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Data Skeptic

Process Mining with LLMs

Sep 24, 2024
26:24
Snipd AI
David Obembe, a recent graduate from the University of Tartu, dives into his Master’s thesis on blending large language models (LLMs) with process mining tools. He explains how process mining can reveal inefficiencies using event logs, and discusses the potential of LLMs to enhance these analyses. David shares insights on methodologies for transforming raw data into actionable insights, the role of Retrieval Augmented Generation (RAG), and challenges like prompt engineering and hallucination in AI models.
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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.

Deep dives

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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