Efficient Declarative-based Process Mining using an Enhanced Framework

Process Mining and Automated Process Management have become a hot research topic in recent years. While most state-of-the-art approaches were developed for very specific and constrained application domains, where also the available data were limited, the WoMan framework is also able to deal with non-standard application domains (such as human routinary activities), characterized by much more variability and by the availability of much more data. Hence, our motivation for studying the scalability of WoMan and for improving it. In this paper we propose two approaches, aimed at improving both its efficiency in learning process models and readability of the learned process models. Experiments show that these approaches significantly extend the applicability of WoMan as long as more and more data are to be processed