Operational process models such as generalised stochastic Petri nets (GSPNs) are useful when answering performance questions about business processes (e.g. ‘how long will it take for a case to finish?’). Recently, methods for process mining have been developed to discover and enrich operational models based on a log of recorded executions of processes, which enables evidence-based process analysis. To avoid a bias due to infrequent execution paths, discovery algorithms strive for a balance between over-fitting and under-fitting regarding the originating log. However, state-of-the-art discovery algorithms address this balance solely for the control-flow dimension, neglecting the impact of their design choices in terms of performance measures. In this work, we thus offer a technique for controlled performance-driven model reduction of GSPNs, using structural simplification rules, namely foldings. We propose a set of foldings that aggregate or eliminate performance information. We further prove the soundness of these foldings in terms of stability preservation and provide bounds on the error that they introduce with respect to the original model. Furthermore, we show how to find an optimal sequence of simplification rules, such that their application yields a minimal model under a given error budget for performance estimation. We evaluate the approach with two real-world datasets from the healthcare and telecommunication domains, showing that model simplification indeed enables a controlled reduction of model size, while preserving performance metrics with respect to the original model. Moreover, we show that aggregation dominates elimination when abstracting performance models by preventing under-fitting due to information loss.
|Number of pages||16|
|State||Published - Nov 2018|
Bibliographical noteFunding Information:
Research partially funded by the German Research Foundation (DFG) under grant agreement number 246594964. The work of Alexander Shleyfman was supported by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities.
Research partially funded by the German Research Foundation ( DFG ) under grant agreement number 246594964 . The work of Alexander Shleyfman was supported by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities .
© 2018 Elsevier Ltd
- Generalised stochastic Petri nets
- Model Simplification
- Process Mining