Efficient Subgraph Matching on Large RDF Graphs Using MapReduce

Efficient Subgraph Matching on Large RDF Graphs Using MapReduce
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使用 MapReduce 对大型 RDF 图进行高效子图匹配

DOI:
10.1007/s41019-019-0090-z
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发表时间:
2019-03
影响因子:
4.2
通讯作者:
Yunpeng Chai
Yunpeng Chai
中科院分区:
--
文献类型:
--
作者:
Xin Wang;Lele Chai;Qiang Xu;Yajun Yang;Jianxin Li;Junhu Wang;Yunpeng Chai

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随着知识图的迅速普及,大量的RDF图被发布,这就需要解决分布式子图匹配查询的挑战。在本文中,我们提出了一种高效的分布式方法,使用MapReduce来回答大型RDF图上的子图匹配查询。在我们的方法中,查询图被分解成一组星,这些星利用嵌入RDF图的语义和结构信息作为启发式方法。为了进一步提高算法的效率,提出了两种优化技术。一种称为drdf属性过滤的算法,过滤掉无效的输入数据以减少中间结果;二是通过延迟笛卡尔积运算来提高查询性能。在合成数据集和真实数据集上进行的大量实验表明,我们的方法平均优于竞争对手S2X和SHARD一个数量级。
With the popularity of knowledge graphs growing rapidly, large amounts of RDF graphs have been released, which raises the need for addressing the challenge of distributed subgraph matching queries. In this paper, we propose an efficient distributed method to answer subgraph matching queries on big RDF graphs using MapReduce. In our method, query graphs are decomposed into a set ofstarsthat utilize the semantic and structural information embedded RDF graphs as heuristics. Two optimization techniques are proposed to further improve the efficiency of our algorithms. One algorithm, calledRDF property filtering, filters out invalid input data to reduce intermediate results; the other is to improve the query performance by postponing the Cartesian product operations. The extensive experiments on both synthetic and real-world datasets show that our method outperforms the close competitors S2X and SHARD by an order of magnitude on average.
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期刊: --
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