Titlebook: Business Process Management; 22nd International C Andrea Marrella,Manuel Resinas,Michael Rosemann Conference proceedings 2024 The Editor(s)

茁壯成長

誰在削木頭

On the?Interplay Between BPMN Collaborations and?the?Physical Environmentental BPMN collaboration models. To facilitate a deeper understanding of the dynamics of these models, we provide a formal account of their semantics. We illustrate our findings through a fire-extinguishing collaborative scenario.

frugal

Exploiting General Purpose Big-Data Frameworks in?Process Mining: The?Case of?Declarative Process Dioping tailored systems for declarative processes. We build on top of a recent scalable framework, named SIESTA, which can perform efficient pattern analysis on large log files. Our approach yields promising results, significantly outperforming the existing Declare Miner and MINERful solutions.

Decongestant

知識分子

stressors

Unity through uniform private law DPNs. Furthermore, we discuss how PP can be used for process mining tasks and report on a prototype implementation of our translation. We also discuss further analysis scenarios that could be easily approached based on the proposed translation and available PP tools.

凝乳

Unity through uniform private law offering an aggregation of closely related variants. We propose a super-variant mining framework based on object-centric variants, evaluate its scalability, and demonstrate its utility through a practical use case. This new approach promises to enhance control-flow analysis by striking a new balance between complexity and aggregation.

Obvious

Data Petri Nets Meet Probabilistic Programming DPNs. Furthermore, we discuss how PP can be used for process mining tasks and report on a prototype implementation of our translation. We also discuss further analysis scenarios that could be easily approached based on the proposed translation and available PP tools.

aerial

Super Variants offering an aggregation of closely related variants. We propose a super-variant mining framework based on object-centric variants, evaluate its scalability, and demonstrate its utility through a practical use case. This new approach promises to enhance control-flow analysis by striking a new balance between complexity and aggregation.

miniature