Keywords: AI Interpretability, Embedding Space, Internal Representations, Multimodal Data, Semantic Event Matching
Astract: Traditional process mining relies on symbolic event logs that represent activities as discrete labels, often overlooking the rich contextual and semantic nuances found in real-world data such as textual reports, visual records, or sensor outputs. In this paper, we propose a paradigm shift: using the internal representations of AI models—embedding spaces learned from data—as the foundation for process mining. Our framework performs both process discovery and conformance checking directly in these continuous vector spaces, enabling the detection of semantically similar yet lexically divergent events. We evaluate our approach along three dimensions: (i) whether embedding-based discovery maintains or improves accuracy over symbolic baselines, (ii) whether multimodal sources such as video and audio can be processed as unified embeddings for mining purposes, and (iii) whether conformance checking in embedding space enables alignment across noisy or semantically perturbed traces. By treating AI’s internal representations as a novel form of process evidence, we show how process mining can move beyond traditional logs and unlock deeper, semantically enriched interpretations of real-world workflows.

Keywords: Business process simulation, Video generation, Process modeling
Astract: Video generation models have opened new opportunities for simulating business processes through realistic visualizations. However, current video generation approaches often fall short of capturing the inherent dynamics and structure of business processes and tend to produce inconsistent simulations that lack the rigor provided by formal process models. To address these limitations, we introduce a novel method termed Petri Net structure-driven video generation, which integrates the inherent structural information from process models to tailor video simulations more closely to the dynamics of business processes. We explore multiple strategies for this tailoring, including i) the use of domain knowledge-rich prompting, ii) a storyboard employing image references extracted from process evidence data, and iii) generated image references informed by process models. We evaluate our method across diverse domains, and demonstrate that the Petri Net structure-driven approach improves the perceived usefulness and consistency of the simulated video, marking a step forward in the use of generative AI for more realistic business process simulation.

Keywords: Process Mining

Keywords: Conceptual Modeling, Multimodal data, Model Enrichment

Keywords: Multimodal data analysis, Mixed Reality, Process Mining
Astract: This paper discusses an end-to-end methodology for real-time surgical conformance checking that uses multimodal process mining, mixed reality (MR), and large language model (LLM) prompting. Our approach aims to support surgeons and medical teams by comparing as-is operational data—captured through a variety of sensors including MR-based gaze tracking—with a reference surgical process model encoded in Business Process Modeling Notation (BPMN). We illustrate how shallow and deep human-in-the-loop feedback mechanisms can be integrated with chain-of-thought prompting to provide relevant, context-aware, and iterative feedback during surgery. We further indicate which aspects of the surgery can be monitored (and hence queried) by our multimodal process mining engine. By enabling precise, actionable feedback during critical surgical procedures, our approach enhances the ability to identify deviations, ensure adherence to best practices, and reduce human error. Ultimately, this methodology empowers surgical teams to make data-driven adjustments, promotes better patient outcomes, and allows hospitals to monitor surgical conformance effectively, setting a new standard for process-driven healthcare assistance.