Association for Computing Machinery
Stochastic performance models are widely used to analyze systems that involve the flow and processing of customers and resources. However, model formulation and parameterization are traditionally manual and thus expensive, intrusive and error-prone. The authors' earlier paper has demonstrated the feasibility of automated performance model construction from location tracking data. In particular, they presented a methodology based on a four-stage data processing pipeline, which automatically constructs Generalized Stochastic Petri Net (GSPN) performance models from an input dataset of raw location tracking traces.