Process mining provides methods to analyse event logs generated by information systems during the execution of processes. It thereby supports the design, validation, and execution of processes in domains ranging from healthcare, through manufacturing, to e-commerce. To explore the regularities of flexible processes that show a large behavioral variability, it was suggested to mine recurrent behavioral patterns that jointly describe the underlying process. Existing approaches to behavioral pattern mining, however, suffer from two limitations. First, they show limited scalability as incremental computation is incorporated only in the generation of pattern candidates, but not in the evaluation of their quality. Second, process analysis based on mined patterns shows limited effectiveness due to an overwhelmingly large number of patterns obtained in practical application scenarios, many of which are redundant. In this paper, we address these limitations to facilitate the analysis of complex, flexible processes based on behavioral patterns. Specifically, we improve COBPAM, our initial behavioral pattern mining algorithm, by an incremental procedure to evaluate the quality of pattern candidates, optimizing thereby its efficiency. Targeting a more effective use of the resulting patterns, we further propose pruning strategies for redundant patterns and show how relations between the remaining patterns are extracted and visualized to provide process insights. Our experiments with diverse real-world datasets indicate a considerable reduction of the runtime needed for pattern mining, while a qualitative assessment highlights how relations between patterns guide the analysis of the underlying process.

翻译：过程挖掘提供了分析信息系统在流程执行过程中生成的事件日志的方法，从而支持从医疗、制造到电子商务等领域中流程的设计、验证与执行。为探索具有高度行为变异性的柔性流程中的规律性，研究者提出挖掘能共同描述底层流程的循环行为模式。然而，现有行为模式挖掘方法存在两个局限：其一，可扩展性有限，增量计算仅用于模式候选的生成，而未应用于其质量评估；其二，基于挖掘模式的过程分析效率受限，因为实际应用场景中获取的模式数量极其庞大，且其中大量存在冗余。本文针对这些局限，旨在促进基于行为模式的复杂柔性流程分析。具体而言，我们通过增量式程序评估模式候选的质量，优化了初始行为模式挖掘算法COBPAM的效率。为进一步提高所得模式的使用效果，我们提出了冗余模式的剪枝策略，并展示了如何提取并可视化剩余模式间的关系以提供流程洞察。基于多个真实数据集的实验表明，模式挖掘所需的运行时间显著降低；同时，定性评估突显了模式间关系如何指导对底层流程的分析。