  Process mining is of great importance for both data-centric and
process-centric systems. Process mining receives so-called process logs which
are collections of partially-ordered events. An event has to possess at least
three attributes, case ID, task ID and a timestamp for mining approaches to
work. When a case ID is unknown, the event is called unlabeled. Traditionally,
process mining is an offline task, where events are collected from different
sources are usually manually correlated. That is, events belonging to the same
instance are assigned the same case ID. With today's high-volume/high-speed
nature of, e.g., IoT applications, process mining shifts to be an online task.
For this, event correlation has to be automated and has to occur as the data is
generated. In this paper, we introduce an approach that correlates unlabeled
events at runtime. Given a process model, a stream of unlabeled events and
other information about task duration, our approach can induce a case
identifier to a set of unlabeled events with a trust percentage. It can also
check the conformance of the identified cases with the process model. A
prototype of the proposed approach was implemented and evaluated against
real-life and synthetic logs.
