LLM-DPM - Workshop on Large Language Models for Data Process Management

The LLM-DPM Workshop investigates the transformative impact of Large Language Models (LLMs) and Explainable AI (XAI) on Data and Process Management. With organizations facing growing dependence on intricate data-driven workflows, there is an urgent demand for systems that prioritize not only efficiency but also transparency, reliability, and equity. This workshop serves as a dedicated platform to explore the convergence of LLMs, process mining, and database technologies, tackling critical issues such as enhancing data integrity, refining query understanding, forecasting process outcomes, and optimizing system performance. Featuring technical discussions, keynote speeches, and collaborative panels, LLM-DPM seeks to bridge the gap between academic research and industry applications, inspiring innovative approaches and advancing the creation of accountable, AI-powered solutions for process management challenges.

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Publication Details

Published
2025-06-17
DOI
https://doi.org/10.1145/3722212.3724492
Primary Topic
Business Process Modeling and Analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

LLM-DPM - Workshop on Large Language Models for Data Process Management

Samira Maghool, Paolo Ceravolo, Faiza Allah Bukhsh, Xu Chu et al.
Business Process Modeling and Analysis
article

LLM-DPM - Workshop on Large Language Models for Data Process Management

Samira Maghool, Paolo Ceravolo, Faiza Allah Bukhsh, Xu Chu, Eugene Wu, C. X. Yu
article en

Abstract

The LLM-DPM Workshop investigates the transformative impact of Large Language Models (LLMs) and Explainable AI (XAI) on Data and Process Management. With organizations facing growing dependence on intricate data-driven workflows, there is an urgent demand for systems that prioritize not only efficiency but also transparency, reliability, and equity. This workshop serves as a dedicated platform to explore the convergence of LLMs, process mining, and database technologies, tackling critical issues such as enhancing data integrity, refining query understanding, forecasting process outcomes, and optimizing system performance. Featuring technical discussions, keynote speeches, and collaborative panels, LLM-DPM seeks to bridge the gap between academic research and industry applications, inspiring innovative approaches and advancing the creation of accountable, AI-powered solutions for process management challenges.

University of Milan (IT), Celsion (United States) (US), Columbia University (US), University of Twente (NL)
Openalex Percentile: Top 76%
Business Process Modeling and Analysis
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