduce
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 1786
EP  - 1794
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.237
DO  - 10.2991/isci-15.2015.237
ID  - Zhang2015/01
ER  -