When a FILTER Makes the Difference in Continuously Answering SPARQL Queries on Streaming and Quasi-Static Linked Data
We are witnessing a growing interest for Web applications that (i) require to continuously combine highly dynamic data stream with background data and (ii) have reactivity as key performance indicator. The Semantic Web community showed that RDF Stream Processing (RSP) is an adequate framework to develop this type of applications.
However, when the background data is distributed over the Web, even RSP engines risk losing reactiveness due to the time necessary to access the background data. State-of-the-art RSP engines remain reactive using a local replica of the background data, but such a replica progressively become stale if not updated to reflect the changes in the remote background data.
For this reason, recently, the RSP community investigated maintenance policies (collectively named Acqua) that guarantee reactiveness while maximizing the freshness of the replica. Acqua’s policies apply to queries that join a basic graph pattern in a window clause with another basic graph pattern in a service clause. In this paper, we extend the class of queries considered in Acqua adding a FILTER clause that selects mapping in the background data. We propose a new maintenance policy (namely, the Filter Update Policy) and we show how to combine it with Acqua policies. A set of experimental evaluations empirically proves the ability of the proposed policies to guarantee reactiveness while keeping the replica fresher than with the Acqua policies.
I would like to acknowledge the support of Soheila Dehghanzadeh and to thank her for the kind help in understanding the code base and the data set of Acqua.
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