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

Previous studies investigating the efficacy of long short-term memory (LSTM) recurrent
neural networks in predictive process monitoring and their ability to capture the underlying …

In the context of online banking, new users have to register their information to become
clients through mobile applications; this process is called digital onboarding. Fraudsters …

Process mining can help organizations by extracting knowledge from event logs. However,
process mining techniques often assume business processes are stationary, while actual …

Purpose This paper aims to describe the use of a meta-learning framework for
recommending cost-sensitive classification methods with the aim of answering an important …

Business processes have undergone a significant transformation with the advent of the
process-oriented view in organizations. The increasing complexity of business processes …

Recording anomalous traces in business processes diminishes an event log's quality. The
abnormalities may represent bad execution, security issues, or deviant behavior. Focusing …

Process discovery aims to automatically derive process models from historical execution
data (event logs). While various process discovery algorithms have been proposed in the …

Through constraints, declarative process models represent the permitted behaviour
associated with a business process, by limiting the potential correct traces. These models …

With the rapid development of artificial intelligence, the selection of algorithms that meet
application requirements from feasible algorithms has become a critical problem to be …