Abstract Explainable Predictive Process Monitoring aims at deriving explanations of the
inner workings of black-box classifiers used to predict the continuation of ongoing process …

Process mining provides methods to analyse event logs generated by information systems
during the execution of processes. It thereby supports the design, validation, and execution …

Predictive process monitoring methods predict ongoing case outcomes by analyzing
historical process data. Recent studies highlighted the increasing need to enhance the …

Generating anomalies for process executions helps to train anomaly detection methods and
evaluate their performance. Anomalous behavior tends to be diverse and very infrequent …

Nirdizati Light is an innovative Python package designed for Explainable Predictive Process
Monitoring (XPPM). It addresses the need for a modular, flexible tool to compare predictive …

For metastatic esophagogastric cancer (EGC), treatment aims to extend survival time.
However, determining the best treatments for patients with EGC is challenging due to …

Process pattern discovery methods (PPDMs) have been developed with the primary goal of
identifying patterns of interest to users. Existing PPDM approaches are predominantly …

In the last decade several Process Pattern Discovery methods have been developed, such
as Sequential Pattern Mining and Significant Pattern Mining. These methods aim to extract …