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Col-Graph: Towards Writable and Scalable Linked Open Data

机译:Col-Graph:迈向可写和可扩展的链接开放数据

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Linked Open Data faces severe issues of scalability, availability and data quality. These issues are observed by data consumers performing federated queries; SPARQL endpoints do not respond and results can be wrong or out-of-date. If a data consumer finds an error, how can she fix it? This raises the issue of the writability of Linked Data. In this paper, we devise an extension of the federation of Linked Data to data consumers. A data consumer can make partial copies of different datasets and make them available through a SPARQL endpoint. A data consumer can update her local copy and share updates with data providers and consumers. Update sharing improves general data quality, and replicated data creates opportunities for federated query engines to improve availability. However, when updates occur in an uncontrolled way, consistency issues arise. In this paper, we define fragments as SPARQL CONSTRUCT federated queries and propose a correction criterion to maintain these fragments incrementally without reevaluating the query. We define a coordination free protocol based on the counting of triples derivations and provenance. We analyze the theoretical complexity of the protocol in time, space and traffic. Experimental results suggest the scalability of our approach to Linked Data.
机译:链接开放数据面临可扩展性,可用性和数据质量的严峻问题。这些问题被执行联合查询的数据使用者所观察到。 SPARQL端点不响应,结果可能是错误的或过时的。如果数据使用者发现错误,如何解决?这就提出了链接数据的可写性问题。在本文中,我们设计了将链接数据联盟扩展到数据使用者的方法。数据使用者可以制作不同数据集的部分副本,并通过SPARQL端点使它们可用。数据使用者可以更新其本地副本,并与数据提供者和使用者共享更新。更新共享提高了常规数据的质量,复制的数据为联合查询引擎创造了提高可用性的机会。但是,如果以不受控制的方式进行更新,则会出现一致性问题。在本文中,我们将片段定义为SPARQL CONSTRUCT联合查询,并提出一种纠正准则,以在不重新评估查询的情况下逐步维护这些片段。我们根据三元组派生和来源的计数定义无协调协议。我们分析了该协议在时间,空间和流量方面的理论复杂性。实验结果表明我们链接数据方法的可扩展性。

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