DocumentCode :
3304208
Title :
Machine Learning Methodology for Enhancing Automated Process in IT Incident Management
Author :
Li, Haochen ; Zhan, Zhiqiang
Author_Institution :
State Key Lab. of Networking & Switching, Beijing Univ. of Posts & Telecommun., Beijing, China
fYear :
2012
fDate :
23-25 Aug. 2012
Firstpage :
191
Lastpage :
194
Abstract :
Operating system experienced a rise in number of incidents in recent years. Analysis and reemployment of past solution therefore may make a contribution in reducing service interrupt time and minimizing business losses. The training and retaining of human resources is another primary disbursement source for enterprise. Thus, it is of great significance for enterprises to find reasonable solutions automatically. Combined with keyword tokenization, data mining, numerical optimization and neural network, this paper presents a system that compares and finds the most similar incident solution in the past, based on the description provided by customers in natural language. We try to improve the automated process by increasing the efficiency and accuracy through machine learning methodology and also devote to presenting a practical decision support method.
Keywords :
business data processing; data mining; human resource management; learning (artificial intelligence); natural language processing; neural nets; IT incident management; automated process; business loss; data mining; decision support method; enterprise; human resource retaining; human resource training; keyword tokenization; machine learning methodology; natural language; neural network; numerical optimization; operating system; service interrupt time; Accuracy; Biological neural networks; Computer architecture; Machine learning; Optimization; Training; IT incident management; data mining; neural network; numerical optimization; tokenize;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Network Computing and Applications (NCA), 2012 11th IEEE International Symposium on
Conference_Location :
Cambridge, MA
Print_ISBN :
978-1-4673-2214-0
Type :
conf
DOI :
10.1109/NCA.2012.28
Filename :
6299094
Link To Document :
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