Automated process discovery techniques enable users to generate business process models from event logs extracted from enterprise information systems. Traditional techniques in this field generate procedural process models (e.g., in the BPMN notation). When dealing with highly variable processes, the resulting procedural models are often too complex to be practically usable. An alternative approach is to discover declarative process models, which represent the behavior of the process as a set of constraints. Declarative process discovery techniques have been shown to produce simpler models than procedural ones, particularly for processes with high variability. However, the bulk of approaches for automated discovery of declarative process models focus on the control-flow perspective, ignoring the data perspective. This paper addresses the problem of discovering declarative process models with data conditions. Specifically, the paper tackles the problem of discovering constraints that involve two activities of the process such that each of these two activities is associated with a condition that must hold when the activity occurs. The paper presents and compares two approaches to the problem of discovering such conditions. The first approach uses clustering techniques in conjunction with a rule mining technique, while the second approach relies on redescription mining techniques. The two approaches (and their variants) are empirically compared using a combination of synthetic and real-life event logs. The experimental results show that the former approach outperforms the latter when it comes to re-discovering constraints artificially injected in a log. Also, the former approach is in most of the cases more computationally efficient. On the other hand, redescription mining discovers rules with higher confidence (and lower support) suggesting that it may be used to discover constraints that hold for smaller subsets of cases of a process.

自动化的流程发现技术使用户能够从从企业信息系统中提取的事件日志中生成业务流程模型。该领域的传统技术生成过程过程模型（例如，以BPMN表示）。当处理高度可变的过程时，生成的过程模型通常太复杂而无法实际使用。另一种方法是发现声明性过程模型，该模型将过程的行为表示为一组约束。声明性过程发现技术已显示出比过程模型更简单的模型，特别是对于具有高可变性的过程。但是，用于自动发现声明性过程模型的大部分方法都集中在控制流方面，而忽略了数据方面。本文解决了发现具有数据条件的声明性过程模型的问题。具体而言，本文解决了发现涉及流程两个活动的约束的问题，从而使这两个活动中的每一个都与活动发生时必须满足的条件相关联。本文介绍并比较了两种解决此类情况的方法。第一种方法将聚类技术与规则挖掘技术结合使用，而第二种方法则依赖于重新定义挖掘技术。使用综合事件日志和实际事件日志对两种方法（及其变体）进行了经验比较。实验结果表明，在重新发现人为注入日志中的约束时，前一种方法优于后者。也，在大多数情况下，前一种方法的计算效率更高。另一方面，重新定义挖掘发现具有较高置信度（和较低支持）的规则，这表明它可用于发现适用于较小情况子集的约束。