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Continual increases of multifaceted IoT devices in healthcare is beset by challenges, such as powering IoT terminal nodes used for health monitoring, data processing, smart decisions, and event management.
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Results also prove that the method has a significant theoretical and practical value for big data processing of smart substation.
The main subject of this paper is to propose an architecture for data processing in Smart Cities.
Data processing for Smart Cities become more challenging, facing with different handling steps: data collection from different heterogeneous sources, processing sometimes in real-time and then delivered to high level services or applications used in Smart Cities.
The incessant increase of the multifaceted IoT devices in health is broadly tested by challenges such as powering the IoT terminal nodes used for health monitoring, real-time data processing and smart decision and event management.
3) Parallel compression experiments verify that with the data records increase in a certain range, the cluster has a better parallel processing efficiency, and S′(m,n)(p) and C (m,n)(p) of cloud cluster further prove that big data processing of smart substation based on Hive is feasible. .
For example, in [17], the authors analyze the performance of different data processing architectures in smart grids.
The data processing flow of smart substation based on Hive can be logically divided into data source layer, computing layer, control layer and application layer.
Four logical layers of data processing flow in the smart substation based on Hive are shown in Fig. 6.
The paper describes the implementation of a novel smart data processing hardware system having medical diagnostic capabilities.
The cloud enables parallel computing that is essential for the big data processing, machine learning and smart algorithms that enable 'data-driven' business.
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