Process Mining allows organizations to obtain actual business process models from event
logs (discovery), to compare the event log or the resulting process model in the discovery …

While a business process is most often executed following a normal path, anomalies may
sometimes arise and can be captured in event logs. Event log anomalies stem, for instance …

Artificial Intelligence for IT Operations (AIOps) combines big data and machine learning to
replace a broad range of IT Operations tasks including availability, performance, and …

Businesses are naturally interested in detecting anomalies in their internal processes,
because these can be indicators for fraud and inefficiencies. Within the domain of business …

Anomaly detection is one of the crucial tasks in daily infrastructure operations as it can
prevent massive damage to devices or resources, which may then lead to catastrophic …

Encoding methods are employed across several process mining tasks, including predictive
process monitoring, anomalous case detection, trace clustering, etc. These methods are …

In this paper, we introduce BINet, a neural network architecture for real-time multi-
perspective anomaly detection in business process event logs. BINet is designed to handle …

This book encompasses a revised version of the PhD dissertation of Jorge Munoz-Gama
written at the Computer Science Department of the Universitat Politècnica de Catalunya …

Anomaly detection in process mining aims to recognize outlying or unexpected behavior in
event logs for purposes such as the removal of noise and identification of conformance …

In this paper, we propose BINet, a neural network architecture for real-time multivariate
anomaly detection in business process event logs. BINet has been designed to handle both …