  Declarative process modeling formalisms - which capture high-level process
constraints - have seen growing interest, especially for modeling flexible
processes. This paper presents DisCoveR, an extremely efficient and accurate
declarative miner for learning Dynamic Condition Response (DCR) Graphs from
event logs. We precisely formalize the algorithm, describe a highly efficient
bit vector implementation and rigorously evaluate performance against two other
declarative miners, representing the state-of-the-art in Declare and DCR Graphs
mining. 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 orders
of magnitude below its declarative counterparts. Finally, we show how the miner
has been integrated in a state-of-the-art declarative process modeling
framework as a model recommendation tool, discuss how discovery can play an
integral part of the modeling task and report on how the integration has
improved the modeling experience of end-users.
