Abstract. 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 maximising 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 polices to guarantee reactiveness while keeping the replica fresher than with the Acqua policies.
Web applications that combine dynamic data stream with distributed background data are getting a growing attention in recent years. Answering in a timely fashion, i.e., reactiveness, is one of the most important performance indicators for those applications.The Semantic Web community showed that RDF Stream Processing (RSP) is an adequate framework to develop this type of applications. However, RSP engines may lose their reactiveness due to the time necessary to access the background data when it is distributed over the Web. State-of-the-art RSP engines remain reactive using a local replica of the background data, but it progressively becomes stale if not updated to re ect the changes in the remote background data. For this reason, recently, the RSP community has investigated maintenance policies of the local replica that guarantee reactiveness while maximizing the freshness of the replica. Previous works simpli ed the problem with several assumptions.In this paper, we investigate how to remove some of those simplication assumptions. In particular, we target a class of queries for which multiple policies may be used simultaneously and we show that rank aggregation can be e ectively used to fairly consider their alternative suggestions. We provide extensive empirical evidence that rank aggregation is key to move a step forward to the practical solution of this problem in the RSP context.
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