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Figure 2(a) describes the black box process of the D2K migration strategy.
The D2K strategy is a power-efficient KDS designed to maximize the DPU(E) and minimize the DCU(E).
We consider that our D2K algorithm is more applicable if it is capable of producing actionable stimulates at the appropriate time irrespective of the data loads and congestion level.
Layer-1 comprises an industrial operational platform to implement a D2K migration strategy at the CH.
Therefore, the D2K algorithm can be efficiently embedded into the circuitry of the CH for real-time operations.
We also proposed a D2K algorithm that runs at an individual CH to filter useful knowledge that is disseminated to actuators and knowledge users to perform timely intelligent actions to protect the chemical plant from highly unexpected mishaps.
Therefore, our proposed D2K algorithm claims to preserve 97% of the transmission energy, and 3% of the packed knowledge can be well perceived by the actuators to perform the safety-critical actions required to resolve threats in the chemical industry.
Our D2K algorithm can directly use the KDS-FIS approach to accelerate the decision-making process at the CH to regulate industrial safety-critical events.
"Our D2K tools offer many ways for scientists to analyze and understand complex relationships and develop better predictive models.
D2K Inc, the San Jose, California-based software house best known for its Tapestry data transformation and migration tool, has a new CEO.
BMC Software Inc is working with D2K Inc on a bundle designed to ease the delivery to a data warehouse of data stored on an MVS mainframe.
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