基于MapReduce的Skyline查询优化算法

Skyline Query Optimization Algorithm Based on MapReduce Frame

随着数据量的增加,Skyline查询在许多领域具有较高的实用价值。由于传统的Skyline算法在大数据情况下处理效率较低,论文研究了MapReduce编程框架下的Skyline查询算法,通过选取支配能力较强的数据点对原始数据集进行过滤,能够有效过滤大部分不能成为Skyline查询结果的数据点,同时保持全局变量的更新,减少数据点之间重复比较的次数,避免数据点的换入换出,提高了算法的效率。大量实验表明:算法具有良好的可用性和高效性。

With the increase of data,skyline query has potential practical value in many fields. This paper studies how to use MapReduce programming framework to improve the efficiency of Skyline query,because the traditional Skyline algorithm is inefficient in large data situation,the raw data set is filtered using data points that are more dominant,it can effectively filter most of the data points that cannot be Skyline query results. Global variables are updated at all times to reduce the number of repeated comparisons between data points and avoid data switching in and out,greatly improve the efficiency of the algorithm. A large number of experiments show that the algorithm has good availability and high efficiency.