A Parallel Computing Method for Entity Recognition based on MapReduce

DOI

10.2991/icence-16.2016.122How to use a DOI?

Keywords

Entity Recognition; Parallel Computing; MapReduce; Hadoop

Abstract

With the rapid development of industrial automation, there are huge amounts of duplicate data refer to the same entity in the data sets have brought enormous challenges in data analysis. To accommodate the entity recognition of huge amounts of data, this paper presents a parallel computing method for entity recognition based on MapReduce. Through the detailed introduction to the MapReduce framework, running the applications on the Hadoop platform and parallel processing data sets to recognize the data entities. The experiments show that the proposed method greatly enhanced the recognition speed and accuracy, which has better effectiveness to meet the demand for entity recognition than other methods.

Copyright

© 2016, 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  - Yushui Geng
AU  - Peng Li
AU  - Jing Zhao
PY  - 2016/09
DA  - 2016/09
TI  - A Parallel Computing Method for Entity Recognition based on MapReduce
BT  - Proceedings of the 2nd International Conference on Electronics, Network and Computer Engineering (ICENCE 2016)
PB  - Atlantis Press
SP  - 648
EP  - 653
SN  - 2352-538X
UR  - https://doi.org/10.2991/icence-16.2016.122
DO  - 10.2991/icence-16.2016.122
ID  - Geng2016/09
ER  -