# Process Mining

The work presents pm4aa, a pipeline that extracts object-centric event logs from GitHub repositories via PyStack't, maps commits to SE tasks with a Conventional Commits regular expression, partitions 589 users into eight roles with a priority-ordered rule classifier, applies object-centric, imperative (BPMN), and declarative (DECLARE) process mining per role, has an LLM generate process descriptions, and synthesizes a LangGraph multi-agent application through IBM BOB; on the Commitizen project (November 2017 to November 2025, 21,488 events, 4,813 objects) it produced five role agents, three smoke tests routed correctly, and a ten-participant user study rated knowledge schema, operational clarity, and human engagement at a median of 4, accountability at 3, and autonomy at only 2.

The work presents pm4aa, a pipeline that extracts object-centric event logs from GitHub repositories via PyStack't, maps commits to SE tasks with a Conventional Commits regular expression, partitions 589 users into eight roles with a priority-ordered rule classifier, applies object-centric, imperative (BPMN), and declarative (DECLARE) process mining per role, has an LLM generate process descriptions, and synthesizes a LangGraph multi-agent application through IBM BOB; on the Commitizen project (November 2017 to November 2025, 21,488 events, 4,813 objects) it produced five role agents, three smoke tests routed correctly, and a ten-participant user study rated knowledge schema, operational clarity, and human engagement at a median of 4, accountability at 3, and autonomy at only 2.

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.

The work presents SnapLog, a pipeline that encodes each video frame with a pre-trained ViT, performs temporal segmentation through a frame-wise similarity matrix with K-Means and a greedy merge, then applies generalized few-shot classification with a 34-layer R(2+1)D encoder pre-trained on Sports-1M plus a linear head to convert raw video into timestamped event logs (either deterministic or uncertainty-aware logs that retain label probability distributions); on 16 brownie-baking videos from TUM Kitchen and 24 kitchen-cleaning videos from Epic Kitchens-100 it achieves frame-wise segmentation accuracy of 74.3% and 67.7%, and with augmentation plus 100 overlapping clips Top-3 classification accuracy of 90.1% and 85.

The work presents SnapLog, a pipeline that encodes each video frame with a pre-trained ViT, performs temporal segmentation through a frame-wise similarity matrix with K-Means and a greedy merge, then applies generalized few-shot classification with a 34-layer R(2+1)D encoder pre-trained on Sports-1M plus a linear head to convert raw video into timestamped event logs (either deterministic or uncertainty-aware logs that retain label probability distributions); on 16 brownie-baking videos from TUM Kitchen and 24 kitchen-cleaning videos from Epic Kitchens-100 it achieves frame-wise segmentation accuracy of 74.3% and 67.7%, and with augmentation plus 100 overlapping clips Top-3 classification accuracy of 90.1% and 85.