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

差分発生行の前後
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更新日時:
2012/07/12 15:57:54 (12 年 前)
更新者:
wu
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  • summarize

    v59 v60  
    106106'''Conclusion''' 
    107107 
    108 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).   
     108Our 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).   
    109109 
    110110When 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.