How Object-Centric is Object-Centric Predictive Process Monitoring? Introducing Objects into Object-Centric Predictive Process Monitoring
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The field of process mining is challenged by the complexity of true processes when extracting accurate process behavior and statistics from information systems. Traditional process mining algorithms assume a single case notion, whereas actual processes contain many possible ones, represented by objects. Object-centric process mining has been introduced as a case-agnostic solution, which mitigates the problem of misleading process behavior and statistics via the object-centric event log (OCEL). It allows multiple case notions which are called object types. Objects have many-to-many relationships with events. However, when performing predictive process monitoring on OCELs, issues arise when including object information as features due to these many-to-many relationships. This has not been addressed by existing literature. We propose a heterogeneous object event graph encoding (HOEG), that incorporates events and objects into a graph with different node types. We evaluate our novel encoding against an extant graph-based encoding and several baselines on the task of remaining time prediction. On our HOEG we employ a heterogeneous graph neural network (GNN) architecture that is converted from a homogeneous one. The HOEG-based GNN learns an optimal way to include object information when forming predictions. The experiments are executed on three OCELs, one of which is extracted from an operational process at a large Dutch financial institution. Our results indicate that HOEG outperforms its competition for well-structured OCELs. Furthermore, we argue that HOEG mainly excels when OCELs host informative object attributes and abundant object interactions. Considering this, we propose HOEG as a promising general technique to leverage the multi-dimensional data structure given in OCELs for tasks like predicting process remaining time.
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