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Titlebook: Business Process Management Forum; BPM 2024 Forum, Krak Andrea Marrella,Manuel Resinas,Michael Rosemann Conference proceedings 2024 The Edi

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發(fā)表于 2025-3-21 17:37:35 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Business Process Management Forum
期刊簡稱BPM 2024 Forum, Krak
影響因子2023Andrea Marrella,Manuel Resinas,Michael Rosemann
視頻videohttp://file.papertrans.cn/193/192805/192805.mp4
學(xué)科分類Lecture Notes in Business Information Processing
圖書封面Titlebook: Business Process Management Forum; BPM 2024 Forum, Krak Andrea Marrella,Manuel Resinas,Michael Rosemann Conference proceedings 2024 The Edi
影響因子.This book constitutes the proceedings of the BPM Forum held at the 22nd International Conference on Business Process Management, BPM 2024, which took place in Krakow, Poland, in September 2024. The BPM Forum hosts innovative research which has a high potential of stimulating discussions. The papers selected for the forum are expected to showcase fresh ideas from exciting and emerging topics in BPM, even if they are not yet as mature as the regular papers at the conference...The 21 papers included in this book were carefully reviewed and selected from a total of 144 submissions to the conference. The papers were organized in research tracks on foundations, engineering, and management.?.
Pindex Conference proceedings 2024
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Object-Centric Event Logs: Characteristics, Comparative Analysis and?Road Mapby considering multiple objects interacting with each other in a process. This object-centric approach offers advantages over traditional methods by avoiding dimension reduction issues. However, in contrast to traditional process mining where a standard event log format was quickly agreed upon with
地板
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Explainable DMN complexity of DMN models can otherwise quickly make it unfeasible for companies to understand and maintain their decision logic. To this aim, we present a formal approach for measuring the . of decision inputs on the decision output. In particular, we show how the decision logic of a DMN model can
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NL2ProcessOps: Towards LLM-Guided Code Generation for?Process Executionnt. This paper presents NL2ProcessOps, a novel approach leveraging Large Language Models (LLMs) and concepts such as Retrieval Augmented Generation (RAG), agents, and tools for code generation to streamline process deployment operations. The proposed approach is designed to work with textual process
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CMMN-Based Modeling and?Customization of?Declarative Business Process Familiesn be expressed using concrete modeling languages and then customized at design time into a specific process variant to enact according to particular configuration requirements. Most process family modeling proposals focus on imperative processes, such as extensions of the Business Process Model and
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Interactive Drift Visualization in?Sensor Data Streams for?Explainable Process Outcome Predictiondrifts, e.g., the quality of a product can be affected by the temperature during the production process. In particular, drifts can explain variations in the process outcome, and hence, their early detection can support the definition of mitigation actions, e.g., canceling the production of probably
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