Titlebook: Business Process Management; 21st International C Chiara Di Francescomarino,Andrea Burattin,Shazia S Conference proceedings 2023 The Editor

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Event Abstraction for?Partial Order Patterns and organizations, there does not exist a universal technique to allow for putting the process mining outcome directly into action. Various techniques have been developed to support human analysis. Meanwhile, as raw event data are often provided at the system level, the . is applied to “l(fā)ift” the d

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Incremental Discovery of?Process Models Using Trace Fragmentsd, i.e., event data is provided, and a process model is returned. Thus, process analysts cannot interact and intervene besides parameter settings. In contrast, Incremental Process Discovery (IPD) enables users to actively participate in the discovery phase by gradually selecting process behavior to

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Investigating the?Influence of?Data-Aware Process States on?Activity Probabilities in?Simulation Mod how to improve a business process. Process simulation requires one to provide a simulation model, which should accurately reflect reality to ensure the reliability of the simulation findings. An accurate simulation model passes through a correct stochastic modelling of the activity firings: activit

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DyLoPro: Profiling the?Dynamics of?Event Logs a static process. This bias is caused by concept drift, which can manifest in many forms and affect various process perspectives. Current research on concept drift in process mining has focused on drift detection techniques in the control-flow perspective, with limited capabilities for comprehensiv

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Inferring Missing Entity Identifiers from?Context Using Event Knowledge Graphsm has operated on, resulting in incomplete event data. We aim to infer missing case identifiers of events by considering the physical constraints of the process which previous work has failed to do. We extended Event Knowledge Graphs (EKGs) with concepts for context and rule-based inference. We use

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