摘要： 现有Top-k查询算法主要运用在集中式关系型数据库中,当应用于分布式网络时会产生巨大的通信开销,导致算法效率低下。为此,提出一种改进的Top-k查询算法,利用预处理索引表对分布式网络中无关数据进行裁剪,在此基础上建立包含正确Top-k结果的候选子集并实现Top-k查询。实验结果表明,与Fagin和Naive Top-k查询算法相比,改进算法获得的查询结果更准确,运行时间更短,网络开销更小。

关键词: Top-k查询, 分布式网络, 数据裁剪策略, 预处理索引表, 大数据

Abstract: Existing Top-k query algorithms are mainly applied in the centralized relational database.However,the algorithms will cause huge communication costs and low efficiency in the distributed networks.In order to solve these problems,an improved Top-k Query Algorithm is proposed.This algorithm sets a Pretreatment Index Table(PIT) to cut the independent data out in the distributed networks,builds candidate subset which contains the correct Top-k results and realizes Top -k query based on it.Experimental result shows that the query results of this algorithm are more accurate,and it has shorter operation time and less network overhead compared with Fagin and Naive Top-k query algorithms.

Key words: Top-k query, distributed networks, data cutting strategy, Pretreatment Index Table(PIT), big data

中图分类号: 

• TP301