.png){kind=icon}
返回
Efficient Processing of Skyline Queries Using MapReduce
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2017.2654459.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The skyline operator has attracted considerable attention recently due to its broad applications. However, computing a skyline is challenging today since we have to deal with big data. For data-intensive applications, the MapReduce framework has been widely used recently. In this paper, we propose the efficient parallel algorithm SKY-MR+ for processing skyline queries using MapReduce. We first build a quadtree-based histogram for space partitioning by deciding whether to split each leaf node judiciously based on the benefit of splitting in terms of the estimated execution time. In addition, we apply the dominance power filtering method to effectively prune non-skyline points in advance. We next partition data based on the regions divided by the quadtree and compute candidate skyline points for each partition using MapReduce. Finally, we check whether each skyline candidate point is actually a skyline point in every partition using MapReduce. We also develop the workload balancing methods to make the estimated execution times of all available machines to be similar. We did experiments to compare SKY-MR+ with the state-of-the-art algorithms using MapReduce and confirmed the effectiveness as well as the scalability of SKY-MR+.
Keyword:
Skyline queries
MapReduce algorithms
distributed and parallel algorithms
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
.png){kind=icon}
查看更多内容