バージョン 59 から バージョン 60 における更新: summarize

更新日時:

2012/07/12 15:57:54 (12 年 前)

更新者:

wu

コメント:

--

summarize

@@ -106 +106 @@
 '''Conclusion'''
 
-Our evaluation shows that the importing cost of the data depends on the multiple factors: Server configuration(CPU,memory,harddisk and so on), the system property(vm.swappiness, JVM), the application configuration(cachememory,etc...), the data format, the size of data set and even data contents, e.g. DDBJ is nearly 2 times the triple size of Uniprot, but its importing cost is 2 times less than Uniprot(2 times longer expected if simply considering the proportional scaling).  
+Our evaluation shows that the importing cost of the data depends on the multiple factors: Server configuration(CPU,memory,harddisk and so on), the system property(vm.swappiness, JVM), the application configuration(cachememory,etc.), the data format, the size of data set and even data contents, e.g. DDBJ is nearly 2 times the triple size of Uniprot, but its importing cost is 2 times less than Uniprot(2 times longer expected if simply considering the proportional scaling).  
 
 When the number of triple size is less than 100M,  4Store can perform well both in loading data and query although providing only limited features. For data with moderate size such as varying from 100M to 500M or so,  Virtuoso and OwlimSE have similar or comparable performance. When increasing data to several billions, Virtuoso works best in the five test triple stores.