期满
发表于 2025-3-23 10:49:16
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扫兴
发表于 2025-3-23 16:49:13
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FLING
发表于 2025-3-23 18:37:05
science by discovering process models using event logs derived from real-world data. Besides mandatory event attributes like case identifier, activity, and timestamp, additional event attributes can be present, such as human resources, costs, and laboratory values. These event attributes can be mod
步兵
发表于 2025-3-23 22:27:19
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sperse
发表于 2025-3-24 03:39:00
Tobias Brandesher information artefacts may exist concurrently for the same business activity. In this paper, we investigate integrating business process models and business rules. Prior literature presents three such approaches, namely text annotation, diagrammatic, and link integration. We evaluate these approa
instill
发表于 2025-3-24 09:55:19
Mahn-Soo Choi,C. Bruder,Daniel Lossdizing business processes are based on manually created “de-jure” process models, which are distorted, error-prone, simplistic, and often deviating from process reality. Theoretically embedded in the organizational contingency theory as kernel theory, this paper employs a design science approach to
津贴
发表于 2025-3-24 14:43:50
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微尘
发表于 2025-3-24 17:27:29
Dmitri S. Golubev,Andrei D. Zaikinher information artefacts may exist concurrently for the same business activity. In this paper, we investigate integrating business process models and business rules. Prior literature presents three such approaches, namely text annotation, diagrammatic, and link integration. We evaluate these approa
bonnet
发表于 2025-3-24 20:36:08
Jean-Philippe Bourgoindizing business processes are based on manually created “de-jure” process models, which are distorted, error-prone, simplistic, and often deviating from process reality. Theoretically embedded in the organizational contingency theory as kernel theory, this paper employs a design science approach to
altruism
发表于 2025-3-25 03:02:15
R. Egger,A. Bachtold,M. S. Fuhrer,M. Bockrath,D. H. Cobden,P. L. McEuendepends on the prediction quality and the explainability of the prediction results. Both, prediction quality and explainability, can be influenced by unseen behavior, i.e., events that have not been observed in the training data so far. Unseen behavior can be caused by, for example, concept drift. E