Partial label learning (PLL) is an emerging framework in weakly supervised machine
learning with broad application prospects. It handles the case in which each training …

Recording runtime status via logs is common for almost computer system, and detecting
anomalies in logs is crucial for timely identifying malfunctions of systems. However …

Nowadays, reliability assurance is crucial in components of IT infrastructures. Unavailability
of any element or connection results in downtime and triggers monetary and performance …

Log anomaly detection is an efficient method to manage modern large-scale Internet of
Things (IoT) systems. More and more works start to apply natural language processing …

In modern datacenter networks (DCNs), failures of network devices are the norm rather than
the exception, and many research efforts have focused on dealing with failures after they …

Anomaly detection is critical for web-based software systems. Anecdotal evidence suggests
that in these systems, the accuracy of a static anomaly detection method that was previously …

Logs are one of the most valuable data sources for large-scale service management. Log
representation, which converts unstructured texts to structured vectors or matrices, serves as …

The Internet of Things (IoT), in combination with advancements in Big Data, communications
and networked systems, offers a positive impact across a range of sectors including health …

Logs are imperative in the management process of networks and services. However,
manually identifying and classifying anomalous logs is time-consuming, error-prone, and …

Logs are one of the most valuable data sources for large-scale service (eg, social network,
search engine) maintenance. Log parsing serves as the the first step towards automated log …