To aggregate or to eliminate? Optimal model simplification for improved process performance prediction

作者：

Highlights：

• A technique for performance-driven model reduction of GSPNs is proposed.

• The technique relies on foldings that aggregate or eliminate performance information.

• Foldings preserve model stability and have a bound for the introduced performance estimation error.

• Given a budget for the estimation error, an optimal sequence of foldings can be found.

摘要

•A technique for performance-driven model reduction of GSPNs is proposed.•The technique relies on foldings that aggregate or eliminate performance information.•Foldings preserve model stability and have a bound for the introduced performance estimation error.•Given a budget for the estimation error, an optimal sequence of foldings can be found.

论文关键词：Generalised stochastic Petri nets,Model Simplification,Folding,Elimination,Aggregation,Process Mining

论文评审过程：Received 19 December 2016, Revised 7 March 2018, Accepted 12 April 2018, Available online 13 April 2018, Version of Record 13 September 2018.

论文官网地址：https://doi.org/10.1016/j.is.2018.04.003