Cardinality estimation for the optimization of queries on ontologies
ACM SIGMOD Record
The SPARQL Query Graph Model for Query Optimization
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Scalable join processing on very large RDF graphs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
| Hi-index | 0.00 |
Today RDF data are proliferating so fast that RDF query engines are faced with very large graphs that contain thousand million of RDF triples. Often there are a lot of joins should be processed when using RDF query language-SPARQL execute queries and the key issue for optimizing SPARQL execution plans is join ordering so selectivity estimation is very important to query cost. Exact estimation could optimize query and reduce query time, in contrast bad estimation could misguide the order of joins and increase query cost. In this paper we introduce two selectivity estimation methods: method based on histogram and method based on Index. We analyze the execute details of each method and compare the two methods then we give the conclusion that which method are better when execute query in large dataset. Finally, our experimental (using these two methods in different queries that have different join times in different size datasets.) testify our viewpoint.