无线传感网络中基于MapReduce的组合Skyline查询算法

G-Skyline query algorithm based on MapReduce in wireless sensor network

无线传感网络通过传感器节点能够收集到海量数据,利用组合Skyline查询技术可以在海量数据中获取以组合为单位的用户感兴趣的信息。然而,由于无线传感网络所处环境的不确定性及节点能量有限等问题,使得在海量数据下组合Skyline查询效率不太理想。针对该问题,提出一种基于MapReduce的组合Skyline查询算法(MR-GSKY算法),首先通过预处理操作去除无用点,然后将数据集分块,充分利用MapReduce分布式计算的特点,在Map阶段并行计算每一分块的键值对,再利用删减操作去除无用候选组合,在Reduce阶段执行扩展操作对不同键值对的组合进行整合并计算出组合Skyline的中间结果,通过多次Map和Reduce操作生成G-Skyline(n)。实验结果表明,该算法比现有算法具有更好的性能。

The wireless sensor network can collect a large amount of data through the sensor nodes,and G-Skyline query technology can obtain the user's interest information in the massive data.However,due to the uncertainty of the environment in which the wireless sensor network is located and the limited node energy,the efficiency of G-Skyline query under massive data is not ideal.To solve this problem,G-Skyline query algorithm based on MapReduce(MR-GSKY)is proposed.Firstly,the research removes the useless points through preprocessing operations.Then,the dataset is divided into blocks,making full use of the characteristics of MapReduce distributed computing,and the key-value pairs of each block are calculated in parallel in the Map phase.Subsequently,the deletion operation is used to remove useless candidate combinations.In the Reduce phase,the expansion operation is performed to integrate the combinations of different key-value pairs,and the intermediate results of G-Skyline are calculated,and G-Skyline(n)are generated through multiple Map and Reduce operations.Experimental results show that the proposed algorithm has better performance than existing algorithms.