Network Support Data Analysis for Fault Identification Using Machine Learning
Machine learning has gained immense popularity in a variety of fields as it has the ability to change the conventional workflow of a process. The abundance of data available serves as the motivation for this. This data can be exploited for a good deal of knowledge. In this article, we focus on opera...
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| Veröffentlicht in: | International journal of software innovation 2019-04, Vol.7 (2), p.41-49 |
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| container_title | International journal of software innovation |
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| creator | Basheer, Shakila Gandhi, Usha Devi Priyan M.K Parthasarathy P |
| description | Machine learning has gained immense popularity in a variety of fields as it has the ability to change the conventional workflow of a process. The abundance of data available serves as the motivation for this. This data can be exploited for a good deal of knowledge. In this article, we focus on operational data of networking devices that are deployed in different locations. This data can be used to predict faults in the devices. Usually, after the deployment of networking devices in customer site, troubleshooting these devices is difficult. Operational data of these devices is needed for this process. Manually analysing the machined produced operational data is tedious and complex due to enormity of data. Using machine learning techniques will be of greater help here as this will help automate the troubleshooting process, avoid human errors and save time for the technical solutions engineers. |
| doi_str_mv | 10.4018/IJSI.2019040104 |
| format | Article |
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| ispartof | International journal of software innovation, 2019-04, Vol.7 (2), p.41-49 |
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| language | eng |
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| subjects | Data analysis Human error Machine learning Troubleshooting Workflow |
| title | Network Support Data Analysis for Fault Identification Using Machine Learning |
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