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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
To perform complex tasks, RDF Stream Processing Web applications evaluate continuous queries over streams and quasi-static (background) data. While the former are pushed in the application, the latter are continuously retrieved from the sources. As soon as the background data increase the volume and become distributed over the Web, the cost to retrieve them increases and applications become unresponsive. In this paper, we address the problem of optimizing the evaluation of these queries by<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-19890-3_20">doi:10.1007/978-3-319-19890-3_20</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kzpddczf3vevnmsqgdgzgvaei4">fatcat:kzpddczf3vevnmsqgdgzgvaei4</a> </span>
more »... aging local views on background data. Local views enhance performance, but require maintenance processes, because changes in the background data sources are not automatically reflected in the application. We propose a two-step query-driven maintenance process to maintain the local view: it exploits information from the query (e.g., the sliding window definition and the current window content) to maintain the local view based on user-defined Quality of Service constraints. Experimental evaluation show the effectiveness of the approach.
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