Resource description framework(RDF)stream is useful to model spatio-temporal data.In this paper,we propose a framework for large-scale RDF stream processing,LRSP,to process general continuous queries over large-scale ...Resource description framework(RDF)stream is useful to model spatio-temporal data.In this paper,we propose a framework for large-scale RDF stream processing,LRSP,to process general continuous queries over large-scale RDF streams.Firstly,we propose a formalization(named CT-SPARQL)to represent the general continuous queries in a unified,unambiguous way.Secondly,based on our formalization we propose LRSP to process continuous queries in a common white-box way by separating RDF stream processing,query parsing,and query execution.Finally,we implement and evaluate LRSP with those popular continuous query engines on some benchmark datasets and real-world datasets.Due to the architecture of LRSP,many efficient query engines(including centralized and distributed engines)for RDF can be directly employed to process continuous queries.The experimental results show that LRSP has a higher performance,specially,in processing large-scale real-world data.展开更多
To decrease the time of generating a closure, a parallel algorithm of generating the closure of a resource description framework schema （RDFS） source is presented. In the algorithm, RDFS triples in the source are cl...To decrease the time of generating a closure, a parallel algorithm of generating the closure of a resource description framework schema （RDFS） source is presented. In the algorithm, RDFS triples in the source are classified according to the forms of triples in the entailment rules and it reduces the scope of searching for specific triples. The dependence among the classes of triples is analyzed. Based on the classification, the initial RDFS source is partitioned into several subsets. The subsets are distributed to each process, and the closure is generated in parallel by applying the RDFS entailment rules. Parallel generating the closure of an RDFS source takes less time and increases efficiency.展开更多
基金the National Key Research and Development Program of China under Grant No.2017YFC0908401the National Natural Science Foundation of China under Grant No.61672377the program of Peiyang Young Scholars of China under Grant No.2019XRX-0032.
文摘Resource description framework(RDF)stream is useful to model spatio-temporal data.In this paper,we propose a framework for large-scale RDF stream processing,LRSP,to process general continuous queries over large-scale RDF streams.Firstly,we propose a formalization(named CT-SPARQL)to represent the general continuous queries in a unified,unambiguous way.Secondly,based on our formalization we propose LRSP to process continuous queries in a common white-box way by separating RDF stream processing,query parsing,and query execution.Finally,we implement and evaluate LRSP with those popular continuous query engines on some benchmark datasets and real-world datasets.Due to the architecture of LRSP,many efficient query engines(including centralized and distributed engines)for RDF can be directly employed to process continuous queries.The experimental results show that LRSP has a higher performance,specially,in processing large-scale real-world data.
基金Supported by the National High-Tech Research and Development Plan of China under Grant No.2004AA112010 (国家高技术研究发展计划(863))the National Basic Research Program of China under Grant No.2002CB312005 (国家重点基础研究发展计划(973))
基金The Weaponry Equipment Foundation of PLA Equipment Ministry (No.51406020105JB8103).
文摘To decrease the time of generating a closure, a parallel algorithm of generating the closure of a resource description framework schema （RDFS） source is presented. In the algorithm, RDFS triples in the source are classified according to the forms of triples in the entailment rules and it reduces the scope of searching for specific triples. The dependence among the classes of triples is analyzed. Based on the classification, the initial RDFS source is partitioned into several subsets. The subsets are distributed to each process, and the closure is generated in parallel by applying the RDFS entailment rules. Parallel generating the closure of an RDFS source takes less time and increases efficiency.
