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Map stage: computation of intermediate paths 3.
Reduce stage: concatenation of intermediate paths 4.
Next, Dijkstra's program [16] is running on each machine to generate a set of intermediate paths.
In the reduce stage, the set of intermediate paths from the map stage are concatenated based on the path key.
The set of intermediate paths are merged to build the full path Fig. 10 Output stage: writing of the full paths.
WR, as well as the approach proposed by Agrawal and Ramakrishnan [36], only considers the maximal path as input for the data mining algorithm, since it avoids repetitive counting of intermediate paths in the same workflow.
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Secondly, the computation of the intermediate paths is carried out in parallel way across the cluster node, as shown in Fig. 8.
The map stage consists of computing the intermediate paths on each subgraph in parallel way by using the A*_Mapper procedure (mapper).
The intermediate paths are computed across the five slave nodes Fig. 9 Reduce stage: concatenation of partial paths.
The first uses intermediate path points and the second exploits the periodicity of the trigonometric functions involved.
Step 8: In case of intermediate node along this path, route table entry corresponding to destination is created or updated consequently.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com