SPARQL查询优化的两阶段方法

J. Sensors Pub Date : 2022-08-25 DOI:10.1155/2022/4624856
Xiaoqing Lin, Dongyang Jiang
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引用次数: 0

摘要

随着来自不同领域的可用资源描述框架(RDF)数据的快速增长,使用图结构的SPARQL查询处理变得越来越重要。为此,我们设计了一种两阶段的SPARQL查询优化方法来处理SPARQL查询。利用RDF数据图的结构特征,即谓词路径序列索引(pps - indexes)对搜索空间进行有效的裁剪,在数据库更新的同时捕捉到RDF数据图的内在特征。我们的存储模型是基于关系数据库的。与基线解决方案相比,该方法有效地降低了查询处理过程中中间结果的基数,过滤性能至少提高了一个数量级,从而提高了查询执行效率。
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A Two-Phase Method for Optimization of the SPARQL Query
With a rapid growth in the available resource description framework (RDF) data from disparate domains, the SPARQL query processing with graph structures has become increasingly important. In this pursuit, we designed a two-phase SPARQL query optimization method to process the SPARQL query. The structural characteristics of RDF data graphs, predicate path sequence indices (PPS-indices), were used to efficiently prune the search space, which captured the inherent features of the RDF data graphs, while the database is updated. Our storage model was based on a relational database. Compared to a baseline solution, the proposed method effectively reduced the cardinalities of the intermediate results during the query processing, and at least an order of magnitude improvement is achieved in filtering performance, thereby improving the efficiency of the query execution.
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