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.

中文翻译：

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发现：准确有效地发现声明性过程模型

声明式流程建模形式——捕获高级流程约束——已经引起越来越多的兴趣，尤其是对于灵活流程建模。本文介绍了 DisCoveR，这是一种极其高效且准确的声明式挖掘器，用于从事件日志中学习动态条件响应 (DCR) 图。我们精确地形式化了算法，描述了一种高效的位向量实现，并针对其他两个声明式矿工严格评估了性能，代表了 Declare 和 DCR Graphs 挖掘的最新技术。在二进制分类任务中，DisCoveR 的表现优于其中每一项，在 2019 年流程发现竞赛中实现了 96.2% 的平均准确率。 由于其线性时间复杂度，DisCoveR 的运行时间也比其声明性对应项低 1-2 个数量级。最后，