Process mining discovers and analyzes a process model from historical event logs. The prior art methods use the key attributes of case-id, activity, and timestamp hidden in an event log as clues to discover a process model. However, a user needs to specify them manually, and this can be an exhaustive task. In this paper, we propose a two-stage key attribute identification method to avoid such a manual investigation, and thus this is a step toward fully automated process discovery. One of the challenging tasks is how to avoid exhaustive computation due to combinatorial explosion. For this, we narrow down candidates for each key attribute by using supervised machine learning in the first stage and identify the best combination of the key attributes by discovering process models and evaluating them in the second stage. Our computational complexity can be reduced from $\mathcal{O}(N^3)$ to $\mathcal{O}(k^3)$ where $N$ and $k$ are the numbers of columns and candidates we keep in the first stage, respectively, and usually $k$ is much smaller than $N$. We evaluated our method with 14 open datasets and showed that our method could identify the key attributes even with $k = 2$ for about 20 seconds for many datasets.

翻译：流程挖掘从历史事件日志中发掘并分析流程模型。现有技术方法以隐藏于事件日志中的案例ID、活动和时间戳等关键属性为线索来发现流程模型。然而，用户需要手动指定这些属性，这可能是项繁重的工作。本文提出一种两阶段关键属性识别方法以避免此类人工探究，从而向全自动流程发现迈进一步。其中一个挑战性问题是如何避免组合爆炸导致的穷举计算。为此，我们在第一阶段通过有监督机器学习缩小各关键属性的候选范围，并在第二阶段通过发现流程模型并对其进行评估来识别关键属性的最佳组合。我们的计算复杂度可从$\mathcal{O}(N^3)$降至$\mathcal{O}(k^3)$，其中$N$和$k$分别代表列数及第一阶段保留的候选数，且通常$k$远小于$N$。我们使用14个开放数据集评估该方法，结果表明，即使当$k=2$时，对多数数据集仍能在约20秒内识别出关键属性。