Guided Uncertainty Reduction in Automatically Generated Business Simulations

Abstract

Model-Driven Performance Engineering enables the automatic generation of simulation models based on business process models and monitored process instance data. When we applied an initial version of our tooling to a number of real world processes, we experienced that we need to support the mapping of monitored process instance data into simulation models under consideration of cases where confidence in these data is low, for instance due to a high variance in monitored resource demands, or a low number of executed process instances. The current paper proposes an architecture which utilizes a decision tree for the intelligent mapping of monitored process instance data into simulation models and, as a by-product, which ranks uncertainties within the imported data.