Systems, Applications & Products in Data Processing (Germany)

Analyzing the intrinsic difficulty of delay detection across 14 public event logs, the study finds that remaining times are strongly right-skewed, that existing LSTM models capture the mode of the distribution but perform poorly on high-delay cases, and that prediction error and prediction-interval width both grow with true remaining time (positive in 10 of 14 logs); it then evaluates imbalanced regression approaches (SMOGN resampling, CSW, BMSE, EAL, SERA) with only limited benefit, and instead feeds uncertainty features from a survival model (prediction standard deviation, 80% and 90% prediction-interval widths, tail mass, and temporal context) into a CatBoost classifier for binary delay detection, raising average recall from 0.21 to 0.61 at the q=0.

Analyzing the intrinsic difficulty of delay detection across 14 public event logs, the study finds that remaining times are strongly right-skewed, that existing LSTM models capture the mode of the distribution but perform poorly on high-delay cases, and that prediction error and prediction-interval width both grow with true remaining time (positive in 10 of 14 logs); it then evaluates imbalanced regression approaches (SMOGN resampling, CSW, BMSE, EAL, SERA) with only limited benefit, and instead feeds uncertainty features from a survival model (prediction standard deviation, 80% and 90% prediction-interval widths, tail mass, and temporal context) into a CatBoost classifier for binary delay detection, raising average recall from 0.21 to 0.61 at the q=0.

The work introduces the concept of an organizational memory for agentic business process execution—a shared, human-governed, agent-consumable reference layer of organization-specific procedural knowledge—and derives requirements (R1–R9), an architecture covering memory curation and runtime consumption, and an instantiation based on process atoms; in a purchase-to-pay proof-of-concept, the Policy Compliance Rate averaged over 10 scenarios with four runs each reached 88% with GPT-4.1 and 95% with Claude Sonnet 4.5 for the memory-equipped agent, versus 70% and 80% for RAG and 30% for the no-knowledge base setup with both models.

The work introduces the concept of an organizational memory for agentic business process execution—a shared, human-governed, agent-consumable reference layer of organization-specific procedural knowledge—and derives requirements (R1–R9), an architecture covering memory curation and runtime consumption, and an instantiation based on process atoms; in a purchase-to-pay proof-of-concept, the Policy Compliance Rate averaged over 10 scenarios with four runs each reached 88% with GPT-4.1 and 95% with Claude Sonnet 4.5 for the memory-equipped agent, versus 70% and 80% for RAG and 30% for the no-knowledge base setup with both models.

The work introduces the first approach for multi-objective optimization of resource-level handover policies in business processes: taking a Multi-Agent System (MAS) simulation model as input, it uses NSGA-II to search for Pareto-optimal person-specific handover policies, and across five synthetic Loan Application variants and seven real event logs it reduces cost by an average of 37% and waiting time by 58% relative to the as-is process, outperforming availability, random, lowest-cost (LC), and shortest-processing-time (SPT) heuristic baselines in most settings.

The work introduces the first approach for multi-objective optimization of resource-level handover policies in business processes: taking a Multi-Agent System (MAS) simulation model as input, it uses NSGA-II to search for Pareto-optimal person-specific handover policies, and across five synthetic Loan Application variants and seven real event logs it reduces cost by an average of 37% and waiting time by 58% relative to the as-is process, outperforming availability, random, lowest-cost (LC), and shortest-processing-time (SPT) heuristic baselines in most settings.