Revolutionizing Process Mining: A Novel Architecture for ChatGPT Integration and Enhanced User Experience through Optimized Prompt Engineering

TL;DR

This paper presents a novel architecture integrating ChatGPT with process mining tools through optimized prompt engineering, significantly enhancing user experience and analytical accuracy in business process management.

Contribution

It introduces a tailored prompt engineering strategy and an ETL-based integration architecture for ChatGPT in process mining, improving accessibility and relevance of AI-generated insights.

Findings

72% of results rated as 'Good' by experts

Significant improvements in user experience observed

Effective integration of ChatGPT with process mining modules

Abstract

In the rapidly evolving field of business process management, there is a growing need for analytical tools that can transform complex data into actionable insights. This research introduces a novel approach by integrating Large Language Models (LLMs), such as ChatGPT, into process mining tools, making process analytics more accessible to a wider audience. The study aims to investigate how ChatGPT enhances analytical capabilities, improves user experience, increases accessibility, and optimizes the architectural frameworks of process mining tools. The key innovation of this research lies in developing a tailored prompt engineering strategy for each process mining submodule, ensuring that the AI-generated outputs are accurate and relevant to the context. The integration architecture follows an Extract, Transform, Load (ETL) process, which includes various process mining engine modules and utilizes zero-shot and optimized prompt engineering techniques. ChatGPT is connected via APIs and receives structured outputs from the process mining modules, enabling conversational interactions. To validate the effectiveness of this approach, the researchers used data from 17 companies that employ BehfaLab's Process Mining Tool. The results showed significant improvements in user experience, with an expert panel rating 72% of the results as "Good". This research contributes to the advancement of business process analysis methodologies by combining process mining with artificial intelligence. Future research directions include further optimization of prompt engineering, exploration of integration with other AI technologies, and assessment of scalability across various business environments. This study paves the way for continuous innovation at the intersection of process mining and artificial intelligence, promising to revolutionize the way businesses analyze and optimize their processes.