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• W3174938923 abstract "Alarm root cause analysis is a significant component in the day-to-day telecommunication network maintenance, and it is critical for efficient and accurate fault localization and failure recovery. In practice, accurate and self-adjustable alarm root cause analysis is a great challenge due to network complexity and vast amounts of alarms. A popular approach for failure root cause identification is to construct a graph with approximate edges, commonly based on either event co-occurrences or conditional independence tests. However, considerable expert knowledge is typically required for edge pruning. We propose a novel data-driven framework for root cause alarm localization, combining both causal inference and network embedding techniques. In this framework, we design a hybrid causal graph learning method (HPCI), which combines Hawkes Process with Conditional Independence tests, as well as propose a novel Causal Propagation-Based Embedding algorithm (CPBE) to infer edge weights. We subsequently discover root cause alarms in a real-time data stream by applying an influence maximization algorithm on the weighted graph. We evaluate our method on artificial data and real-world telecom data, showing a significant improvement over the best baselines." @default.
• W3174938923 created "2021-07-05" @default.
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• W3174938923 date "2021-01-01" @default.
• W3174938923 modified "2023-10-12" @default.
• W3174938923 title "An Influence-Based Approach for Root Cause Alarm Discovery in Telecom Networks" @default.
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• W3174938923 doi "https://doi.org/10.1007/978-3-030-76352-7_16" @default.
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