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摘要:
A lot of scholars have focused on developing effective techniques for package queries, and a lot of excellent approaches have been proposed. Unfortunately, most of the existing methods focus on a small volume of data. The rapid increase in data volume means that traditional methods of package queries find it difficult to meet the increasing requirements. To solve this problem, a novel optimization method of package queries(HPPQ) is proposed in this paper. First, the data is preprocessed into regions. Data preprocessing segments the dataset into multiple subsets and the centroid of the subsets is used for package queries, this effectively reduces the volume of candidate results. Furthermore, an efficient heuristic algorithm is proposed(namely IPOL-HS) based on the preprocessing results. This improves the quality of the candidate results in the iterative stage and improves the convergence rate of the heuristic algorithm. Finally, a strategy called HPR is proposed, which relies on a greedy algorithm and parallel processing to accelerate the rate of query. The experimental results show that our method can significantly reduce time consumption compared with existing methods.
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篇名 HPPQ: A Parallel Package Queries Processing Approach for Large-Scale Data
来源期刊 大数据挖掘与分析(英文) 学科 工学
关键词 PACKAGE QUERIES HEURISTIC algorithms PARALLEL processing opposition-based learning
年,卷(期) 2018,(2) 所属期刊栏目
研究方向 页码范围 146-159
页数 14页 分类号 TP311.13
字数 语种
DOI
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研究主题发展历程
节点文献
PACKAGE
QUERIES
HEURISTIC
algorithms
PARALLEL
processing
opposition-based
learning
研究起点
研究来源
研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
大数据挖掘与分析(英文)
季刊
2096-0654
10-1514/G2
出版文献量(篇)
91
总下载数(次)
3
总被引数(次)
0
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