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However, this centralized paradigm hinders the scalability of the data distribution process.
It introduces more than 20 policies to define the QoS of the data distribution process.
In the original GPSR algorithm, the data distribution process can be described as follows: 1. Greedy mode.
Firstly, the data distribution process should be decoupled between senders and receivers because the computing environment is highly dynamic.
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Since Self-Organizing Maps (SOM) provide compact representation of the data distribution, efficient process monitoring can be performed in the two-dimensional projection of the process variables.
Moreover, considering both global and local structures of data distribution makes our feature selection process more effective.
Data distribution is used to supply processed data to the target object.
The normalization process, which tests data distribution against a model (Figure 1), is useful for identifying the likely range of data.
By adopting this model-driven development process, we have automated the process from data modeling to data distribution.
By adopting this model-driven development process, we have automated the process from business modeling to data distribution and reduced the time taken to implement a new product.
One may think of d2o as a layer of abstraction that is added to numpy arrays in order to take care of data distribution and collection among multiple MPI processes.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com