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Scalable data warehouses, as a desired solution to some of the problems introduced in the previous chapter, have specific architectural dimensions that are explained in this chapter, including workload, data complexity, query complexity, availability and data latency.
The next four sections return to topics introduced in the previous chapter, on architectural structures in the ISA design.
The chapter focuses on dynamical Monte Carlo (MC) methods, which are widely used in physics and engineering, and which were introduced in the previous chapter.
In a first stage of the project, former eligibility procedures were disaggregated conceptually using the ICF-CY extended model as introduced in the last chapter.
Comparing the satellite data with the Millstone Hill model we find similar discrepancies at 850 km as were noted for daytime in the previous chapter.
As mentioned in the previous chapter, data parallelism abounds in SDR applications.
The statistics of the fiber orientation in the non-woven given as image processing data (IPD) complement the FIDYST informations as described in the previous chapter.
As noted in the previous chapter, chemical development is a very different "phase" of the development of a new medicine.
BMCs were seeded on scaffolds as described in the previous chapter.
As stated in the previous chapter, HPA-axis regulating cochaperone FKBP5 emerged as putative PTSD susceptibility marker.
In addition, such data might also be used directly in predictive modelling providing parameters for the model as suggested in the previous chapter.
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