Development of a simulation result management and prediction system using machine learning techniques Online publication date: Mon, 11-Dec-2017
by Ki Yong Lee; Young-Kyoon Suh; Kum Won Cho
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 19, No. 1, 2017
Abstract: Simulations are widely used in various fields of computational science and engineering. As IT technology advances, the complexity and accuracy requirements of the simulations are increasingly rising up, accordingly escalating their execution cost as well. Nevertheless, it appears that the community has not yet paid much attention to the reuse of previously obtained simulation results to improve the performance of the execution of later requested simulations. In this regard, we propose a novel simulation service system that can utilise the results of previously executed simulations and thus improve the performance of later simulations. The proposed system can not only convert completed simulation results into a standard form and then store them into a NoSQL database for efficient retrieval, but also predict the result of a requested simulation using machine learning techniques without actual simulations. We demonstrate that the proposed system achieved very low error prediction rates only up to 7.4% from 0.9%.
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