Andersen, JuliaJuliaAndersenRathje, PatrickPatrickRathjeImenkamp, ChristianChristianImenkampKoschmider, AgnesAgnesKoschmiderLandsiedel, OlafOlafLandsiedel2026-01-122026-01-122025-05-1440th Annual ACM Symposium on Applied Computing, SAC 2025979-8-4007-0629-5https://hdl.handle.net/11420/60717Process mining is moving beyond mining traditional event logs and nowadays includes, for example, data sourced from sensors in the Internet of Things (IoT). The volume and velocity of data generated by such sensors make it increasingly challenging to efficiently process the data by traditional process discovery algorithms, which operate on a centralized event log. This paper presents EdgeMiner, an algorithm for distributed process mining operating directly on sensor nodes on a stream of real-time event data. In contrast to centralized algorithms, EdgeMiner tracks each event and its predecessor and successor events directly on the sensor node where the event is sensed and recorded. As EdgeMiner aggregates direct successions on the individual nodes, the raw data does not need to be stored centrally, thus improving scalability and privacy. We analytically and experimentally show the correctness of EdgeMiner. Our evaluation results show that EdgeMiner determines predecessors for each event efficiently, reducing the communication overhead by up to 96% compared to querying all nodes. Further, we show that the number of queried nodes stabilizes after relatively few events, and batching predecessor queries in groups reduces the average queried nodes per event to less than 2.5%.enhttps://creativecommons.org/licenses/by/4.0/distributed process miningevent data streamin-network processinginternet of thingsscalabilityMLE@TUHHComputer Science, Information and General Works::005: Computer Programming, Programs, Data and Security::005.7: DataComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence::006.33: Knowledge-based SystemsConference Paperhttps://doi.org/10.15480/882.1644110.1145/3672608.370787310.15480/882.1644110.15480/882.15121Conference Paper