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.

中文翻译：

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电信网络中基于影响的根本原因警报发现方法

警报根本原因分析是日常电信网络维护中的重要组成部分，对于高效，准确地进行故障定位和故障恢复至关重要。在实践中，由于网络的复杂性和大量的警报，准确和可自行调整的警报根本原因分析是一个巨大的挑战。一种常见的故障根本原因识别方法是通常基于事件共现或条件独立性测试来构造具有近似边缘的图形。但是，边缘修剪通常需要相当多的专家知识。我们提出了一种用于根源警报定位的新型数据驱动框架，结合了因果推理和网络嵌入技术。在此框架中，我们设计了一种混合因果图学习方法（HPCI），结合了Hawkes流程和条件独立性测试，并提出了一种新颖的基于因果传播的嵌入算法（CPBE）来推断边缘权重。随后，我们通过在加权图上应用影响力最大化算法，在实时数据流中发现根本原因警报。我们在人工数据和现实世界的电信数据上评估了我们的方法，显示出相对于最佳基准而言的显着改进。