Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important steps in process mining. Many discovery techniques have been proposed, but none of them combines scalability with strong quality guarantees. We would like such techniques to handle billions of events or thousands of activities, to produce sound models (without deadlocks and other anomalies), and to guarantee that the underlying process can be rediscovered when sufficient information is available. In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framework. To measure the quality of discovered models for such large logs, we introduce a model–model and model–log comparison framework that applies a divide-and-conquer strategy to measure recall, fitness, and precision. We experimentally show that these discovery and measuring techniques sacrifice little compared to other algorithms, while gaining the ability to cope with event logs of 100,000,000 traces and processes of 10,000 activities on a standard computer.

中文翻译：

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可扩展的过程发现和一致性检查。

如今，组织已收集并存储了大量数据，包括流程事件。从此类事件数据中发现过程模型并验证发现的模型的质量是过程挖掘中的重要步骤。已经提出了许多发现技术，但是它们都没有将可伸缩性与强大的质量保证相结合。我们希望这样的技术能够处理数十亿个事件或数千个活动，生成声音模型（没有死锁和其他异常），并保证在有足够的信息可用时可以重新发现基础过程。在本文中，我们介绍了一个用于过程发现的框架，该框架可确保这些属性同时仅传递一次日志，并介绍使用该框架的三种算法。为了衡量此类大型日志的发现模型的质量，我们引入了模型-模型和模型-日志比较框架，该框架应用分而治之策略来衡量召回率，适用性和准确性。我们通过实验证明，与其他算法相比，这些发现和测量技术所付出的代价很少，同时具有在标准计算机上应对100,000,000条痕迹的事件日志和10,000条活动的过程的能力。