Data Skeptic
Process Mining with LLMs
Sep 24, 2024
David Obembe, a recent graduate from the University of Tartu, dives into his master's thesis on blending large language models with process mining tools. He explains how process mining uses event logs to map out inefficiencies in business processes. Fascinating insights include the evolution of these techniques post-LLM integration, enhancing data retrieval and insights. David shares his experiments with Retrieval Augmented Generation and discusses the challenges of prompt engineering, highlighting the balance between accuracy and model reliability.
26:24
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Quick takeaways
- Integrating large language models with process mining tools enables a more conversational approach for business analysts to quickly identify inefficiencies.
- The use of retrieval-augmented generation techniques enhances LLMs' capabilities in querying complex databases, optimizing the accuracy of insights derived from event logs.
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Understanding Process Mining
Process mining is a technique used by businesses to extract insights from event log data, helping them identify areas for improvement. Every organization follows a set of activities, or processes, that create value for customers, and these processes can be mapped to visualize their flow. By analyzing event logs from various sources such as databases or internal systems, organizations convert raw data into process maps that inform operational decisions. This structured approach allows companies to optimize their operations and enhance competitive performance.
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