business process performance prediction on a tracked simulation model
Business processes need to achieve key performance indicators with minimum resources in changing operating conditions. Changes include hardware and software failures, load variation and variations in user interaction with the system. By incorporating simulation in the prediction model it is possible to predict with more confidence system performance degradations. We present our dynamic predictive model which uses forecasting techniques on historical process performance estimates for business process optimization. The parameters of the simulation model are estimates tuned at run-time by tracking the system with a particle filter.
business process prediction simulation estimation optimization particle filter linear regression bpm box-jenkins arima