| 19 | | 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. We test five native triple stores Virtuoso, OwlimSE, Mulgara, 4store, and Bigdata. We select five real biological data set instead synthetic ones, which ranging from tens of millions to eight billions. We present their load times and query cost. We do not test the inference ability this time. |
| | 19 | 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. Our target is to verify whether the current triple stores are efficient to deal with the tremendous biological data. We test five native triple stores Virtuoso, OwlimSE, Mulgara, 4store, and Bigdata. We select five real biological data set instead of synthetic ones, which ranging from tens of millions to eight billions. We present their load times and query cost. We do not test the inference ability this time. |
