Massive RDF Management Based On Column-oriented Database And Graph Cache

The development of Linked Data has led some areas publishing domainknowledge in RDF format, which brought the issue of how to organize and managemassive RDF datasets efficiently. Currently, the storage solutions based on RelationalDatabase or Graph Database of NoSQL, have shown query performance problems.Based on the above two mainstream storage model, this paper presents distributedstorage solution based on column-oriented Relational Database on disk, and constructRDF graph in memory.Firstly, the paper list some common RDF storage, analyzes advantages anddisadvantages of each, and discusses the feasibility of managing large-scale RDFdatasets based on column-oriented Relational Databases MonetDB and graph cache.Then it proposed the architecture: using vertical partitioning to store RDF datasetbased on MonetDB on disk, also, caching part of RDF data into memory based ongraph models; while querying, memory will be first to search, following by database,which reducing access to disk and query performance will be promoted. The mainachievement are following:(1) distributed RDF storage based on column-orienteddatabase;(2) building graph model of RDF in memory;(3)the solution of storagenode processing SPARQL queries, including cache lookup and database search;(4)merge query datasets and extract data needed;(5)validates the performanceadvantages in RDF datasets storage and query through open RDF datasets.