Process discovery algorithms aim to capture process models from event logs. These algorithms have been designed for logs in which the events that belong to the same case are related to each other — and to that case — by means of a unique case identifier. However, in service-oriented systems, these case identifiers are rarely stored beyond request-response pairs, which makes it hard to relate events that belong to the same case. This is known as the correlation challenge. This paper addresses the correlation challenge by introducing a technique, called the correlation miner, that facilitates discovery of business process models when events are not associated with a case identifier. It extends previous work on the correlation miner, by not only enabling the discovery of the process model, but also detecting which events belong to the same case. Experiments performed on both synthetic and real-world event logs show the applicability of the correlation miner. The resulting technique enables us to observe a service-oriented system and determine — with high accuracy — which request-response pairs sent by different communicating parties are related to each other.

中文翻译：

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Correlation Miner：在没有案例标识符的情况下挖掘业务流程模型和事件相关性

流程发现算法旨在从事件日志中捕获流程模型。这些算法是为日志设计的，在这些日志中，属于同一案例的事件通过唯一的案例标识符相互关联，并且与该案例相关。然而，在面向服务的系统中，这些案例标识符很少存储在请求-响应对之外，这使得很难关联属于同一案例的事件。这被称为相关性挑战。本文通过引入一种称为关联挖掘器的技术来解决关联挑战，该技术有助于在事件未与案例标识符相关联时发现业务流程模型。它扩展了之前在相关挖掘器上的工作，不仅可以发现流程模型，还可以检测哪些事件属于同一案例。在合成和真实世界事件日志上进行的实验表明了相关挖掘器的适用性。由此产生的技术使我们能够观察面向服务的系统，并以高精度确定不同通信方发送的哪些请求-响应对彼此相关。