Cabanillas Macias, Cristina, Sturm, Christian, Mendling, Jan, Schönig, Stefan. 2018. Mining Expressive and Executable Resource-Aware Imperative Process Models. In BPMDS/EMMSAD@CAiSE 2018, Hrsg. Jens Gulden and Iris Reinhartz{-}Berger and Rainer Schmidt and S{\'{e}}rgio Guerreiro and Wided Gu{\'{e}}dria and Palash Bera, 3-18. Tallinn: Springer.

Abstract

Process mining extracts relevant information on executed business processes from historical data stored in event logs. The data typically available include the activities executed, temporal information and the resources in charge of their execution. With such data, the functional, behavioural and organisational perspectives of a process can be discovered. Many existing process mining approaches are capable of generating representations involving the first two perspectives with all types of processes. The extraction of simple and complex resource assignment rules has also been tackled with declarative process models. However, it is noticeable that despite imperative notations like BPMN are mostly used for process modelling nowadays, the existing process mining approaches for enriching such models with resource assignments cannot discover rules like separation of duties and do not produce executable resource-aware process models. In this paper we present an approach for mining resource-aware imperative process models that uses an expressive resource assignment language (RALph) with the de-facto standard notation BPMN. The organisational perspective of the resulting models can be automatically analysed thanks to the formal semantics of RALph. The method has been implemented and tested with a real use case.