Process pattern discovery methods (PPDMs) aim at identifying patterns of interest to users. Existing PPDMs typically are unsupervised and focus on a single dimension of interest, such as discovering frequent patterns. We present an interactive multi interest driven framework for process pattern discovery aimed at identifying patterns that are optimal according to a multi-dimensional analysis goal. The proposed approach is iterative and interactive, thus taking experts knowledge into account during the discovery process. The paper focuses on a concrete analysis goal, i.e., deriving process patterns that affect the process outcome. We evaluate the approach on real world event logs in both interactive and fully automated settings. The approach extracted meaningful patterns validated by expert knowledge in the interactive setting. Patterns extracted in the automated settings consistently led to prediction performance comparable to or better than patterns derived considering single interest dimensions without requiring user defined thresholds.

翻译：过程模式发现方法旨在识别用户感兴趣的模式。现有方法通常是非监督式的，并且仅关注单一兴趣维度（如发现频繁模式）。本文提出了一种交互式多兴趣驱动的过程模式发现框架，旨在根据多维分析目标识别最优模式。所提方法具有迭代性和交互性，因此在发现过程中能够纳入专家知识。本文聚焦于一个具体的分析目标，即推导影响过程结果的过程模式。我们在交互式和全自动两种设置下，基于真实世界事件日志对该方法进行了评估。在交互式设置中，该方法提取了经专家知识验证的有意义的模式。在全自动设置中提取的模式，其预测性能始终与考虑单一兴趣维度提取的模式相当或更优，且无需用户定义阈值。