Automatic log file analysis enables early detection of relevant incidents such as system
failures. In particular, self-learning anomaly detection techniques capture patterns in log …

Logging is a common practice in software engineering to provide insights into working
systems. The main uses of log files have always been failure identification and root cause …

Log data is an essential and valuable resource of online service systems, which records
detailed information of system running status and user behavior. Log anomaly detection is …

Today, software systems are getting increasingly large and complex and a short failure time
may cause huge loss. Therefore, it is important to detect and diagnose anomalies accurately …

Anomaly detection becomes increasingly important for the dependability and serviceability
of IT services. As log lines record events during the execution of IT services, they are a …

Distributed databases are fundamental infrastructures of today's large-scale software
systems such as cloud systems. Detecting anomalies in distributed databases is essential …

Log-based anomaly detection plays an essential role in the fast-emerging Artificial
Intelligence for IT Operations (AIOps) of software systems. Many log-based anomaly …

With the increasing complexity of modern software systems, it is essential yet hard to detect
anomalies and diagnose problems precisely. Existing log-based anomaly detection …

Large-scale software-intensive systems often generate logs for troubleshooting purpose.
The system logs are semi-structured text messages that record the internal status of a …

Log-based anomaly detection is an essential aspect of maintaining software reliability,
particularly in the context of microservice systems. However, existing log-based anomaly …