2025/04/11 by Sabine Folz-Weinstein, Christian Rennert, Folz-Weinstein, Sabine +7 · 1 citation
Computer Science · #Databases (cs.DB) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Rough Sets and Fuzzy Logic #Semantic Web and Ontologies #Service-Oriented Architecture and Web Services
paper · pdf · doi:10.48550/arxiv.2504.08372
openalex publication_date 2025/04/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 eST2 Miner. The eST2 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.