We performed several experiments on road accident data to measure the speed up and scale up of implementations of our proposed approach.
Recently, due to the scale up in high throughput data, availability of integrated OMICS data, and various advanced statistical analysis methods, many novel systems biology approaches have been employed to reveal the deeper underlying systematic mechanisms of various cancers [ 4- 6].
The need for scale-up of laboratory milling data to full operation is critical especially for the proper selection of design parameters.
Developing a set of MOH guidelines and policies to address these types of issues will be critical to further scale-up of any new data collection system.
Therefore, they can be easily scaled up to big data consisting of aspects from a variety of domains.
Important for NGS data analysis are specifications for the usage of computational resources (RAM, number of CPUs, processing time, space requirements) and how those scale up with the size of the data set (=number and size of data sets) or type of NGS data (genomic, RNAseq, ChIPseq, Bisulfite-Seq) - all of which are not mentioned.
Data Science need not be always for big data; however, the fact that data are scaling up makes big data an important aspect of data science [3].
In particular, there is a concern that the current tools and approaches no longer scale up to the present volume of data, thus resulting in a bottleneck in the synthesis of knowledge from data (1).
For the design and scale-up of gas liquid ejectors, reliable data are required which describe the mass transfer characteristics as a function of the physical fluid properties, geometrical design and the process related parameters.
It is important to remember that many sites were still in the process of scaling up when data were obtained and the large-scale plans have not been fully implemented.
