Elastic Knowledge-based System for Data Analytics on Cloud Computing Platform

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Ekasit Kijsipongse
Suriya U-ruekolan
Namfon Assawamekin


Knowledge-based system (KBS) is a methodology to carry out an analysis on data having complex interrelationships as it can derive implicit information and knowledge from explicit facts through logical inference process. However, due to the growing number and size of data, the data analysis process spends longer time. This research presents a framework to analyze big data with KBS on cloud computing. Our work aims to shorten the time to analyze big data by distributing KBS processes to multiple cloud computing resources elastically. The system can auto-scale to increase or decrease computing resources to match the workload at each moment. The proposed system is tested by comparing the execution time when using and not using auto-scaling to analyze equipment failure dataset. The results show that the analysis of the dataset is completed faster. The execution time is close to that of 9 virtual machines while the cost is similar to that of 2 virtual machines, achieving the best price-performance ratio.

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