Clustering Method Of Distributed Technologies In Data Flow Management
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Grouping data into groups based on certain rules is known to increase data flow, not only to extract meaning from data, but also to increase the efficiency of large data processing. A uniform distribution of data is particularly effective in approaches such as distributed computing or parallel processing. The main reason for this is that dividing the data into as many equal clusters as possible allows for the highest performance results in these approaches. But the human-factor-based uniform distribution of data is a complex process due to the impossibility of pre-planning the data in the data stream and the size of the data. Therefore, in the study, the application of the clustering method of distributed technologies in data flow management was considered.
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