Application Of Complex Event Processing For Broadband Network Fault Prediction Using Random Forest
The customer satisfaction of the clients of broadband network mostly depend on robustness of the service offered by Internet Service Providers (ISP)s. Providing uninterrupted network service is essential in this communication era even though interruption in internet connection is unavoidable. Howeve...
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my-mmu-ep.77312020-09-18T05:19:36Z Application Of Complex Event Processing For Broadband Network Fault Prediction Using Random Forest 2018-11 Khan, Chy. Mohammed Tawsif QA75.5-76.95 Electronic computers. Computer science The customer satisfaction of the clients of broadband network mostly depend on robustness of the service offered by Internet Service Providers (ISP)s. Providing uninterrupted network service is essential in this communication era even though interruption in internet connection is unavoidable. However, if it is predicted earlier, the consequences can be minimised. So, it is very essential to accurately forecast the faults in internet connection for telecommunication companies. However, currently the companies do not have real-time fault prediction system to solve the issue. This system may help to reduce future operational expenses to some extend and it may enable to perform preventive actions as far as possible. Hence, the objective of this thesis is to develop a suitable tool to predict the network fault. The proposed tool is a combination of Complex Event Processing (CEP) and Predictive Analytics (PA). The PA is used to predict network fault using Random Forest Classifier and CEP is used to perform the prediction in real-time on streaming events. 2018-11 Thesis http://shdl.mmu.edu.my/7731/ http://library.mmu.edu.my/library2/diglib/mmuetd/ masters Multimedia University Faculty of Engineering & Technology |
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QA75.5-76.95 Electronic computers Computer science |
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QA75.5-76.95 Electronic computers Computer science Khan, Chy. Mohammed Tawsif Application Of Complex Event Processing For Broadband Network Fault Prediction Using Random Forest |
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The customer satisfaction of the clients of broadband network mostly depend on robustness of the service offered by Internet Service Providers (ISP)s. Providing uninterrupted network service is essential in this communication era even though interruption in internet connection is unavoidable. However, if it is predicted earlier, the consequences can be minimised. So, it is very essential to accurately forecast the faults in internet connection for telecommunication companies. However, currently the companies do not have real-time fault prediction system to solve the issue. This system may help to reduce future operational expenses to some extend and it may enable to perform preventive actions as far as possible. Hence, the objective of this thesis is to develop a suitable tool to predict the network fault. The proposed tool is a combination of Complex Event Processing (CEP) and Predictive Analytics (PA). The PA is used to predict network fault using Random Forest Classifier and CEP is used to perform the prediction in real-time on streaming events. |
format |
Thesis |
qualification_level |
Master's degree |
author |
Khan, Chy. Mohammed Tawsif |
author_facet |
Khan, Chy. Mohammed Tawsif |
author_sort |
Khan, Chy. Mohammed Tawsif |
title |
Application Of Complex Event Processing For Broadband Network Fault Prediction Using Random Forest |
title_short |
Application Of Complex Event Processing For Broadband Network Fault Prediction Using Random Forest |
title_full |
Application Of Complex Event Processing For Broadband Network Fault Prediction Using Random Forest |
title_fullStr |
Application Of Complex Event Processing For Broadband Network Fault Prediction Using Random Forest |
title_full_unstemmed |
Application Of Complex Event Processing For Broadband Network Fault Prediction Using Random Forest |
title_sort |
application of complex event processing for broadband network fault prediction using random forest |
granting_institution |
Multimedia University |
granting_department |
Faculty of Engineering & Technology |
publishDate |
2018 |
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1747829668944281600 |