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A Parallel Computing Method for Entity Recognition based on MapReduce

机译:基于MapReduce的实体识别的并行计算方法

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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.
机译:随着工业自动化的快速发展,有大量的重复数据是指数据集中的同一实体在数据分析中带来了巨大的挑战。为了适应大量数据的实体识别,本文介绍了基于MapReduce的实体识别的并行计算方法。通过详细介绍MapReduce框架,在Hadoop平台上运行应用程序和并行处理数据集以识别数据实体。实验表明,该方法大大提高了识别速度和准确性,这具有更好的效果,以满足实体识别的需求而不是其他方法。

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