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2021 | OriginalPaper | Buchkapitel

An Influence-Based Approach for Root Cause Alarm Discovery in Telecom Networks

verfasst von : Keli Zhang, Marcus Kalander, Min Zhou, Xi Zhang, Junjian Ye

Erschienen in: Service-Oriented Computing – ICSOC 2020 Workshops

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
An Influence-Based Approach for Root Cause Alarm Discovery in Telecom Networks
verfasst von
Keli Zhang
Marcus Kalander
Min Zhou
Xi Zhang
Junjian Ye
Copyright-Jahr
2021
DOI
https://doi.org/10.1007/978-3-030-76352-7_16