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An efficient ERS and a standardised data processing model that integrates these techniques are essential to facilitate clinical research that utilises EMRs.
We designed a pragmatic data processing model optimised for patient identification and for the collection of necessary information from EMRs for clinical research.
Due to high-dimensional and dynamic oceanic data, we propose an intelligent integrated data processing model for the OWS.
Recently, Wu presented a HACE theorem that characterizes the features of the big data revolution, and proposed a 3-tiered big data processing model [7].
Further, Peddi et al. propose an intelligent cloud-based data processing model for mobile e-health multimedia applications [13].
We believe an efficient ERS and standardised data processing model are essential to facilitate clinical research that utilises EMRs.
Machine learning engineering happens in three stages — data processing, model building and deployment and monitoring.
However, it shows that such solutions require comprehensive approaches covering different aspects of data processing, model construction and the usage.
Statistical ensemble postprocessing always requires computer software for data processing, model fitting, forecast generation, and forecast verification.
The KNIME workflow package [27] has been utilised for data processing, model building and prediction as well as the framework for the development of the prototype methodology.
In this paper, all the data processing, model validation and implementation of the control algorithm are based on extensive measurements collected from an office building on the campus of the University of California, Merced.
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Justyna Jupowicz-Kozak
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