EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Prediction

2026-07-02

AI Summary

Existing approaches to modeling Object-Centric Event Logs (OCEL) struggle to simultaneously capture the complex interactions among multiple types of business objects within shared events, event-driven state evolution, and both local temporal dynamics and global execution patterns. To address these limitations, this work proposes the EHHN model, which uniquely represents predictive prefixes as heterogeneous hypergraphs. EHHN employs a dual-stream architecture that separately models fine-grained object state transitions and coarse-grained temporal dynamics through global prototype retrieval, then effectively fuses these representations for next-activity prediction. Evaluated on four public OCEL datasets, the proposed method consistently achieves state-of-the-art performance, yielding up to an 8.1% improvement in accuracy and a 12.4% gain in macro F1-score over the strongest baseline, while reducing peak GPU memory consumption by up to 24-fold.

Abstract

Next activity prediction helps service-oriented processes anticipate upcoming steps before delays, exceptions, or service-level risks occur. Most existing methods assume classical single-case event logs, whereas real service processes often involve events shared by multiple typed business objects. Object-centric event logs (OCELs) capture such interactions, but current predictors remain limited. Flattening-based approaches lose cross-object context, and native OCEL graph-based approaches encode multi-object events through pairwise relations. Existing models also do not jointly capture event-driven object state changes, inter-event timing, and global execution patterns. We propose EHHN, an Event-driven Heterogeneous Hypergraph Network for object-centric next activity prediction. EHHN represents each prediction prefix as a heterogeneous hypergraph, where event--object hyperedges bind retained co-participating objects and a lifecycle hyperedge groups the primary object's observed lifecycle events. Based on this representation, EHHN uses a dual-stream architecture in which a micro-spatial stream models event-driven object-state evolution and a macro-evolution stream captures temporal dynamics using retrieved global prototypes. The two streams are fused to predict the next activity. Experiments on four public OCEL benchmarks against nine baselines show that EHHN achieves the best accuracy and macro F1-score on all datasets, with improvements of up to 8.1 and 12.4 percentage points over the strongest baselines. Compared with the strongest OCEL-native graph baseline, EHHN also reduces peak GPU memory by up to 24 times. Code is available at https://github.com/chenkaitao1112/EHHN.

Problem

• next activity prediction
• object-centric event logs
• heterogeneous hypergraph
• event-driven modeling
• process mining

Innovation

• heterogeneous hypergraph
• object-centric event log
• next activity prediction
• dual-stream architecture
• event-driven modeling