In this paper,a highly parallel batch processing engine is designed for SPARQL queries.Machine learning algorithms were applied to make time predictions of queries and reasonably group them,and further make reasonable...In this paper,a highly parallel batch processing engine is designed for SPARQL queries.Machine learning algorithms were applied to make time predictions of queries and reasonably group them,and further make reasonable estimates of the memory footprint of the queries to arrange the order of each group of queries.Finally,the query is processed in parallel by introducing pthreads.Based on the above three points,a spall time prediction algorithm was proposed,including data processing,to better deal with batch SPARQL queries,and the introduction of pthread can make our query processing faster.Since data processing was added to query time prediction,the method can be implemented in any set of data-queries.Experiments show that the engine can optimize time and maximize the use of memory when processing batch SPARQL queries.展开更多
文摘In this paper,a highly parallel batch processing engine is designed for SPARQL queries.Machine learning algorithms were applied to make time predictions of queries and reasonably group them,and further make reasonable estimates of the memory footprint of the queries to arrange the order of each group of queries.Finally,the query is processed in parallel by introducing pthreads.Based on the above three points,a spall time prediction algorithm was proposed,including data processing,to better deal with batch SPARQL queries,and the introduction of pthread can make our query processing faster.Since data processing was added to query time prediction,the method can be implemented in any set of data-queries.Experiments show that the engine can optimize time and maximize the use of memory when processing batch SPARQL queries.
