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- W4386590500 abstract "Telecommunication Networks (TNs) have become the most important infrastructure for data communications over the last century. Operations and Maintenance (O&M) is extremely important to ensure the availability, effectiveness, and efficiency of TN communications. Different from the popular O&M technique for IT systems (e.g., the cloud), Artificial Intelligence for IT Operations (AIOps), O&M for TNs meets the following three fundamental challenges: topological dependence of network components, highly heterogeneous software, and restricted failure data. This article presents TelOps, the first AI-driven O&M framework for TNs, systematically enhanced with mechanism, data, and empirical knowledge. We provide a comprehensive comparison between TelOps and AIOps, and conduct a proof-of-concept case study on a typical O&M task (failure diagnosis) for a real industrial TN. As the first systematic AI-driven O&M framework for TNs, TelOps opens a new door to applying AI techniques to TN automation." @default.
- W4386590500 created "2023-09-11" @default.
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- W4386590500 date "2023-01-01" @default.
- W4386590500 modified "2023-09-26" @default.
- W4386590500 title "TelOps: AI-driven Operations and Maintenance for Telecommunication Networks" @default.
- W4386590500 doi "https://doi.org/10.1109/mcom.003.2300055" @default.
- W4386590500 hasPublicationYear "2023" @default.
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