| 38 | | We use five real biological SPARQL endpoints,and designed five basic queries,considering the number of really queried endpoints, the triple patterns (varying from 4 to 9), and the number of results(from 5 to 11000). And we rewrite query 3 and 5 with “limit 100” clause. To keep a stable server and network environment, we sequentially execute a query for all engines, and repeat it five times. Finally we remove the biggest value and calculate the average of other four values. To test the performance when users do federated 1.1 queries in an endpoint directly instead of using a federated query engine, we rewrite all five queries with service keywords and change the order of two service clauses, and execute the query in one of five endpoints. |
| | 38 | We use five real biological SPARQL endpoints,and designed five basic queries,considering the number of really queried endpoints, the triple patterns (varying from 4 to 9), and the number of results(from 5 to 11000). And we rewrite query 3 and 5 with “limit 100” clause. |
| | 39 | |
| | 40 | To keep a stable server and network environment, we sequentially execute a query for all engines, and repeat it five times. Finally we remove the biggest value and calculate the average of other four values. |
| | 41 | |
| | 42 | To test the performance when users do federated 1.1 queries in an endpoint directly instead of using a federated query engine, we rewrite all five queries with service keywords and change the order of two service clauses, and execute the query in one of five endpoints. |
