Fedra：具有发散性的SPARQL联邦的查询处理
Fedra: Query Processing for SPARQL Federations with Divergence
摘要：
本地SPARQL端点的数据复制和部署提高了公共SPARQL端点的可伸缩性和可用性，使链接数据的消费成为现实。此解决方案需要同步和特定的查询处理策略来利用复制。然而，现有的复制感知技术在SPARQL端点的联邦不考虑数据的动态性。我们提出了Fedra，一种查询从复制中受益的端点联盟的方法。Fedra联邦的参与者可以从多个数据集中复制数据片段，并使用出处和视图描述它们。这些描述使Fedra能够减少所选端点的数量，同时满足用户的分散需求。在真实世界数据集上的实验表明，节省高达三个数量级。
英文摘要：
Data replication and deployment of local SPARQL endpoints improve scalability and availability of public SPARQL endpoints, making the consumption of Linked Data a reality. This solution requires synchronization and specific query processing strategies to take advantage of replication. However, existing replication aware techniques in federations of SPARQL endpoints do not consider data dynamicity. We propose Fedra, an approach for querying federations of endpoints that benefits from replication. Participants in Fedra federations can copy fragments of data from several datasets, and describe them using provenance and views. These descriptions enable Fedra to reduce the number of selected endpoints while satisfying user divergence requirements. Experiments on real-world datasets suggest savings of up to three orders of magnitude.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
