The plethora of algorithms in the research field of process mining builds on directly-follows relations. Even though various improvements have been made in the last decade, there are serious weaknesses of these relationships. Once events associated with different objects that relate with a cardinality of 1:N and N:M to each other, techniques based on directly-follows relations produce spurious relations, self-loops, and back-jumps. This is due to the fact that event sequence as described in classical event logs differs from event causation. In this paper, we address the research problem of representing the causal structure of process-related event data. To this end, we develop a new approach called Causal Process Mining. This approach renounces the use of flat event logs and considers relational databases of event data as an input. More specifically, we transform the relational data structures based on the Causal Process Template into what we call Causal Event Graph. We evaluate our approach and compare its outputs with techniques based on directly-follows relations in a case study with an European food production company. Our results demonstrate that directly-follows miners produce a large number of spurious relationships, which our approach captures correctly.
翻译:工序采矿研究领域繁多的算法建立在直接随波逐流的关系之上。 尽管在过去十年中取得了各种改进, 但这些关系也存在严重的弱点。 一旦与与1:N和N:M这两个基本点相关的不同物体相关的事件发生后, 直接随波逐流的关系产生的技术产生假关系、 自我滑动和回跳。 这是因为古典事件日志中描述的事件序列与事件因果关系不同。 在本文中, 我们处理的是代表过程相关事件数据因果结构的研究问题。 为此, 我们开发了一种名为 Causal Process Mining 的新方法。 这种方法放弃使用平板事件日志, 并且考虑将事件数据的关系数据库作为一种投入。 更具体地说, 我们把基于Causal 进程模板的关联数据结构转换为我们称之为Causal 事件图。 我们评估了我们的方法, 并将它的产出与一个欧洲粮食生产公司在案例研究中直接跟踪关系的技术进行比较。 我们的结果表明, 直接跟踪矿工产生了大量令人怀疑的关系, 我们的方法是正确的。