Practical Solutions for Process Mining Enhancement
Introduction to Process Mining
Process mining involves analyzing event logs from information systems to understand business processes, optimizing workflows, and identifying areas for improvement.
Challenges in Process Mining
Dealing with complex scenarios in process mining often requires advanced reasoning and decision-making, which traditional tools struggle to handle effectively.
Existing Techniques and Limitations
Current process mining techniques use Large Language Models (LLMs) to generate insights and code for process analysis, but they face limitations in handling complex scenarios that require combining different skills.
AI-Based Agents Workflow Paradigm
The AI-Based Agents Workflow (AgWf) paradigm, developed through collaboration between multiple institutions, addresses the limitations of traditional methods by decomposing complex tasks into more manageable workflows.
Benefits of AgWf Methodology
The AgWf methodology breaks down complex tasks into smaller units, with specialized agents handling each, resulting in improved accuracy, reliability, and efficiency in process mining tasks.
Results and Implications
Testing of the AgWf methodology demonstrated significant improvements in handling complex process mining tasks, achieving higher accuracy rates and overcoming the limitations of current techniques.
Advancement in Process Mining
The AI-Based Agents Workflow represents a powerful paradigm for enhancing the effectiveness of process mining, offering significant accuracy and reliability improvements for organizations.
AI Integration for Business Evolution
Utilizing the AI-Based Agents Workflow paradigm can redefine business processes, optimize workflows, and enhance customer engagement through AI solutions.
AI Implementation Guidance
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