Practical data monitoring in the internet-services domain
원문 초록
Large-scale monitoring, anomaly detection, and root cause analysis of metrics are essential requirements of the internet-services industry. To address the need to continuously monitor millions of metrics, many anomaly detection approaches are being used on a daily basis by large internet-based companies. However, in spite of the significant progress made to accurately and efficiently detect anomalies in metrics, the sheer scale of the number of metrics has meant there are still a large number of false alarms that need to be investigated. This paper presents a framework for reliable large-scale anomaly detection. It is significantly more accurate than existing approaches and allows for easy interpretation of models, thus enabling practical data monitoring in the internet-services domain.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defects
Evaluating Language Models For Threat Detection in IoT Security Logs
mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series
E-ABIN: an Explainable module for Anomaly detection in BIological Networks
Exact Matrix Seriation through Mathematical Optimization: Stress and Effectiveness-Based Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.