  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.
