Machine Learning Process for the Prediction of the IT Asset Fault Recovery


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 4, pp. 281-290, Apr. 2013
10.3745/KTSDE.2013.2.4.281,   PDF Download:

Abstract

The IT asset is a core part that supports the management objective of an organization, and the last settlement of the ii asset Fault is very important. In this study, a fault recovery prediction technique is proposed, which uses the existing fault data to address the IT asset fault. The proposed fault recovery prediction technique is as follows. First, the existing fault recovery data were pre-processed and classified by fault recovery type: second, a rule was established for the keyword mapping of the classified fault recovery types and reported data; and third, a machine learning process that allows the prediction of the fault recovery method based on the established rule was presented. To verify the effectiveness of the proposed machine learning process, company A`s 33.000 computer fault data for the duration of six months were tested. The hit rate for fault recovery prediction was approximately 72%, and it increased to 81% via continuous machine learning.


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Cite this article
[IEEE Style]
Y. J. Moon, S. Y. Rhew, I. W. Chol, "Machine Learning Process for the Prediction of the IT Asset Fault Recovery," KIPS Transactions on Software and Data Engineering, vol. 2, no. 4, pp. 281-290, 2013. DOI: 10.3745/KTSDE.2013.2.4.281.

[ACM Style]
Young Joon Moon, Sung Yul Rhew, and Il Woo Chol. 2013. Machine Learning Process for the Prediction of the IT Asset Fault Recovery. KIPS Transactions on Software and Data Engineering, 2, 4, (2013), 281-290. DOI: 10.3745/KTSDE.2013.2.4.281.