To obtain comparable high query performance with relational databases,diverse database technologies have to be adapted to confront the complexity posed by both Resource Description Framework(RDF) data and SPARQL query...To obtain comparable high query performance with relational databases,diverse database technologies have to be adapted to confront the complexity posed by both Resource Description Framework(RDF) data and SPARQL query.Database caching is one of such technologies that improves the performance of database with reasonable space expense based on the spatial/temporal/semantic locality principle.However,existing caching schemes exploited in RDF stores are found to be dysfunctional for complex query semantics.Although semantic caching approaches work effectively in this case,little work has been done in this area.In this paper,we try to improve SPARQL query performance with semantic caching approaches,i.e.,SPARQL algebraic expression tree(AET) based caching and entity caching.Successive queries with multiple identical sub-queries and star-shaped joins can be efficiently evaluated with these two approaches.The approaches are implemented on a two-level-storage structure.The main memory stores the most frequently accessed cache items,and items swapped out are stored on the disk for future possible reuse.Evaluation results on three mainstream RDF benchmarks illustrate the effectiveness and efficiency of our approaches.Comparisons with previous research are also provided.展开更多
基金supported by the National Natural Science Foundation of China (Nos.60903010,61025007,and 60933001)the National Basic Research Program (973) of China (No.2011CB302206)+2 种基金the Natural Science Foundation of Jiangsu Province,China (No.BK2009268)the Fundamental Research Funds for the Central Universities (No.N110404013)the Key Laboratory of Advanced Information Science and Network Technology of Beijing (No.XDXX1011)
文摘To obtain comparable high query performance with relational databases,diverse database technologies have to be adapted to confront the complexity posed by both Resource Description Framework(RDF) data and SPARQL query.Database caching is one of such technologies that improves the performance of database with reasonable space expense based on the spatial/temporal/semantic locality principle.However,existing caching schemes exploited in RDF stores are found to be dysfunctional for complex query semantics.Although semantic caching approaches work effectively in this case,little work has been done in this area.In this paper,we try to improve SPARQL query performance with semantic caching approaches,i.e.,SPARQL algebraic expression tree(AET) based caching and entity caching.Successive queries with multiple identical sub-queries and star-shaped joins can be efficiently evaluated with these two approaches.The approaches are implemented on a two-level-storage structure.The main memory stores the most frequently accessed cache items,and items swapped out are stored on the disk for future possible reuse.Evaluation results on three mainstream RDF benchmarks illustrate the effectiveness and efficiency of our approaches.Comparisons with previous research are also provided.
