Detecting Anomalous Events in Object-centric Business Processes via Graph Neural Networks

arXiv:2403.00775v1 Announce Type: cross
Abstract: Detecting anomalies is important for identifying inefficiencies, errors, or fraud in business processes. Traditional process mining approaches focus on analyzing 'flattened', sequential, event logs based on a single case notion. However, many real-world process executions exhibit a graph-like structure, where events can be associated with multiple cases. Flattening event logs requires selecting a single case identifier which creates a gap with the real event data and artificially introduces anomalies in the event logs. Object-centric process …

abstract arxiv business business processes case cs.db cs.lg errors event events focus fraud graph graph neural networks logs mining networks neural networks notion process processes process mining q-fin.st type via world