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Optimizing Multi-Query Evaluation in Federated RDF Systems

机译:优化联合RDF系统中的多查询评估

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This paper revisits the classical problem of multiple query optimization in federated RDF systems. We propose a heuristic query rewriting-based approach to optimize the evaluation of multiple queries. This approach can take advantage of SPARQL 1.1 to share the common computation of multiple queries while considering the cost of both query evaluation and data shipment. Although we prove that finding the optimal rewriting for multiple queries is NP-complete, we propose a heuristic rewriting algorithm with a bounded approximation ratio. Furthermore, we propose an efficient method to use the interconnection topology between RDF sources to filter out irrelevant sources, and utilize some characteristics of SPARQL 1.1 to optimize multiple joins of intermediate matches. The extensive experimental studies show that the proposed techniques are effective, efficient and scalable.
机译:本文重新审视了联合RDF系统中多种查询优化的经典问题。我们提出了一种启发式查询重写的基于方法,以优化对多个查询的评估。这种方法可以利用SPARQL 1.1来共享多个查询的共同计算,同时考虑查询评估和数据发货的成本。虽然我们证明了解多个查询的最佳重写是NP-Tremines,但我们提出了一种具有界近似比的启发式重写算法。此外,我们提出了一种有效的方法来使用RDF源之间的互连拓扑以滤除不相关的源,并利用SPARQ11.1的一些特征来优化中间匹配的多个连接。广泛的实验研究表明,该技术是有效,高效且可扩展的。

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