A Framework For RDF Stream Distributed Processing

RDF stream,as a new type of dataset,can model real-time and continuous information in a wide range of applications,e.g.,environmental monitoring.However,the stream data is different from the traditional static data.We must consider the timeliness,vulnerability and order of data.Therefore,it is attracting more and more attention to deal with it effectively,and it is also an urgent problem to be solved.This paper studies the processing technology of RDF stream,presents a simple method for processing RDF stream,and makes the process of data transformation and query processing separate.Therefore,we can exploit different SPARQL engines to process RDF stream in order to meet the requirements of different scenarios for users,and implement a framework for RDF stream distributed querying named PRSP and a framework for RDF stream distributed reasoning named PRSPR.The evaluation of C-SPARQL queries on RDF streams can be reduced to the evaluation of SPARQL queries on RDF graphs.We prove that the reduction is sound and complete.Moreover,taking advantage of PRSP,we can process large-scale RDF streams in a distributed context via distributed SPARQL engines.Furthermore,PRSPR is an extension of PRSP by adding another new RDFS reasoned for RDF stream reasoning.Within PRSPR,not only can we apply all kinds of SPARQL query engines to process RDF streams,but support various inference engines for RDFS/OWL.Finally,a large number of experiments are carried out in the YABench RSP.The experiments show that PRSP and PRSPR can still maintain the high performance with SPARQL engines in RDF stream processing although there are some slight differences among them.And PRSPR can obtain more implicit knowledge by using the RDFS reasoned.Furthermore,the method has a good application in the inertial navigation case.In summary,the RDF stream processing framework based on white boxes separates the data transformation and processing of RDF streams,which can make the process for RDF stream more quickly and efficiently.And it is more flexible and convenient by embedding different SPARQL query engines to meet user's requirement of different scenarios.What is more,the method can make the applications about RDF stream more and more widely and take a key step in the processing of RDF streams.