Modeling business processes with simulation tools
WSC '94 Proceedings of the 26th conference on Winter simulation
Free choice Petri nets
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Coloured Petri Nets and CPN Tools for modelling and validation of concurrent systems
International Journal on Software Tools for Technology Transfer (STTT)
Cycle Time Prediction: When Will This Case Finally Be Finished?
OTM '08 Proceedings of the OTM 2008 Confederated International Conferences, CoopIS, DOA, GADA, IS, and ODBASE 2008. Part I on On the Move to Meaningful Internet Systems:
Information Systems
Complex events in business processes
BIS'07 Proceedings of the 10th international conference on Business information systems
History-Dependent stochastic petri nets
PSI'09 Proceedings of the 7th international Andrei Ershov Memorial conference on Perspectives of Systems Informatics
An iterative approach for business process template synthesis from compliance rules
CAiSE'11 Proceedings of the 23rd international conference on Advanced information systems engineering
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Business processes are constantly affected by the environment in which they execute. The environment can change due to seasonal and financial trends. For organisations it is crucial to understand their processes and to be able to estimate the effects of these trends on the processes. Business process simulation is a way to investigate the performance of a business process and to analyse the process response to injected trends. However, existing simulation approaches assume a steady state situation. Until now correlations and dependencies in the process have not been considered in simulation models, which can lead to wrong estimations of the performance. In this work we define an adaptive simulation model with a history-dependent mechanism that can be used to propagate changes in the environment through the model. In addition we focus on the detection of dependencies in the process based on the executions of the past. We demonstrate the application of adaptive simulation models by means of an experiment.