Abstract
Several approaches have already been proposed to extract both business processes and business rules from a legacy source code. These approaches usually consider static and dynamic source code analysis for re-learning of these models. However, business processes have components that cannot be directly extracted by static analysis (i.e., process participants and concurrent tasks). Moreover, most of well-known process mining algorithms used in dynamic analysis do not support all required operations of incremental extraction. Re-learning of large legacy systems can benefit from an incremental analysis strategy in order to provide iterative extraction of process models. This paper discusses an approach for business knowledge extraction from legacy systems through incremental process mining. Discovery results can be used in various ways by business analysts and software architects, e.g. documentation of legacy systems or for re-engineering purposes.
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Kalsing, A.C., Iochpe, C., Thom, L.H., do Nascimento, G.S. (2014). Re-learning of Business Process Models from Legacy System Using Incremental Process Mining. In: Hammoudi, S., Cordeiro, J., Maciaszek, L., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2013. Lecture Notes in Business Information Processing, vol 190. Springer, Cham. https://doi.org/10.1007/978-3-319-09492-2_19
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