Although current algorithms are useful for fairly large datasets, scaling problems are found when the number of instances is in the hundreds of thousands or millions.
It is evident through the results that the retrieval accuracy of the proposed methods as the datasets scale-up not only remains sufficiently high in absolute numbers but, more importantly, also significantly outperforms the scalability of the original methods, validating the overall robustness and reliability of the scheme.
For small datasets with only a few dozen species, the parsimony code scales well only to about 8 16 processors while the distance code on these same small datasets scales well to about 32 64 processors.
Regression analyses showed a significant positive relationship between NLCP and reductions in the number of gene lineages in simulated datasets (scaled NLOST; Figure 4 solid circles).
Complete results are available in additional file 1 and 2. In all datasets scaling effects between microarrays and intensity dependent bias within microarrays are present, visible from the microarray data distributions.
The dataset scale and eigenvector dimension are as shown in Table 4.
Figure 6 shows how execution times for the mouse brain dataset scale with different numbers of compute nodes when using the ppn2-t6 configuration.
The encoded training dataset was scaled between 0 and 1 and using the parameters used for training dataset scaling, the test set was also scaled, as it was strongly suggested for kernel-based SVM learning.
Based on the moderately positive correlation between estimated parental breeding values from the mean and variance models for untransformed data (DHGLM2) of the current dataset, scaling effects may explain some of the genetic heterogeneity of within-family variance.
However, it can be improved by more tailored indexing, query caching, and possibly by alternative tuning of MySQL configuration properties, and this strategies will be particularly useful if the dataset scales up.
As a result, insurance companies began to independently use satellite datasets to scale up their services.
