一句话：提出OAS，用监督学习融合告警的语义与行为信息，在线判定告警关联并生成摘要。
在线服务系统中，组件间复杂关联导致一次故障触发大量告警，人工处理难以为继，自动摘要成为迫切问题。论文提出OAS（Online Alert Summarizing），一种基于监督学习的方法：分别从告警中学习语义信息和行为信息，再用特定深度学习模型聚合二者的表示，从而判定告警之间的关联，并能够对新上报的告警进行在线摘要。在两个大型商业银行的真实告警数据集上开展大量实验，验证了OAS的效率和有效性。
Alerts, which record details about system failures, are crucial data for monitoring a online service system. Due to the complex correlation between system components, a system failure usually triggers a large number of alerts, making the traditional manual handling of alerts insufficient. Thus, automatically summarizing alerts is a problem demanding prompt solution. This paper tackles this challenge through a novel approach based on supervised learning. The proposed approach, OAS (Online Alert Summarizing), first learns two types of information from alerts, semantic information and behavior information, respectively. Then, OAS adopts a specific deep learning model to aggregate semantic and behavior representations of alerts and thus determines the correlation between alerts. OAS is able to summarize the newly reported alert online. Extensive experiments, which are conducted on real alert datasets from two large commercial banks, demonstrate the efficiency and the effectiveness of OAS.
DOI 原文 ·
@inproceedings{paperbot6931,
title = {Online summarizing alerts through semantic and behavior information},
author = {Jia Chen and Peng Wang and Wei Wang},
booktitle = {ICSE},
year = {2022},
doi = {10.1145/3510003.3510055}
}