Discovery of Object-Centric Behavioral Constraint Models With Noise

Abstract

Process discovery techniques can automatically discover process models from event data. These models reveal the actual behavior of the related organization and have successfully been applied in a range of domains. Event data need to be extracted from information systems. Today, most organizations use object-centric systems such as ERP and CRM systems, which generate and store data in an object-centric manner. Unfortunately, existing discovery techniques are more focused on a behavioral perspective of processes, where the data perspective is often considered as a second-class citizen. Moreover, these discovery techniques fail to deal with object-centric data with many-to-many relationships. Event data need to be “flattened” focusing on a single object type (i.e., case notion). Therefore, in this paper, we aim to discover a novel model which combines data and behavior perspectives, and the resulting Object-Centric Behavioral Constraint (OCBC) model is able to describe processes involving interacting instances and complex data dependencies. Besides, we provide solutions to deal with the noise problem, which enables process discovery in real life data.

Keywords

• Process mining
• process discovery
• cardinality constraints
• OCBC models
• noise