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Content synchronization and differential updates delivery are performed in a separate subsystem from current LMS.
Differential operation is employed to obtain differential updates between the previous content and the current content.
It further improves availability and strengthens reliability and uses a simple application level check-pointing to find differential updates.
The algorithm has two stages: buffering of filtered network and generation of differential updates for the visualization (see Figure 1).
The differential updates are written in output in the form of a JSON file formatted according to the Gephi Streaming API [69].
At Master LMS, a subsystem called Master subsystem is responsible for determining the differential updates to be delivered to Slave LMS.
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Each differential update correspond to one visualization frame, i.e., one frame of an animation.
Each such differential update is meant to be visualized in the resulting animation of the network, e.g., as a frame of a movie.
In the second stage, for the purpose of visualization, the algorithm selects N v < N b nodes with the highest strength and creates a differential update to the visualized network consisting of these nodes and the connections between them.
In the second stage, for the purpose of visualization, the algorithm selects N v < N b nodes with the highest strength and all edges between these nodes with the highest strength and all edges between these nodes that have weight above a certain threshold w min. The subgraph induced by the N v nodes is compared with the subgraph in the previous state and a differential update is created.
However, the DCT Refresh architecture trails the two DPCM architectures in compression ratio at an equivalent PSNR since more data is needed to transmit an entire block rather than just a differential update.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com