Operating System Process Modeling: An Implementation of Association Learning Algorithms using Router Kernel Simulated Data

摘要

Large chunk of dispersed data exists in several databases and data marts, these amount of data if not properly gathered and analyzed will lead to total loss of useful knowledge. With the existence of the problem of an efficient scheduling and resource management techniques in Operating System, there is a dire need to provide a rulebased scheme to help optimize and maintain the operating system process modeling in a very efficient manner. To help improve on this issue, data mining techniques such as data extraction, cleaning and association rules have been used, Hence, this paper aims at investigating two of the most efficient learning association algorithms, FPGrowth and Apriori algorithms with the objective of helping understand the process of association learning in a network environment using router kernel data.. This is implemented using Rapid Miner tool to model the kernel data and further comparison of the two methods.