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Usually, the local JVM may fail to utilize the large size of data in memory, as a reason of the limited Java Heap memory.
The large size of data sets generated using hyperspectral imaging techniques significantly increases both the capability and difficulty of designing detection and classification systems.
To overcome such inefficiencies, some techniques split the large size of data randomly into small chunks of data blocks, so they can fit into the memory for carrying out the required computations [6].
It is possible that population sub-groups with limited access to technology may be systemically underrepresented in data thus the need of sound statistical analysis is not obviated due to the large size of data.
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However, the large size of NGS data causes a significant data transfer latency from the client's site to the cloud, which presents a bottleneck for using cloud computing services.
The major strength in using data from registries is the large size of collected data and the population-based information free from recall bias.
Organisation of data was assisted by use of the N-Vivo software given the large size of the data set.
Bootstrapping calculations were performed with 1000 replicates and a branch and bound search for the Streptomyces 16S rDNA data set, for which a heuristic search with 100 replicates was used due to the large size of the data set (40+ taxa, 1300 bp each).
The resulting model is still complex, which combined with the large size of the data matrix, make the resulting analyses very computationally demanding.
Since none of the competing algorithms used in previous sections can complete this task due to the large size of the data, we only report the results from the LRW algorithm.
Because of the large size of the data set − almost 70,000 male second-generation immigrants, which is a total survey rather than a sample − we are able to divide the data into 20 sub-groups with respect to geographical origin, which allows for a separate analysis.
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