Journal article

Automated Discovery of Process Models from Event Logs: Review and Benchmark

A Augusto, R Conforti, M Dumas, M La Rosa, FM Maggi, A Marrella, M Mecella, A Soo

IEEE Transactions on Knowledge and Data Engineering | Institute of Electrical and Electronics Engineers | Published : 2019


Process mining allows analysts to exploit logs of historical executions of business processes to extract insights regarding the actual performance of these processes. One of the most widely studied process mining operations is automated process discovery. An automated process discovery method takes as input an event log, and produces as output a business process model that captures the control-flow relations between tasks that are observed in or implied by the event log. Various automated process discovery methods have been proposed in the past two decades, striking different tradeoffs between scalability, accuracy, and complexity of the resulting models. However, these methods have been eva..

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Awarded by Australian Research Council

Funding Acknowledgements

This research is partly funded by the Australian Research Council (grant DP150103356) and the Estonian Research Council (grant IUT20-55). It is also partly supported by the H2020-RISE EU project FIRST (734599), the Sapienza grant DAKIP and the Italian projects Social Museum and Smart Tourism (CTN01_00034_23154), NEPTIS (PON03PE_00214_3), and RoMA - Resilience of Metropolitan Areas (SCN_00064).