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Moreover, Figure 26 shows the results for the mapping times of the algorithms.
Figure 21 shows the results for the mapping times of the algorithms on a logarithmic scale.
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.
Compared with A p p r o x M a p 5/25%, the mapping times of A l g Hung increased by between 0.11 and 1.92.
Compared with A l g Sign, the increase in mapping times of A p p r o x M a p 5/25% varied between 0.46 and 1.59.
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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.
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.
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%.
In addition to the maps, times of peak ground motion at sources were interpolated from real seismological data.
Map phase execution time can be calculated by Eq. (5), where the average processing time of input data block in each wave multiplied by the corresponding number of wave is the map time of the ith data center.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com