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Executing SPARQL Queries over the Web of Linked Data

机译:通过链接数据的网络执行SPARQL查询

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The Web of Linked Data forms a single, globally distributed dataspace. Due to the openness of this dataspace, it is not possible to know in advance all data sources that might be relevant for query answering. This openness poses a new challenge that is not addressed by traditional research on federated query processing. In this paper we present an approach to execute SPARQL queries over the Web of Linked Data. The main idea of our approach is to discover data that might be relevant for answering a query during the query execution itself. This discovery is driven by following RDF links between data sources based on URIs in the query and in partial results. The URIs are resolved over the HTTP protocol into RDF data which is continuously added to the queried dataset. This paper describes concepts and algorithms to implement our approach using an iterator-based pipeline. We introduce a formalization of the pipelining approach and show that classical iterators may cause blocking due to the latency of HTTP requests. To avoid blocking, we propose an extension of the iterator paradigm. The evaluation of our approach shows its strengths as well as the still existing challenges.
机译:链接数据网形成单个全局分布的数据空间。由于此数据空间的开放性,因此不可能事先知道与查询应答可能相关的所有数据源。这种开放性提出了一个新的挑战,有关联合查询处理的传统研究无法解决这一挑战。在本文中,我们提出了一种通过链接数据网络执行SPARQL查询的方法。我们方法的主要思想是发现与查询执行本身有关的数据,这些数据可能与回答查询有关。通过在查询和部分结果中基于URI的数据源之间遵循RDF链接来驱动此发现。 URI通过HTTP协议解析为RDF数据,该数据连续添加到查询的数据集中。本文介绍了使用基于迭代器的管道来实现我们的方法的概念和算法。我们介绍了流水线方法的形式化,并表明经典的迭代器可能由于HTTP请求的延迟而导致阻塞。为了避免阻塞,我们提出了迭代器范式的扩展。对我们方法的评估表明了它的优势以及仍然存在的挑战。

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