Sentence examples for mapping time of from inspiring English sources

In the final three instances, Alg Sign achieved better performance than ApproxMap 5/25%, with decreased mapping time of between 0.12 and 0.65.

When considering the ratio between the time spent by these algorithms, the mapping time of A l g Hung was up to 283 times greater than that of A p p r o x M a p 5/25%.

Finally, compared with ApproxMap 5/12%, the mapping time of ApproxMap 1/10% showed a maximum increase of 0.52, while that of ApproxMap 5/25% decreased by 0.16. Figure 21 Results of mapping the synthetic datasets.

Besides, we also verified a time increase for the ApproxMap tests; the mapping time of A p p r o x M a p 1/10% increased by between 0.42 and 0.60, while that of A p p r o x M a p 5/25% decreased by 0.12, compared with A p p r o x M a p 5/12%. Figure 18 Execution for breadth-first/load-balancing crawled datasets.

Finally, considering the differences between steps η 1 and η 2, compared with A p p r o x M a p 5/12%, the mapping time for A p p r o x M a p 1/10% increased by between 0.38 and 0.60, while A p p r o x M a p 5/25% showed a maximum reduction in mapping time of 0.06.

Figure 21 shows the results for the mapping times of the algorithms on a logarithmic scale.

Moreover, Figure 26 shows the results for the mapping times of the algorithms.

The reconfiguration time of a single node is negligible compared to the download and mapping times of a Pervasive App.

The adopted values in this experiment were chosen empirically with the aim of reducing the mapping times of the algorithms.

We used a logarithmic scale for charts showing mapping times of the algorithms, thereby providing better visualization and comparison of the results.

Figures 23 and 24 show the results obtained for these datasets, in terms of delta sizes and mapping times of the algorithms, respectively.