Concurrent Processing of Scientific Articles using Cohesion Network Analysis

Execution of complex tasks can be optimized by using parallel computing integrated within specialized frameworks. Thus, the AKKA framework was employed to speed up the processing time of the HUB-TECH platform to recommend semantically relevant articles, starting from a project description. Both parallel and distributed approaches are presented and a speed-up analysis between the two is performed. Distributed architectures enhance the system's performance by computing an increased number of semantic distances in less time. This usually requires more physical resources than parallel processing which is also capable of efficiently exploiting single nodes. The hardware specifications of the machine are relevant when trying to determine the optimal number of actors that can run on a machine in order to produce the maximal throughput. Our results show a 22% decrease in execution time while comparing serial and parallel approaches, a drastic 86% decrease when distributed methods are employed in contrast to serial executions, as well as an 82% improvement when comparing distributed and parallel experiments.