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pro vyhledávání: '"Jessen, Urszula"'
Autor:
Jessen, Urszula, Fahland, Dirk
Anomalies in complex industrial processes are often obscured by high variability and complexity of event data, which hinders their identification and interpretation using process mining. To address this problem, we introduce WISE (Weighted Insights f
Externí odkaz:
http://arxiv.org/abs/2410.04387
Autor:
Berti, Alessandro, Maatallah, Mayssa, Jessen, Urszula, Sroka, Michal, Ghannouchi, Sonia Ayachi
Large Language Models (LLMs) have emerged as powerful conversational interfaces, and their application in process mining (PM) tasks has shown promising results. However, state-of-the-art LLMs struggle with complex scenarios that demand advanced reaso
Externí odkaz:
http://arxiv.org/abs/2408.07720
Object-centric event logs, allowing events related to different objects of different object types, represent naturally the execution of business processes, such as ERP (O2C and P2P) and CRM. However, modeling such complex information requires novel p
Externí odkaz:
http://arxiv.org/abs/2407.09023
This research investigates the application of Large Language Models (LLMs) to augment conversational agents in process mining, aiming to tackle its inherent complexity and diverse skill requirements. While LLM advancements present novel opportunities
Externí odkaz:
http://arxiv.org/abs/2307.09909
Publikováno v:
International Journal of Data Science and Analytics; 20230101, Issue: Preprints p1-23, 23p