Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis
This work addresses labeling bottlenecks in IT operations for AI practitioners, but it is incremental as it builds on existing anomaly detection techniques without presenting new empirical results.
The paper tackles the challenge of limited labeled data in AIOps by introducing a taxonomy for log anomalies and exploring automated labeling methods, aiming to improve anomaly detection and enable future root cause analysis for more resilient IT systems.
The realm of AIOps is transforming IT landscapes with the power of AI and ML. Despite the challenge of limited labeled data, supervised models show promise, emphasizing the importance of leveraging labels for training, especially in deep learning contexts. This study enhances the field by introducing a taxonomy for log anomalies and exploring automated data labeling to mitigate labeling challenges. It goes further by investigating the potential of diverse anomaly detection techniques and their alignment with specific anomaly types. However, the exploration doesn't stop at anomaly detection. The study envisions a future where root cause analysis follows anomaly detection, unraveling the underlying triggers of anomalies. This uncharted territory holds immense potential for revolutionizing IT systems management. In essence, this paper enriches our understanding of anomaly detection, and automated labeling, and sets the stage for transformative root cause analysis. Together, these advances promise more resilient IT systems, elevating operational efficiency and user satisfaction in an ever-evolving technological landscape.