The Alpha algorithm was the first process discovery algorithm that was able to discover process models with concurrency based on incomplete event data while still providing formal guarantees. However, as was stated in the original paper, practical applicability is limited when dealing with exceptional behavior and processes that cannot be described as a structured workflow net without short loops. This paper presents the Alpha+++ algorithm that overcomes many of these limitations, making the algorithm competitive with more recent process mining approaches. The different steps provide insights into the practical challenges of learning process models with concurrency, choices, sequences, loops, and skipping from event data. The approach was implemented in ProM and tested on various publicly available, real-life event logs.

翻译：Alpha算法是首个能够基于不完整事件数据发现包含并发性的流程模型，同时仍提供形式保证的流程发现算法。然而，如原始论文所述，在处理异常行为和无法描述为无短环结构化工作流网的流程时，其实际适用性有限。本文提出了Alpha+++算法，克服了这些局限中的大部分，使其与更现代的流程挖掘方法具有竞争力。各步骤揭示了从事件数据中学习包含并发性、选择、顺序、循环和跳过的流程模型所面临的实际挑战。该方法已在ProM中实现，并在多个公开可用的真实事件日志上进行了测试。