バージョン 32 から バージョン 33 における更新: survey

更新日時:

2012/07/04 10:27:09 (12 年 前)

更新者:

wu

コメント:

--

survey

@@ -20 +20 @@
 We present an evaluation of native triple stores on biological data. Compared with the data in other areas biological data is typically huge. Therefore the performance of bulk loading and querying are essential to decide whether a triple store can be applied into the biological field. Here we test five native triple stores Virtuoso, OwlimSE, Mulgara, 4store, and Bigdata with five biological dataset, which ranging from tens of millions to eight billions. We present their load times and query cost. 
 
-For each database we provide several results by adjusting their parameters, which could influence the performance importantly. However  These parameters could perform differently with different hardware and software platforms, and even with different data set. It is difficult to test all the performance by adjusting and combining all the parameters for every data set because the importing of our data set, such as uniprot and DDBJ, takes over two days at least. Therefore we do not guarantee what we provide is the best performance of each database although we try to find out the best performance for each triple store.
+For each database we provide several results by adjusting their parameters, which could influence the performance importantly. However  These parameters could perform differently with different hardware and software platforms, and even with different data set. It is difficult to test all the performance by adjusting and combining all the parameters for every data set because the importing of our data set, such as uniprot and DDBJ, takes over two days or more. Therefore we do not guarantee what we provide is the best performance of each database although we try to find out the best performance for each triple store.
 
 '''4store'''