  Process 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 makes it
increasingly challenging for traditional process discovery algorithms to store
and mine such data in traditional event logs. Further, privacy considerations
often prevent data collection at a central location in the first place. To
address this challenge, this paper introduces EdgeAlpha, a distributed
algorithm for process discovery operating directly on sensor nodes and edge
devices on a stream of real-time event data. Based on the Alpha Miner,
EdgeAlpha tracks each event and its predecessor and successor events directly
on the sensor node where the event is sensed and recorded. From this local
view, each node in EdgeAlpha derives a partial footprint matrix, which we then
merge at a central location, whenever we query the system to compute a process
model. EdgeAlpha enables (a) scalable mining, as a node, for each event, only
interacts with its predecessors and, when queried, only exchanges aggregates,
i.e., partial footprint matrices, with the central location and (b) privacy
preserving process mining, as nodes only store their own as well as predecessor
and successor events. On the Sepsis Cases event log, for example, a node
queries on average 18.7% of all nodes. For the Hospital Log, we can even reduce
the overall querying to 3.87% of the nodes.
