Abstract
IT storage enterprise infrastructure management and support is becoming more and more complicated. Engineers have to face everyday with technical challenges to ensure the availability and performance of the data for users and virtual instances. In addition, storage requirements are different for every single business unit and storage support teams have to deal with multivendor storage systems. Certain storage support group used to receive on their ticket queue numerous restore tasks from end users which wrongly deleted important files or folders. The high repetitiveness of restore tasks can be dangerous for the storage engineer because several restores involves larges files and folders with similar names (business naming convention), and the tediousness may lead the engineer to lower the focus and increase human error. An intelligent automation based on machine learning, capable to analyze text and perform repetitive large time consuming restore tasks has been developed to alleviate the workload of the storage support group.
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Esquivel-García, S., Hernández-Uribe, Ó. (2020). File Restore Automation with Machine Learning. In: Mata-Rivera, M.F., Zagal-Flores, R., Barria-Huidobro, C. (eds) Telematics and Computing. WITCOM 2020. Communications in Computer and Information Science, vol 1280. Springer, Cham. https://doi.org/10.1007/978-3-030-62554-2_5
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