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In this paper, we propose a plugin-based framework for RDF stream processing named PRSP. Within this framework, we can employ SPARQL query engines to process C-SPARQL queries with maintaining the high performance of those engines in a simple way. Taking advantage of PRSP, we can process large-scale RDF streams in a distributed context via distributed SPARQL engines. Besides, we can evaluate the performance and correctness of existing SPARQL query engines in handling RDF streams in a united way, which amends the evaluation of them ranging from static RDF (i.e., RDF graph) to dynamic RDF (i.e., RDF stream). Finally, within PRSP, we experimently evaluate the correctness and the performance on YABench. The experiments show that PRSP can still maintain the high performance of those engines in RDF stream processing although there are some slight differences among them.
In the real world datasets (e.g.,DBpedia query log), queries built on well-designed patterns containing only AND and OPT operators (for short, WDAO-patterns) account for a large proportion among all SPARQL queries. In this paper, we present a plugin-
Incremental processing is widely-adopted in many applications, ranging from incremental view maintenance, stream computing, to recently emerging progressive data warehouse and intermittent query processing. Despite many algorithms developed on this t
A growing number of applications that generate massive streams of data need intelligent data processing and online analysis. Real-time surveillance systems, telecommunication systems, sensor networks and other dynamic environments are such examples.
The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is significant adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has pave
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