Abstract
AbstractThe inference of performance models from low-level location tracking traces provides a means to gain high-level insight into customer and/or resource flow in complex systems. In this context our earlier work presented a methodology for automatically constructing Petri Net performance models from location tracking data. However, the capturing of synchronisation between service centres – the natural expression of which is one of the most fundamental advantages of Petri nets as a modelling formalism – was not explicitly supported. In this paper, we introduce mechanisms for automatically detecting and incorporating synchronisation into our existing methodology. We present a case study based on synthetic location tracking data where the derived synchronisation detection mechanism is applied.KeywordsLocation TrackingPerformance ModellingData MiningGeneralised Stochastic Petri Nets
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