MapReduce is a very popular parallel programming model for cloud computing platforms, and has become an effective method for processing massive data by using a cluster of computers. X-to-MapReduce (X is a program language) translator is a possible solution to help traditional programmers easily deploy an application to cloud systems through translating sequential codes to MapReduce codes. Recently, some SQL-to-MapReduce translators emerge to translate SQL-like queries to MapReduce codes and have good performance in cloud systems. However, SQL-to-MapReduce translators mainly focus on SQL-like queries, but not on numerical computation. Matlab is a high-level language and interactive environment for numerical computation, visualization, and programming, which is very popular in engineering. We propose and develop a simple Matlab-to-MapReduce translator for cloud computing, called M2M, for basic numerical computations. M2M can translate a Matlab code with up to 100 commands to MapReduce code in few seconds, which may cost a proficient Hadoop MapReduce programmer some days on coding so many commands. In addition, M2M can also recognize the dependency between complex commands, which is always confusing during hand coding. We implemented M2M with evaluation for Matlab commands on a cluster. Several common commands are used in our experiments. The results show that M2M is comparable in performance with hand-coded programs.
弱监督关系抽取利用已有关系实体对从文本集中自动获取训练数据,有效解决了训练数据不足的问题。针对弱监督训练数据存在噪声、特征不足和不平衡,导致关系抽取性能不高的问题,文中提出NF-Tri-training(Tritraining with Noise Filtering)弱监督关系抽取算法。它利用欠采样解决样本不平衡问题,基于Tri-training从未标注数据中迭代学习新的样本,提高分类器的泛化能力,采用数据编辑技术识别并移除初始训练数据和每次迭代产生的错标样本。在互动百科采集数据集上实验结果表明NF-Tri-training算法能够有效提升关系分类器的性能。
