Distributed subgraph query for RDF graph data based on MapReduce
Distributed subgraph query for RDF graph data based on MapReduce
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基于MapReduce的RDF图数据分布式子图查询
DOI：
10.1016/j.compeleceng.2022.108221
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发表时间：
2022-09
影响因子：
4.3
通讯作者：
Ying Pan
中科院分区：
文献类型：
--
作者：
Qianxiang Su;Qingrong Huang;Nan Wu;Ying Pan
• A subgraph query algorithm for RDF graph data in a distributed environment is proposed. • A star decomposition method is used to decompose RDF graphs into star subgraphs. • The query efficiency is improved due to the query order of star subgraphs. • RDF query efficiency is improved by using adjacency lists to store RDF graphs distributed in multiple tables. Nowadays, Resource Description Framework (RDF) query has been widely used in social networks, biomedicine and other fields. With the explosion of RDF data due to the Internet of Things and Semantic Web, people's demand for intelligent computing and intelligent search is increasing, effectively querying RDF has become a major challenge. The current query methods often introduce a large number of join operations, and repeatedly traverse in some subgraphs during the query process, which makes the query efficiency and query performance poor. To address the above problems, this paper proposes a subgraph query algorithm for RDF graph data in a distributed environment. The graph structure is used to decompose the stars of the RDF graph, and the optimal query sequence of the stars is calculated. Fewer intermediate results can be produced based on the query sequence to reduce repeated calculations. Besides, adjacency lists are used to store RDF graphs, which are distributed across multiple tables. Multiple table operations can reduce the scope of subject node traversal, and further improve the query efficiency of RDF subgraph by matching one star per iteration. Experimental results show that our work can improve the query efficiency of RDF subgraphs.
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DOI：
10.1145/1940747.1940751
发表时间：
2010-10
期刊：
--
影响因子：
--
作者：
Kurt Rohloff;R. Schantz
通讯作者：
Kurt Rohloff;R. Schantz
使用 Pregel 在大知识图上进行分布式子图匹配
影响因子：
3.9
作者：
Qiang Xu;Xin Wang;Jianxin Li;Qingpeng Zhang;Lele Chai
通讯作者：
Lele Chai
DOI：
10.32604/jbd.2020.010358
发表时间：
2020
期刊：
Journal on Big Data
影响因子：
--
作者：
Aoran Li;Xinmeng Wang;Xueliang Wang;Bohan Li
通讯作者：
Aoran Li;Xinmeng Wang;Xueliang Wang;Bohan Li
DOI：
10.1109/aitc.2019.8920921
发表时间：
2019-11
期刊：
2019 International Conference on Advanced Information Technologies (ICAIT)
影响因子：
--
作者：
Khin Myat Kyu;Aung Nway Oo
通讯作者：
Khin Myat Kyu;Aung Nway Oo
DOI：
10.11896/j.issn.1002-137x.2019.04.005
发表时间：
2019
期刊：
计算机科学
影响因子：
--
作者：
Wen Xu;Wenai Song;Lizhen Fu;Wei Lv
通讯作者：
Wen Xu;Wenai Song;Lizhen Fu;Wei Lv