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Decomposing federated queries in presence of replicated fragments

机译:在存在复制片段的情况下分解联合查询

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Federated query engines allow for linked data consumption using SPARQL endpoints. Replicating data fragments from different sources enables data re-organization and provides the basis for more effective and efficient federated query processing. However, existing federated query engines are not designed to support replication. In this paper, we propose a replication-aware framework named LILAC, sparqL query decomposItion against federations of repLicAted data sourCes, that relies on replicated fragment descriptions to accurately identify sources that provide replicated data. We defined the query decomposition problem with fragment replication (QDP-FR). QDP-FR corresponds to the problem of finding the sub-queries to be sent to the endpoints that allows the federated query engine to compute the query answer, while the number of tuples to be transferred from endpoints to the federated query engine is minimized. An approximation of QDP-FR is implemented by the LILAC replication-aware query decomposition algorithm. Further, LILAC techniques have been included in the state-of-the-art federated query engines FedX and ANAPSID to evaluate the benefits of the proposed source selection and query decomposition techniques in different engines. Experimental results suggest that LILAC efficiently solves QDP-FR and is able to reduce the number of transferred tuples and the execution time of the studied engines. (C) 2016 Elsevier B.V. All rights reserved.
机译:联合查询引擎允许使用SPARQL端点链接数据。复制来自不同来源的数据片段可实现数据重组,并为更有效和高效的联合查询处理提供基础。但是,现有的联合查询引擎并不旨在支持复制。在本文中,我们提出了一个名为LILAC的可感知复制的框架,即针对已复制数据源联合的sparqL查询分解,它依赖于复制的片段描述来准确地标识提供复制数据的源。我们使用片段复制(QDP-FR)定义了查询分解问题。 QDP-FR对应于以下问题:找到要发送到端点的子查询,该查询允许联合查询引擎计算查询答案,同时将要从端点传输到联合查询引擎的元组的数量最小化。通过可识别LILAC复制的查询分解算法可实现QDP-FR的近似值。此外,LILAC技术已包含在最新的联合查询引擎FedX和ANAPSID中,以评估所提出的源选择和查询分解技术在不同引擎中的好处。实验结果表明,LILAC有效地解决了QDP-FR问题,并且能够减少所传输的元组的数量和所研究引擎的执行时间。 (C)2016 Elsevier B.V.保留所有权利。

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