Large Process Models: Business Process Management in the Age of Generative AI

The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over rigidly defined symbolic models, but also serves as a proof-point of the challenges that purely statistics-based approaches have in terms of safety and trustworthiness. As a framework for contextualizing the potential, as well as the limitations of LLMs and other foundation model-based technologies, we propose the concept of a Large Process Model (LPM) that combines the correlation power of LLMs with the analytical precision and reliability of knowledge-based systems and automated reasoning approaches. LPMs are envisioned to directly utilize the wealth of process management experience that experts have accumulated, as well as process performance data of organizations with diverse characteristics, e.g., regarding size, region, or industry. In this vision, the proposed LPM would allow organizations to receive context-specific (tailored) process and other business models, analytical deep-dives, and improvement recommendations. As such, they would allow to substantially decrease the time and effort required for business transformation, while also allowing for deeper, more impactful, and more actionable insights than previously possible. We argue that implementing an LPM is feasible, but also highlight limitations and research challenges that need to be solved to implement particular aspects of the LPM vision.

Extracting Semantic Process Information from the Natural Language in Event Logs

Process mining focuses on the analysis of recorded event data in order to gain insights about the true execution of business processes. While foundational process mining techniques treat such data as sequences of abstract events, more advanced techniques depend on the availability of specific kinds of information, such as resources in organizational mining and business objects in artifact-centric analysis. However, this information is generally not readily available, but rather associated with events in an ad hoc manner, often even as part of unstructured textual attributes. Given the size and complexity of event logs, this calls for automated support to extract such process information and, thereby, enable advanced process mining techniques. In this paper, we present an approach that achieves this through so-called semantic role labeling of event data. We combine the analysis of textual attribute values, based on a state-of-the-art language model, with a novel attribute classification technique. In this manner, our approach extracts information about up to eight semantic roles per event. We demonstrate the approach's efficacy through a quantitative evaluation using a broad range of event logs and demonstrate the usefulness of the extracted information in a case study.