  The IoT and Business Process Management (BPM) communities co-exist in many
shared application domains, such as manufacturing and healthcare. The IoT
community has a strong focus on hardware, connectivity and data; the BPM
community focuses mainly on finding, controlling, and enhancing the structured
interactions among the IoT devices in processes. While the field of Process
Mining deals with the extraction of process models and process analytics from
process event logs, the data produced by IoT sensors often is at a lower
granularity than these process-level events. The fundamental questions about
extracting and abstracting process-related data from streams of IoT sensor
values are: (1) Which sensor values can be clustered together as part of
process events?, (2) Which sensor values signify the start and end of such
events?, (3) Which sensor values are related but not essential? This work
proposes a framework to semi-automatically perform a set of structured steps to
convert low-level IoT sensor data into higher-level process events that are
suitable for process mining. The framework is meant to provide a generic
sequence of abstract steps to guide the event extraction, abstraction, and
correlation, with variation points for plugging in specific analysis techniques
and algorithms for each step. To assess the completeness of the framework, we
present a set of challenges, how they can be tackled through the framework, and
an example on how to instantiate the framework in a real-world demonstration
from the field of smart manufacturing. Based on this framework, future research
can be conducted in a structured manner through refining and improving
individual steps.
