计算机应用与软件2024，Vol.41Issue(7)：34-41,8.DOI:10.3969/j.issn.1000-386x.2024.07.006
基于表示学习的告警数据流压缩算法
ALERT STREAM COMPRESSION METHOD BASED ON REPRESENTATION LEARNING
阴振生 1陈佳 1王鹏 1汪卫1
作者信息
- 1. 复旦大学计算机科学技术学院 上海 200438
- 折叠
摘要
Abstract
The large-scale online service system has a large number of alerts,and the correlations of which are rather complicated,which greatly increases the difficulty of fault diagnosis for operators.To solve this problem,we propose an alert stream compression method based on representation learning.The method included two stages:offline learning stage and online compression stage.In the offline learning stage,the semantic information of the original alert data and the topology information between components were learned and represented through embedding technologies.In the online compression stage,the streaming clustering method was used to associate the alert vectors by representation learning in real-time.Experiments on the synthetic dataset and the real dataset show that the method can meet the real-time and effectiveness requirements of the alert stream compression.关键词
在线服务系统/告警数据流压缩/表示学习/词嵌入/图嵌入/流式聚类Key words
Online service system/Alert stream compression/Representation learning/Word embedding/Graph embedding/Streaming clustering分类
信息技术与安全科学引用本文复制引用
阴振生,陈佳,王鹏,汪卫..基于表示学习的告警数据流压缩算法[J].计算机应用与软件,2024,41(7):34-41,8.
