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Adaptive Stream Query Processing Approach for Linked Stream Data: (Extended Abstract)

机译:链接流数据的自适应流查询处理方法:(扩展摘要)

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Over the last few years, numerous efforts have been proposed based on SPARQL-like query languages on harvesting Linked Stream Data (LSD) processing in RDF and related formats. While each existing processor has advantages, neither of them wins in diverse settings. They differ on a wide range of aspects including the execution method, operational semantics, streaming operators and more. Considering state-of-the-art solutions, recent evaluations by show that C-SPARQL suffers from duplicate results for simple queries and misses some certain output in complex queries but provides more correct results than others. On the otherhand CQELS performs better than others in terms of throughput and functionalities. This diversity in output result is true for other processors including EP-SPARQL and StreamingSPARQL.
机译:在过去的几年中,基于类SPARQL的查询语言,已经提出了许多努力来收集RDF和相关格式的链接流数据(LSD)处理。尽管每个现有处理器都具有优势,但它们都无法在不同的环境中获胜。它们在很多方面有所不同,包括执行方法,操作语义,流运算符等。考虑到最先进的解决方案,最近的评估显示,C-SPARQL的简单查询结果重复,并且在复杂查询中缺少某些输出,但比其他查询提供了更正确的结果。另一方面,就吞吐量和功能而言,CQELS的性能要优于其他产品。输出结果的这种多样性对于其他处理器(包括EP-SPARQL和StreamingSPARQL)都是正确的。

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