Property:Has Results

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Showing 20 pages using this property.
A
Using stability constraints improves alignment quality with both learned and manually set weights.  +
Because ARQ is using a pipelining concept the response time is very good, even when data has to be retrieved from a remote data source.  +
We observe that SHJ and ANAPSID operators are able to produce the first tuple faster than ARQ or Hash join, even in an ideal scenario with no delays; further, ARQ performance is clearly aff_ected by data transfer distribution and its execution time can be almost two orders of magnitude greater than the time of SHJ or ANAPSID. We notice that SHJ and ANAPSID are competitive, this is because the number of intermediate results is very small, and the benefits of the RJTs cannot be exploited. This suggests that the performance of ANAPSID operators depends on the selectivity of the join operator and the data transfer delays.  +
Experiments have shown that our quality measure is usually effective in defining weights for the LWC matcher.  +
Avalanche is able to successfully execute query plans and retrieves many up-to-date results without having any prior knowledge of the data distribution. We, furthermore, see that different objective functions have a significant influence on the outcome and should play a critical role when deployed on the Semantic Web.  +
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With our optimization techniques, we are able to reduce the number of requests significantly, e.g., from 170,579 (DARQ) and 93,248 (AliBaba) to just 23 (FedX) for query CD3.  +
No data available now.  +
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LIMES outperforms SILK in all experimental settings. It is important to notice that the difference in performance grows with the (product of the) size of the source and target knowledge bases.  +