COLON
发表于 2025-3-25 05:28:25
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疏远天际
发表于 2025-3-25 11:35:10
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知识
发表于 2025-3-25 12:35:52
Visual Analytics Meets Process Mining: Challenges and Opportunities,algorithms makes complex information structures more comprehensible and facilitates new insights. In this position paper I portray various concepts of interactive visual support for process mining, focusing on the challenges, but also the great opportunities for analyzing process data with visual analytics methods.
评论者
发表于 2025-3-25 16:28:31
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Anal-Canal
发表于 2025-3-25 23:37:12
Introduction: Nonlocal or Unreal?-critical aspects. To create complete, valid and reliable planning and customization process data for a product deployment, an overarching engineering process is crucial for the successful completion of a project. In this paper, we introduce a framework for process management in complex engineering
LURE
发表于 2025-3-26 00:44:46
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有毛就脱毛
发表于 2025-3-26 05:07:27
Introduction: Nonlocal or Unreal?ntinuously evolve over time. For example, in the healthcare domain, advances in medicine trigger changes in diagnoses and treatment processes. Case data (e.g. treating physician, patient age) also influence how processes are executed. Existing . techniques assume processes to be static and therefore
丰富
发表于 2025-3-26 11:31:55
Conceptual Basis of Quantum Mechanicsithout allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. Firs
小卷发
发表于 2025-3-26 13:48:55
William Jankowiak,Claudia Woodmane of a consistent terminology to label the activities of these process models. To support this task, prior research has proposed quality metrics to support the usage of consistent terms, mainly based on linguistic relations such as synonymy or homonymy. In this paper, we propose a new approach that
Abrade
发表于 2025-3-26 18:20:41
Kinship: The Tie that Binds (Disciplines)al effects, delays and performance trends are well-known to exist for business processes, there is up until now no prediction model available that explicitly captures seasonality. In this paper, we introduce time series Petri net models. These models integrate the control flow perspective of Petri n