Comparative Studies on Intelligent Swarming Network (iSWAN) Geno-Generative Algorithm and Top-K Query Processing Algorithm

Abstract

This paper proposed an enhanced Top-k query processing in a real time distributed database system. The system employs a Particle Swarm Optimizer (PSO) based Geno-Generative iSWAN Model technique that enhances and allows multi-task concurrent query processing in a real time co-simulation data acquisition platform and as part of refinement to an existing Top-k query processing Technique. In this paper, the proposed system is compared for efficiency with the Top-K Query Algorithm, which is emerging as an alternative to more conventional technique for real time query processing in distributed databases. Dynamic simulations were performed with a real time small testbed comprising of physical and non-physical devices to test and evaluate the performance and efficiency of the two systems. Considering the estimated and expected temperatures, the result of simulation study proves that the Intelligent Swarming Network (iSWAN) Geno-Generative Model is more preferred over Top-K Query Algorithm due to its 70% accuracy over the Top-K Model, which reported a lower accuracy level of 40%.