  Object-centric event logs, allowing events related to different objects of
different object types, represent naturally the execution of business
processes, such as ERP (O2C and P2P) and CRM. However, modeling such complex
information requires novel process mining techniques and might result in
complex sets of constraints. Object-centric anomaly detection exploits both the
lifecycle and the interactions between the different objects. Therefore,
anomalous patterns are proposed to the user without requiring the definition of
object-centric process models. This paper proposes different methodologies for
object-centric anomaly detection and discusses the role of domain knowledge for
these methodologies. We discuss the advantages and limitations of Large
Language Models (LLMs) in the provision of such domain knowledge. Following our
experience in a real-life P2P process, we also discuss the role of algorithms
(dimensionality reduction+anomaly detection), suggest some pre-processing
steps, and discuss the role of feature propagation.
