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|Title||SPARQL Query Optimization Using Selectivity Estimation|
|Abstract Text||This poster describes three static SPARQL optimization approaches for in-memory RDF graphs: (1) a selectivity estimation index (SEI) for single query triple patterns; (2) a query pattern index (QPI) for joined triple patterns; and (3) a hybrid optimization approach that combines both indexes. Using the Lehigh University Benchmark (LUBM), we show that the hybrid approach outperforms other SPARQL query engines such as ARQ and Sesame for in-memory graphs.|