Titlebook: Enterprise Design, Operations, and Computing; 26th International C Jo?o Paulo A. Almeida,Dimka Karastoyanova,Claudeni Conference proceeding

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PHONE

Scientific Research as a Global Enterprise,non-stationary processes, i.e., the impact of unseen data variability in event streams on prediction models from a structural and behavioural point of view. Strategies and methods are proposed to incorporate unknown data variability and to update recurrent neural network based models continuously in

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https://doi.org/10.1057/9781137352750t performs. They also highlight the challenge posed by control flow relations. Second, it provides a first set of lessons learned on how to interact with these kinds of models that can facilitate future investigations on this subject.

Exploit

https://doi.org/10.1057/9780230374010ed patterns. Playing by the rules ensures that the resulting models are always sound and free-choice. Furthermore, the discovered models are not restricted to block structures and are thus more flexible. The approach has been implemented in Python and tested using various real-life event logs. The e

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