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Traditional process mining techniques take event data as input where eachevent is associated with exactly one object. An object represents theinstantiation of a process. Object-centric event data contain events associatedwith multiple objects expressing the interaction of multiple processes. Astraditional process mining techniques assume events associated with exactly oneobject, these techniques cannot be applied to object-centric event data. To usetraditional process mining techniques, the object-centric event data areflattened by removing all object references but one. The flattening process islossy, leading to inaccurate features extracted from flattened data.Furthermore, the graph-like structure of object-centric event data is lost whenflattening. In this paper, we introduce a general framework for extracting andencoding features from object-centric event data. We calculate featuresnatively on the object-centric event data, leading to accurate measures.Furthermore, we provide three encodings for these features: tabular,sequential, and graph-based. While tabular and sequential encodings have beenheavily used in process mining, the graph-based encoding is a new techniquepreserving the structure of the object-centric event data. We provide six usecases: a visualization and a prediction use case for each of the threeencodings. We use explainable AI in the prediction use cases to show theutility of both the object-centric features and the structure of the sequentialand graph-based encoding for a predictive model.

A Framework for Extracting and Encoding Features from Object-Centric Event Data