摘要
提出一种MapReduce并行计算模型下基于R树索引的Skyline查询算法,解决了海量空间数据集下执行Skyline查询效率低的问题.通过建立R树索引实现空间数据不同粒度的范围剪枝,有效降低了分布式Skyline查询需扫描的数据规模,提高了在MapReduce模型下Skyline查询的执行效率.在不同数据分布下进行对比实验的结果表明,该方法比已有算法在执行效率上更具优势.
We proposed a Skyline query algorithm based on R-tree index in MapReduce parallel computing model, which solved the low execution efficiency problems of Skyline query in the massive spatial data sets. Through the establishment of R-tree index to realize spatial data of different size range pruning, the algorithm effectively reduced the size of the data required to scan the distributed Skyline query, and improved the execution efficiency of Skyline query in MapReduce model. Comparative tests in different data distribution shows the proposed method has more advantages on efficiency than the existing algorithms.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2016年第4期833-838,共6页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:61300147
61472159)
吉林省重点科技攻关项目(批准号:20140204010SF)
