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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (4): 281-294    DOI: 10.1631/jzus.C1101009
    
Improving SPARQL query performance with algebraic expression tree based caching and entity caching
Gang Wu, Meng-dong Yang
College of Information Science and Engineering, Northeastern University, Shenyang 110004, China; MOE Key Laboratory of Medical Image Computing, Northeastern University, Shenyang 110004, China; School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
Improving SPARQL query performance with algebraic expression tree based caching and entity caching
Gang Wu, Meng-dong Yang
College of Information Science and Engineering, Northeastern University, Shenyang 110004, China; MOE Key Laboratory of Medical Image Computing, Northeastern University, Shenyang 110004, China; School of Computer Science and Engineering, Southeast University, Nanjing 210096, China
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摘要： 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.
关键词： SPARQL;  Resource Description Framework (RDF);  Semantic caching;  Algebraic expression tree (AET);  Entity    
Abstract: 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.
Key words: SPARQL    Resource Description Framework (RDF)    Semantic caching    Algebraic expression tree (AET)    Entity
收稿日期: 2011-08-08 出版日期: 2012-04-07
CLC:  TP392  
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Gang Wu, Meng-dong Yang. Improving SPARQL query performance with algebraic expression tree based caching and entity caching. Front. Inform. Technol. Electron. Eng., 2012, 13(4): 281-294.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1101009    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I4/281

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