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Declarative process modeling formalisms - which capture high-level processconstraints - have seen growing interest, especially for modeling flexibleprocesses. This paper presents DisCoveR, an extremely efficient and accuratedeclarative miner for learning Dynamic Condition Response (DCR) Graphs fromevent logs. We precisely formalize the algorithm, describe a highly efficientbit vector implementation and rigorously evaluate performance against two otherdeclarative miners, representing the state-of-the-art in Declare and DCR Graphsmining. DisCoveR outperforms each of these w.r.t. a binary classification task,achieving an average accuracy of 96.2% in the Process Discovery Contest 2019.Due to its linear time complexity, DisCoveR also achieves run-times 1-2 ordersof magnitude below its declarative counterparts. Finally, we show how the minerhas been integrated in a state-of-the-art declarative process modelingframework as a model recommendation tool, discuss how discovery can play anintegral part of the modeling task and report on how the integration hasimproved the modeling experience of end-users.

DisCoveR: Accurate & Efficient Discovery of Declarative Process Models