提出可直接处理部分有序输入的流程发现算法，提升真实场景建模准确性。
eST$^2$ Miner -- Process Discovery Based on Firing Partial Orders
- 融合eST Miner与Firing LPO算法，支持部分顺序事件输入
- 具备强形式化保证，运行高效且内存占用极低
- 适合需要高精度建模并发、重叠等复杂行为的工业应用
流程发现从事件日志中生成流程模型。传统上，事件日志被定义为多集迹（traces），每条迹是事件序列，其顺序基于时间发生。然而真实流程本质上是部分有序的：不同活动可在流程不同部分独立发生，因此时间顺序未必反映因果关系，无关事件也可能按时间排序。只有部分序（partial order）才能表达并发、持续、重叠和不确定性。因此，亟需能直接处理部分有序输入的流程挖掘算法。本文结合流程挖掘领域的eST Miner与彼得网领域的Firing LPO算法，提出eST$^2$ Miner。该算法可直接处理部分有序输入，具有强形式化保证，运行效率高，空间复杂度优异，适用于真实场景应用。
原文摘要 · Abstract (English)
Process discovery generates process models from event logs. Traditionally, an event log is defined as a multiset of traces, where each trace is a sequence of events. The total order of the events in a sequential trace is typically based on their temporal occurrence. However, real-life processes are partially ordered by nature. Different activities can occur in different parts of the process and, thus, independently of each other. Therefore, the temporal total order of events does not necessarily reflect their causal order, as also causally unrelated events may be ordered in time. Only partial orders allow to express concurrency, duration, overlap, and uncertainty of events. Consequently, there is a growing need for process mining algorithms that can directly handle partially ordered input. In this paper, we combine two well-established and efficient algorithms, the eST Miner from the process mining community and the Firing LPO algorithm from the Petri net community, to introduce the eST$^2$ Miner. The eST$^2$ Miner is a process discovery algorithm that can directly handle partially ordered input, gives strong formal guarantees, offers good runtime and excellent space complexity, and can, thus, be used in real-life applications.
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