CoopIS2024: A Decomposed Hybrid Approach to Business Process and Data Modeling with LLMs

Effective business process execution requires the integration of both process logic and business data. While recent approaches explore the potential of Large Language Models (LLMs) in automating process modeling, their applicability is limited in real-world scenarios where textual descriptions—often authored by non-experts—are complex or incomplete. Moreover, these works primarily focus on the control-flow perspective and overlook the critical role of data modeling and execution. In this paper, we propose a hybrid and decomposed approach to automatically generate executable process and data models from text using LLMs. Our method modularizes the task: the LLM clarifies and enriches the description, then extracts both process and data elements, which are combined into a unified model. Structured algorithms ensure robust and executable outputs. Evaluation results demonstrate that our approach improves model completeness, clarity, and efficiency compared to existing methods.