Abstract
With RDF knowledge base and SPARQL have been widely used, the performance of query engine gets more attention. In the actual complicated application scenarios, query engine may receive intensive query requests with similar structure in a short time, as usual these queries will be evaluated independently. Multi-query optimization evaluation approach can mine feasible common query patterns deeply, choose preferable combination of common query patterns according to the cost model, and reduce the total time consumption by taking advantage of the common query pattern evaluation results. The experiments on LUBM dataset indicate that the total evaluation time of multi-query optimization evaluation approach is shorter than sequential evaluation approach and making the throughput of query engine improve.