Approximate Continuous Query Answering Over Streams and Dynamic Linked Data Sets
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. But as soon as the background data increase the volume and become distributed over the Web, the cost to retrieve them increases, and consequently applications are at risk of becoming unresponsive. In this paper, we address the problem of optimizing the evaluation of these queries by leveraging local views on background data. This is proven to enhance the performance of the query processor but requires the introduction of a maintenance process, because changes in the background data sources are not automatically reflected in the local views. We propose a two-step query-driven maintenance process to maintain the local view. The process exploits information from the query (e.g., the sliding window definition and the current window content) to maintain the local view on-demand based on user-defined Quality of Service constraints on the response. Experimental comparisons on synthetic and real data show the effectiveness of the proposed approach.
Friday, 6 March, 2015 (All day)