Scalable SPARQL querying using path partitioning
Scalable SPARQL querying using path partitioning
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DOI：
10.1109/icde.2015.7113334
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发表时间：
2015-04
期刊：
影响因子：
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通讯作者：
Buwen Wu;Yongluan Zhou;Pingpeng Yuan;Ling Liu;Hai Jin
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文献类型：
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作者：
Buwen Wu;Yongluan Zhou;Pingpeng Yuan;Ling Liu;Hai Jin
对大型 RDF 数据集进行复杂分析的新兴需求需要能够利用计算集群来处理大型 RDF 数据集的横向扩展解决方案。对 RDF 数据的查询通常涉及复杂的自连接，如果数据没有在集群中仔细分区，那么运行起来将非常昂贵，因此需要对大量数据进行分布式连接。现有的 RDF 数据分区方法可以很好地本地化简单查询，但对于更复杂的查询仍然需要采用昂贵的分布式连接。在本文中，我们提出了一种新的数据分区方法，该方法利用 RDF 数据集中丰富的结构信息，并最大限度地减少必须跨不同计算节点连接的数据量。我们使用两个流行的 RDF 基准数据和一个包含多达数十亿个 RDF 三元组的真实 RDF 数据集进行了广泛的实验研究。结果表明，我们的方法可以产生平衡且低冗余的数据分区方案，即使对于非常复杂的查询也可以避免或很大程度上降低分布式连接的成本。就查询执行时间而言，我们的方法可以比最先进的方法高出几个数量级。
The emerging need for conducting complex analysis over big RDF datasets calls for scale-out solutions that can harness a computing cluster to process big RDF datasets. Queries over RDF data often involve complex self-joins, which would be very expensive to run if the data are not carefully partitioned across the cluster and hence distributed joins over massive amount of data are necessary. Existing RDF data partitioning methods can nicely localize simple queries but still need to resort to expensive distributed joins for more complex queries. In this paper, we propose a new data partitioning approach that takes use of the rich structural information in RDF datasets and minimizes the amount of data that have to be joined across different computing nodes. We conduct an extensive experimental study using two popular RDF benchmark data and one real RDF dataset that contain up to billions of RDF triples. The results indicate that our approach can produce a balanced and low redundant data partitioning scheme that can avoid or largely reduce the cost of distributed joins even for very complicated queries. In terms of query execution time, our approach can outperform the state-of-the-art methods by orders of magnitude.